Anatomy of Knowledge Cleaning Ontology (AKCO)
language en

Anatomy of Knowledge Cleaning Ontology (AKCO)

Latest version:
https://purl.archive.org/akco
Revision:
1.0
Issued on:
28.05.2025
Authors:
Dennis Sommer
Download serialization:
JSON-LD RDF/XML N-Triples TTL
License:
https://creativecommons.org/licenses/by/4.0/
Visualization:
Visualize with WebVowl
Cite as:
Dennis Sommer. Anatomy of Knowledge Cleaning Ontology (AKCO), 2025, https://purl.archive.org/akco
Ontology Specification Draft

Abstract

The Anatomy of Knowledge Cleaning Ontology defines relevant aspects of knowledge cleaning. It is based on a vast review of approaches, whose results compose the anatomy of knowledge cleaning. This anatomy identifies relevant aspects, like background knowledge, dimensions, or cleaning techniques, and relates these concepts. The AKCO is an ontology that represents these aspects. It adopts existing vocabularies, and aims to support the analyzation of correlations in knowledge cleaning. the contains definitions and instantiations of these defintions.

Introduction back to ToC

The Anatomy of Knowledge Cleaning Ontology (AKCO) defines relevant aspects of knowledge cleaning. Knowledge cleaning, i.e., error detection and correction, is an essential part of the life cycle of a knowledge graph, aiming to improve its correctness. Thereby, knowledge graphs contain different types of errors, associated to the type of an assertion, and the nature of the error. Assertions typically make up most of a knowledge graph, representing factual knowledge. Most knowledge graphs use three different types of assertions, instance, property-value, and equality assertions. These assertions can be afflicted by either a semantic or syntactic error, which is the error nature. The assertions, if wrong are the error sources, and depending on the error nature, can have different error types. Each assertion and error type has distinct characteristics, requiring a tailored cleaning approach. Therefore, several approaches exist for targeting specific types of errors in different error sources. There are typically several possibilities to target the same error. However, these possibilities differ in the cleaning technique they use to process an error. A cleaning technique can be described as a certain methodology that is followed. All approaches that adopt the same technique use similar insights, and background knowledge. Background knowledge refers to the context that is to compare an assertion to or identify an correction. The Anatomy of Knowledge cleaning is designed to represent important concepts and relate those. It adopts concepts form, skos, dc terms, and dqv. It aims to represent the relevant aspects of knowledge cleaning and provide a source for understanding, comparing, and selecting cleaning approaches. It not only provides definitions but also over one hundred named individuals that are based on the results of our literature review [cite]. It

The Anatomy of knowledge cleaning

This part introduces the anatomy of knowledge cleaning, outlining and connecting key aspects relevant to cleaning knowledge graphs. It addresses the first research question (see Section 1.1).

These aspects include cleaning techniques, which define the specific strategies employed by different approaches. The techniques depend on various forms of background knowledge for effective processing, and this section introduces the different types involved.

In addition, four sets of dimensions are discussed—each set characterizes particular properties of cleaning approaches and techniques.

Dimensions

This section introduces various dimensions, i.e., characteristics, of knowledge graph cleaning approaches. Each dimension has one or more variations, and individual approaches are not limited to a single variation.

For instance, hybrid approaches often combine different techniques and dimensions to achieve a more robust and adaptable cleaning workflow. Such approaches may, for example, incorporate both internal and external methods.

Select a section from the menu.

Background knowledge

Error detection and correction rely on background knowledge, which provides essential context for identifying or resolving errors. This knowledge can come from different parts or features of a knowledge graph itself (i.e., internal background knowledge) or from external supplementary sources (i.e., external background knowledge).

We distinguish seven types of background knowledge:

Select a section from the menu.

Cleaning techniques

Approaches use different cleaning techniques to detect and correct errors. Each technique is characterized by a methodology focusing on specific insights and requirements. It leverages background knowledge to address the cleaning task. Each technique and related approaches are described, using an example knowledge graph, figures, and tables to visualize key insights.

We distinguish between eleven cleaning techniques:

Anatomy of Knowledge Cleaning Ontology (AKCO): Overview back to ToC

Namespace declarations

Table 1: Namespaces used in the document
akco<https://purl.archive.org/akco>
cube<http://purl.org/linked-data/cube#>
dc<http://purl.org/dc/elements/1.1/>
dqv<http://www.w3.org/ns/dqv#>
owl<http://www.w3.org/2002/07/owl#>
rdf<http://www.w3.org/1999/02/22-rdf-syntax-ns#>
rdfs<http://www.w3.org/2000/01/rdf-schema#>
skos<http://www.w3.org/2004/02/skos/core#>
terms<http://purl.org/dc/terms/>
vann<http://purl.org/vocab/vann/>
xml<http://www.w3.org/XML/1998/namespace>
xsd<http://www.w3.org/2001/XMLSchema#>
This ontology has the following classes and properties.

Visualization

Visualization of the ontology, following the notation of the Chowlk Visual Notation [Chávez-Feria, et al., 2021].

The classes and properties that are from external ontologies, are highlighted with different colors.

Anatomy of Knowledge Cleaning Ontology (AKCO): Description back to ToC

The anatomy of knowledge cleaning ontology represents important features of knowledge cleaning. These features include knowledge cleaning approaches, techniques and backgroundknowledge. It aims to provide the context from which knowledge cleaning can be performed. The ontology proviedes means to select approaches for a custom cleaning process, depending on for example, the knowledge graph and available additional cleaning sources. After the selection the approaches need to be applied, this application is represented by the Application algorithm and logic classes.

AKCO application

This section describes how the defined concepts and relations are used to (1) represent relevant aspects, and to (2) answer relevant questions to knowledge cleaning.

Representing relevant aspects of knowledge cleaning

Select an aspect to represent, using the AKCO

Answering important questions

Select an example from the left.

Cross-reference for Anatomy of Knowledge Cleaning Ontology (AKCO) classes, object properties and data properties back to ToC

This section provides details for each class and property defined by Anatomy of Knowledge Cleaning Ontology (AKCO).

Classes

Approachc back to ToC or Class ToC

IRI: https://purl.archive.org/akco#Approach

An approach is a cleaning solution that targets a specific error type and source, performing error detection and/or correction. Each approach employs a particular technique and leverages background knowledge.
has super-classes
Concept c
is in domain of
hasDimension op, isPublished op, targetsError op, usesTechnique op
has members
appADianReFrfoDLKB ni, appAPE ni, appAPrInToImLiDaQuUsDiOuDe ni, appAttributE ni, appBioGRER ni, appCEDAL ni, appCOLIBRI ni, appCOPAAL ni, appChanHaInofDB ni, appCoCKG ni, appCorhist ni, appDBOnEnfoInDe ni, appDECIDE ni, appDeFacto ni, appDeInNuDainDB ni, appDeanCoTyErinOpKnGrUsSeReofEn ni, appDecefitogr ni, appDeerinnulidauscroude ni, appDojuanenbyitnaEntyuslamo ni, appExfakt ni, appFACTY ni, appFEA ni, appFaVawiKnGrEm ni, appFacTify ni, appFachvievpa ni, appFactCheck ni, appGAT ni, appGRR ni, appGeneralCorrectionFramework ni, appGltyfoliinrdtrwiaptoensu ni, appHMGCN ni, appHybridFC ni, appIDLabTurtleValidator ni, appIO ni, appIOTW ni, appIdwrlibedabymuoude ni, appIncoinOW ni, appJSAE ni, appKD2R ni, appKGClean ni, appKGTtm ni, appKS ni, appKV-rule ni, appKefogr ni, appLOD-Community-Detection ni, appLODLaundromat ni, appLeRiXt ni, appLediaxwiasrumianitaptoindeoflida ni, appLodeofinSastinRDda ni, appMTM ni, appNotquitethesame ni, appOGFC ni, appOptimalABoxRepairw.r.t.StaticELTBoxes ni, appPaTyBRED ni, appPredPath ni, appPrerdemofokngrre ni, appRDD-Checker ni, appRDFDoctor ni, appRDFUnit ni, appRDvase ni, appRUGE ni, appReRaViinDB ni, appS3K ni, appSDType ni, appSDValidate ni, appSLCN ni, appServDBpediaDOLCE ni, appSiApCofoRDMo ni, appTISCO ni, appToDeSePrfoRDusSPasInLa ni, appTripleNet ni, appTyPrfoEfCoReinHeSeGr ni, appTypingErrorsinFactualKnowledgeGraphs ni, appUsReofInKnBa ni, appVGFD ni, appVRP ni, appValidata ni, appValidatrr ni, appVeriGraph ni, appWhEvlidahewiaquthclupDB ni, appWhenowl ni, apprdfvalidator ni
is disjoint with
Technique c

BackgroundKnowledgec back to ToC or Class ToC

IRI: https://purl.archive.org/akco#BackgroundKnowledge

Background knowledge provides the necessary context for a cleaning technique to detect or correct errors.
has sub-classes
Data Set c, ExpertKnowledge c
is in domain of
has quality measurement op
is in range of
usesBackgroundKnowledge op

Conceptc back to ToC or Class ToC

IRI: http://www.w3.org/2004/02/skos/core#Concept

An idea or notion; a unit of thought.
Is defined by
http://www.w3.org/2004/02/skos/core
has sub-classes
Approach c, Error c, ExternalSource c, Technique c

Data Setc back to ToC or Class ToC

IRI: http://purl.org/linked-data/cube#DataSet

has super-classes
BackgroundKnowledge c
has sub-classes
ExternalSource c, KnowledgeGraph c

Dimensionc back to ToC or Class ToC

IRI: https://purl.archive.org/akco#Dimension

A dimension is a specific characteristic of a cleaning approach or technique. All approaches or techniques that share a dimension have a specific feature in common. For example, internal approaches only use a given knowledge graph as background knowledge, while external approaches consult other (knowledge) sources.
is in range of
hasDimension op
has members
dimCorrection ni, dimData-driven ni, dimDetection ni, dimExternal ni, dimInternal ni, dimKnowledge-driven ni

Errorc back to ToC or Class ToC

IRI: https://purl.archive.org/akco#Error

An error is an incorrect assertion. The error source, error nature, and error type further define it. The source identifies the type of assertion that is incorrect. The nature identifies if the error is semantic or syntactic. The combination of the error nature inside the error source defines the error type.
has super-classes
Concept c
is in domain of
errorNature dp, errorSource dp, errorType dp
is in range of
hasError op, targetsError op
has members
errESemSemWrong ni, errESynNotPIdentID ni, errESynNotPIdentIR ni, errISemSemWrong ni, errISemSynNotEType ni, errISynNotPIdent ni, errPVSemNotADomain ni, errPVSemNotARange ni, errPVSemSemWrong ni, errPVSemSynNotEProperty ni, errPVSynNotPIdentID ni, errPVSynNotPIdentIR ni, errSystematic ni, errTailVertical ni

ExpertKnowledgec back to ToC or Class ToC

IRI: https://purl.archive.org/akco#ExpertKnowledge

Expert knowledge is provided by human expertise; It includes (domain) expertise, expertise about the cleaning process in general, and involved (semantic) technologies. Different types of expertise may be required to apply specific approaches and techniques successfully.
has super-classes
BackgroundKnowledge c
has members
extSrcDomaInExpertise ni

ExternalSourcec back to ToC or Class ToC

IRI: https://purl.archive.org/akco#ExternalSource

An external source contains domain-relevant information that is not part of the given knowledge graph.
has super-classes
Concept c, Data Set c
has members
extSrcAny ni, extSrcDBpedia ni, extSrcFormalAndSyntaxSpecifications ni, extSrcHornClauses ni, extSrcKnowledgeGraph ni, extSrcQueryLogs ni, extSrcTextCorpus ni, extSrcWeb ni, extSrcWikipedia ni

KnowledgeGraphc back to ToC or Class ToC

IRI: https://purl.archive.org/akco#KnowledgeGraph

A knowledge graph is a data structure that can represent any domain or subject area. It structures domain knowledge in a machine-processable manner. It is a semantic network, also known as a knowledge base.
has super-classes
Data Set c
is in domain of
hasError op
has members
kGContextualGraph ni, kGGraphStructure ni, kGValuesOfPV ni

Quality Measurementc back to ToC or Class ToC

IRI: http://www.w3.org/ns/dqv#QualityMeasurement

Represents the evaluation of a given dataset (or dataset distribution) against a specific quality metric.
is in range of
has quality measurement op

Techniquec back to ToC or Class ToC

IRI: https://purl.archive.org/akco#Technique

A cleaning technique describes a specific methodology used for detecting or correcting errors. It uses different types of background knowledge as a context. The techniques differ in their background knowledge and how each technique utilizes it.
has super-classes
Concept c
is in domain of
usesBackgroundKnowledge op
is in range of
usesTechnique op
has members
tecCrowdsourcing-based ni, tecEmbeddingAndNeuralNetwork-based ni, tecHybrid ni, tecIntegrityConstraint-based ni, tecOntology-based ni, tecPath-based ni, tecRuleMining-based ni, tecStatistical ni, tecSyntactic ni, tecVerbalization-based ni
is disjoint with
Approach c

Object Properties

has broaderop back to ToC or Object Property ToC

IRI: http://www.w3.org/2004/02/skos/core#broader

Broader concepts are typically rendered as parents in a concept hierarchy (tree).
Is defined by
http://www.w3.org/2004/02/skos/core

has narrowerop back to ToC or Object Property ToC

IRI: http://www.w3.org/2004/02/skos/core#narrower

Narrower concepts are typically rendered as children in a concept hierarchy (tree).
Is defined by
http://www.w3.org/2004/02/skos/core

has quality measurementop back to ToC or Object Property ToC

IRI: http://www.w3.org/ns/dqv#hasQualityMeasurement

Refers to the performed quality measurements. Quality measurements can be performed to any kind of resource (e.g., a dataset, a linkset, a graph, a set of triples). However, in the DQV context, this property is generally expected to be used in statements in which subjects are instances of dcat:Dataset or dcat:Distribution.
has domain
BackgroundKnowledge c
has range
Quality Measurement c

has relatedop back to ToC or Object Property ToC

IRI: http://www.w3.org/2004/02/skos/core#related

Relates a concept to a concept with which there is an associative semantic relationship.
Is defined by
http://www.w3.org/2004/02/skos/core

hasDimensionop back to ToC or Object Property ToC

IRI: https://purl.archive.org/akco#hasDimension

Approaches have different dimensions, i.e., characteristics. For example, if an approach is data- or knowledge-driven.
has domain
Approach c
has range
Dimension c

hasErrorop back to ToC or Object Property ToC

IRI: https://purl.archive.org/akco#hasError

A knowledge graph can have certain errors.
has domain
KnowledgeGraph c
has range
Error c

isPublishedop back to ToC or Object Property ToC

IRI: https://purl.archive.org/akco#isPublished

An approach is published inside a publication.
has domain
Approach c
has range
Bibliographic Resource c

targetsErrorop back to ToC or Object Property ToC

IRI: https://purl.archive.org/akco#targetsError

A cleaning approach targets a specific type of error.
has domain
Approach c
has range
Error c

usesBackgroundKnowledgeop back to ToC or Object Property ToC

IRI: https://purl.archive.org/akco#usesBackgroundKnowledge

A cleaning technique leverages a specific type of background knowledge as context.
has domain
Technique c
has range
BackgroundKnowledge c

usesTechniqueop back to ToC or Object Property ToC

IRI: https://purl.archive.org/akco#usesTechnique

An approaches implements specific cleaning techniques. This property defines those used/implemented techniques.
has domain
Approach c
has range
Technique c

Data Properties

errorNaturedp back to ToC or Data Property ToC

IRI: https://purl.archive.org/akco#errorNature

The nature of an error- An error can be semantic or syntatic. A semantic error causes the error source to express wrong semantics (meaning). A syntatic error causes the error source to express no or wrong semantics.
has domain
Error c
has range
string

errorSourcedp back to ToC or Data Property ToC

IRI: https://purl.archive.org/akco#errorSource

The error source, in which an error originates. It is an assertion type, for example a wrong instance or property-value assertion.
has domain
Error c
has range
string

errorTypedp back to ToC or Data Property ToC

IRI: https://purl.archive.org/akco#errorType

"The error type is a specific error in an error source, caused by a certain type of error nature. For example, an instance assertion might have a syntatically wrong instance identifier or a syntatically wrong type name."
has domain
Error c
has range
string

Annotation Properties

Bibliographic Citationap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/terms/bibliographicCitation

has range
Literal
is also defined as
data property, named individual

creatorap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/elements/1.1/creator

dateap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/elements/1.1/date

has sub-properties
Date Issued ap, modified ap

Date Issuedap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/terms/issued

has super-properties
date ap
has range
Literal
is also defined as
data property, named individual

definitionap back to ToC or Annotation Property ToC

IRI: http://www.w3.org/2004/02/skos/core#definition

descriptionap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/terms/description

languageap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/elements/1.1/language

has sub-properties
Language ap

Languageap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/terms/language

has super-properties
language ap
is also defined as
named individual

licenseap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/terms/license

is also defined as
named individual

modifiedap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/terms/modified

has super-properties
date ap

pref Labelap back to ToC or Annotation Property ToC

IRI: http://www.w3.org/2004/02/skos/core#prefLabel

preferred Namespace Prefixap back to ToC or Annotation Property ToC

IRI: http://purl.org/vocab/vann/preferredNamespacePrefix

preferred Namespace Uriap back to ToC or Annotation Property ToC

IRI: http://purl.org/vocab/vann/preferredNamespaceUri

scope Noteap back to ToC or Annotation Property ToC

IRI: http://www.w3.org/2004/02/skos/core#scopeNote

sourceap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/elements/1.1/source

titleap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/elements/1.1/title

has sub-properties
title ap

titleap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/terms/title

has super-properties
title ap
has range
Literal
is also defined as
data property

typeap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/elements/1.1/type

has sub-properties
type ap

typeap back to ToC or Annotation Property ToC

IRI: http://purl.org/dc/terms/type

has super-properties
type ap

Named Individuals

appAPrInToImLiDaQuUsDiOuDeni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appAPrInToImLiDaQuUsDiOuDe

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publAPrInToImLiDaQuUsDiOuDe ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecStatistical ni

appAttributEni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appAttributE

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publAttributE ni
targetsError op errESemSemWrong ni
usesTechnique op tecEmbeddingAndNeuralNetwork-based ni

appCEDALni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appCEDAL

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publCEDAL ni
targetsError op errESemSemWrong ni
usesTechnique op tecPath-based ni

appCOLIBRIni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appCOLIBRI

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimExternal ni
isPublished op publCOLIBRI ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecStatistical ni

appCOPAALni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appCOPAAL

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
hasDimension op dimKnowledge-driven ni
isPublished op publCOPAAL ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecPath-based ni

appCorhistni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appCorhist

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimData-driven ni
hasDimension op dimExternal ni
isPublished op publCorhist ni
targetsError op errPVSemNotADomain ni
targetsError op errPVSemNotARange ni
usesTechnique op tecRuleMining-based ni

appDeanCoTyErinOpKnGrUsSeReofEnni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appDeanCoTyErinOpKnGrUsSeReofEn

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimExternal ni
hasDimension op dimInternal ni
isPublished op publDeanCoTyErinOpKnGrUsSeReofEn ni
targetsError op errISemSemWrong ni
usesBackgroundKnowledge op extSrcWikipedia ni
usesTechnique op tecEmbeddingAndNeuralNetwork-based ni

appDecefitogrni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appDecefitogr

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimDetection ni
hasDimension op dimExternal ni
hasDimension op dimKnowledge-driven ni
isPublished op publDecefitogr ni
targetsError op errESemSemWrong ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecHybrid ni

appDECIDEni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appDECIDE

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimInternal ni
hasDimension op dimKnowledge-driven ni
isPublished op publDECIDE ni
targetsError op errESemSemWrong ni
usesTechnique op tecOntology-based ni

appDeerinnulidauscroudeni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appDeerinnulidauscroude

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimExternal ni
hasDimension op dimInternal ni
hasDimension op dimKnowledge-driven ni
isPublished op publDeerinnulidauscroude ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecStatistical ni

appDeInNuDainDBni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appDeInNuDainDB

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publDeInNuDainDB ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecStatistical ni

appDojuanenbyitnaEntyuslamoni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appDojuanenbyitnaEntyuslamo

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publDojuanenbyitnaEntyuslamo ni
targetsError op errISemSemWrong ni
usesTechnique op tecEmbeddingAndNeuralNetwork-based ni

appFachvievpani back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appFachvievpa

belongs to
Approach c
has facts
hasDimension op dimDetection ni
hasDimension op dimExternal ni
hasDimension op dimKnowledge-driven ni
isPublished op publFachvievpa ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecPath-based ni

appFacTifyni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appFacTify

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimExternal ni
isPublished op publFacTify ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecVerbalization-based ni

appFaVawiKnGrEmni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appFaVawiKnGrEm

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publFaVawiKnGrEm ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecEmbeddingAndNeuralNetwork-based ni

appGATni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appGAT

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimData-driven ni
hasDimension op dimInternal ni
isPublished op publGAT ni
targetsError op errISemSemWrong ni
usesTechnique op tecEmbeddingAndNeuralNetwork-based ni

appGeneralCorrectionFrameworkni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appGeneralCorrectionFramework

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimData-driven ni
hasDimension op dimInternal ni
isPublished op publGeneralCorrectionFramework ni
targetsError op errESemSemWrong ni
targetsError op errPVSemNotARange ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecHybrid ni

appGltyfoliinrdtrwiaptoensuni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appGltyfoliinrdtrwiaptoensu

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimData-driven ni
hasDimension op dimInternal ni
isPublished op publGltyfoliinrdtrwiaptoensu ni
targetsError op errISemSemWrong ni
usesTechnique op tecStatistical ni

appIDLabTurtleValidatorni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appIDLabTurtleValidator

belongs to
Approach c
has facts
hasDimension op dimDetection ni
hasDimension op dimExternal ni
hasDimension op dimKnowledge-driven ni
isPublished op publIDLabTurtleValidator ni
targetsError op errESynNotPIdentID ni
targetsError op errESynNotPIdentIR ni
targetsError op errISynNotPIdent ni
usesTechnique op tecSyntactic ni

appIdwrlibedabymuoudeni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appIdwrlibedabymuoude

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publIdwrlibedabymuoude ni
targetsError op errESemSemWrong ni
usesTechnique op tecStatistical ni

appIOni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appIO

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimExternal ni
hasDimension op dimKnowledge-driven ni
isPublished op publIO ni
targetsError op errESemSemWrong ni
usesTechnique op tecOntology-based ni

appIOTWni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appIOTW

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimInternal ni
hasDimension op dimKnowledge-driven ni
isPublished op publIOTW ni
targetsError op errESemSemWrong ni
usesTechnique op tecOntology-based ni

appJSAEni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appJSAE

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publJSAE ni
targetsError op errESemSemWrong ni
usesTechnique op tecEmbeddingAndNeuralNetwork-based ni

appKD2Rni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appKD2R

belongs to
Approach c
has facts
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publKD2R ni
targetsError op errESemSemWrong ni
usesTechnique op tecPath-based ni

appKefogrni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appKefogr

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publKefogr ni
targetsError op errESemSemWrong ni
usesTechnique op tecPath-based ni

appKGCleanni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appKGClean

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publKGClean ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecEmbeddingAndNeuralNetwork-based ni

appKSni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appKS

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publKS ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecPath-based ni

appKV-ruleni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appKV-rule

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publKV-rule ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecRuleMining-based ni

appLediaxwiasrumianitaptoindeoflidani back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appLediaxwiasrumianitaptoindeoflida

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publLediaxwiasrumianitaptoindeoflida ni
targetsError op errISemSemWrong ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecRuleMining-based ni

appLeRiXtni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appLeRiXt

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publLeRiXt ni
targetsError op errPVSemNotADomain ni
targetsError op errPVSemNotARange ni
usesTechnique op tecStatistical ni

appLOD-Community-Detectionni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appLOD-Community-Detection

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publLOD-Community-Detection ni
targetsError op errESemSemWrong ni
usesTechnique op tecPath-based ni

appLodeofinSastinRDdani back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appLodeofinSastinRDda

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
hasDimension op dimKnowledge-driven ni
isPublished op publLodeofinSastinRDda ni
targetsError op errESemSemWrong ni
usesTechnique op tecPath-based ni

appLODLaundromatni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appLODLaundromat

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimDetection ni
hasDimension op dimExternal ni
hasDimension op dimKnowledge-driven ni
isPublished op publLODLaundromat ni
targetsError op errISemSynNotEType ni
targetsError op errISynNotPIdent ni
usesTechnique op tecSyntactic ni

appMTMni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appMTM

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimExternal ni
isPublished op publMTM ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecHybrid ni

appNotquitethesameni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appNotquitethesame

belongs to
Approach c
has facts
hasDimension op dimDetection ni
hasDimension op dimInternal ni
hasDimension op dimKnowledge-driven ni
isPublished op publNotquitethesame ni
targetsError op errESemSemWrong ni
usesTechnique op tecPath-based ni

appOGFCni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appOGFC

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publOGFC ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecPath-based ni

appOptimalABoxRepairw.r.t.StaticELTBoxesni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appOptimalABoxRepairw.r.t.StaticELTBoxes

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimInternal ni
hasDimension op dimKnowledge-driven ni
isPublished op publOptimalABoxRepairw.r.t.StaticELTBoxes ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecOntology-based ni

appPaTyBREDni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appPaTyBRED

belongs to
Approach c
has facts
hasDimension op dimDetection ni
hasDimension op dimInternal ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecHybrid ni
usesTechnique op tecPath-based ni
usesTechnique op tecStatistical ni

appPredPathni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appPredPath

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
hasDimension op dimKnowledge-driven ni
isPublished op publPredPath ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecPath-based ni

appPrerdemofokngrreni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appPrerdemofokngrre

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publPrerdemofokngrre ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecStatistical ni

appRDFDoctorni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appRDFDoctor

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimDetection ni
hasDimension op dimExternal ni
hasDimension op dimKnowledge-driven ni
isPublished op publRDFDoctor ni
targetsError op errESynNotPIdentID ni
targetsError op errESynNotPIdentIR ni
usesTechnique op tecSyntactic ni

apprdfvalidatorni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#apprdfvalidator

belongs to
Approach c
has facts
hasDimension op dimDetection ni
hasDimension op dimExternal ni
hasDimension op dimKnowledge-driven ni
isPublished op publrdfvalidator ni
targetsError op errISemSemWrong ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecIntegrityConstraint-based ni

appReRaViinDBni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appReRaViinDB

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimData-driven ni
hasDimension op dimInternal ni
isPublished op publReRaViinDB ni
targetsError op errPVSemNotARange ni
usesTechnique op tecPath-based ni

appRUGEni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appRUGE

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publRUGE ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecEmbeddingAndNeuralNetwork-based ni

appS3Kni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appS3K

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimExternal ni
isPublished op publS3K ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecVerbalization-based ni

appSDTypeni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appSDType

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimData-driven ni
hasDimension op dimInternal ni
isPublished op publSDType ni
targetsError op errISemSemWrong ni
usesTechnique op tecStatistical ni

appSDValidateni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appSDValidate

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publSDValidate ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecStatistical ni

appSiApCofoRDMoni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appSiApCofoRDMo

belongs to
Approach c
has facts
hasDimension op dimDetection ni
hasDimension op dimExternal ni
hasDimension op dimKnowledge-driven ni
isPublished op publSiApCofoRDMo ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecIntegrityConstraint-based ni

appSLCNni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appSLCN

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimData-driven ni
hasDimension op dimInternal ni
hasDimension op dimKnowledge-driven ni
isPublished op publSLCN ni
targetsError op errISemSemWrong ni
usesTechnique op tecStatistical ni

appToDeSePrfoRDusSPasInLani back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appToDeSePrfoRDusSPasInLa

belongs to
Approach c
has facts
hasDimension op dimDetection ni
hasDimension op dimExternal ni
hasDimension op dimKnowledge-driven ni
isPublished op publToDeSePrfoRDusSPasInLa ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecIntegrityConstraint-based ni

appTripleNetni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appTripleNet

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publTripleNet ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecEmbeddingAndNeuralNetwork-based ni

appTypingErrorsinFactualKnowledgeGraphsni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appTypingErrorsinFactualKnowledgeGraphs

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publTypingErrorsinFactualKnowledgeGraphs ni
targetsError op errISemSemWrong ni
usesTechnique op tecEmbeddingAndNeuralNetwork-based ni

appTyPrfoEfCoReinHeSeGrni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appTyPrfoEfCoReinHeSeGr

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimData-driven ni
hasDimension op dimInternal ni
isPublished op publTyPrfoEfCoReinHeSeGr ni
targetsError op errISemSemWrong ni
usesTechnique op tecStatistical ni

appUsReofInKnBani back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appUsReofInKnBa

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimDetection ni
hasDimension op dimExternal ni
hasDimension op dimKnowledge-driven ni
isPublished op publUsReofInKnBa ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecIntegrityConstraint-based ni

appVGFDni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appVGFD

belongs to
Approach c
has facts
hasDimension op dimData-driven ni
hasDimension op dimDetection ni
hasDimension op dimInternal ni
isPublished op publVGFD ni
targetsError op errPVSemSemWrong ni
usesTechnique op tecIntegrityConstraint-based ni

appWhenowlni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appWhenowl

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimInternal ni
hasDimension op dimKnowledge-driven ni
isPublished op publWhenowl ni
targetsError op errESemSemWrong ni
usesTechnique op tecOntology-based ni

appWhEvlidahewiaquthclupDBni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#appWhEvlidahewiaquthclupDB

belongs to
Approach c
has facts
hasDimension op dimCorrection ni
hasDimension op dimDetection ni
hasDimension op dimExternal ni
hasDimension op dimKnowledge-driven ni
isPublished op publWhEvlidahewiaquthclupDB ni
targetsError op errESemSemWrong ni
targetsError op errESynNotPIdentID ni
targetsError op errESynNotPIdentIR ni
usesTechnique op tecCrowdsourcing-based ni

dimCorrectionni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#dimCorrection

The error-correction dimension describes approaches and techniques that determine a replacement entity for an erroneous entity in an assertion. Typical examples for this dimension are type or link-prediction approaches.
belongs to
Dimension c

dimData-drivenni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#dimData-driven

The data-driven dimension describes approaches and techniques that leverage statistical methods and machine learning to detect and correct errors. For example, embeddings use other assertions in the knowledge graph as context when transforming an assertion into a low-dimensional vector representation. Additionally, outlier-detection methods can identify anomalies in numerical property values, assuming a consistent domain and context.
belongs to
Dimension c

dimDetectionni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#dimDetection

The error-detection dimension describes approaches and techniques aimed at identifying errors within a knowledge graph or a specific assertion. When the focus is on a single assertion, this process is often referred to as fact-checking
belongs to
Dimension c
has facts
hasDimension op dimDetection ni

dimExternalni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#dimExternal

The external dimension describes approaches and techniques that rely on background knowledge from sources external to the given knowledge graph.
belongs to
Dimension c

dimInternalni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#dimInternal

The internal dimension describes approaches and techniques that rely solely on the given knowledge graph for background knowledge.
belongs to
Dimension c

dimKnowledge-drivenni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#dimKnowledge-driven

The knowledge-driven dimension describes approaches and techniques that rely on domain knowledge—such as ontology-based reasoning or human expertise—to detect and correct errors.
belongs to
Dimension c

errESemSemWrongni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#errESemSemWrong

Error is of semantic nature, afflicting equality assertions, and causing a semantically wrong equality assertion.
belongs to
Error c
has facts
errorNature dp "Semantic"@en
errorSource dp "Equality assertion"@en
errorType dp "Assertion is semantically wrong."@en

errESynNotPIdentIDni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#errESynNotPIdentID

Error is of syntactic nature, afflicting equality assertions, and causing that the instance at the domain position uses not a proper instance identifier.
belongs to
Error c
has facts
errorNature dp "Syntactic"@en
errorSource dp "Equality assertion"@en
errorType dp "Instance (domain) uses not a proper instance identifier."@en

errESynNotPIdentIRni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#errESynNotPIdentIR

Error is of syntactic nature, afflicting equality assertions, and causing that the instance at the range position uses not a proper instance identifier.
belongs to
Error c
has facts
errorNature dp "Syntactic"@en
errorSource dp "Equality assertion"@en
errorType dp "Instance (range) uses not a proper instance identifier."@en

errISemSemWrongni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#errISemSemWrong

Error is of semantic nature, afflicting instance assertions, and causing a semantically wrong instance assertion.
belongs to
Error c
has facts
errorNature dp "Semantic"@en
errorSource dp "Instance assertion"@en
errorType dp "Assertion is semantically wrong."@en

errISemSynNotETypeni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#errISemSynNotEType

Error is of semantic or syntactic nature, afflicting instance assertions, and causing that the assertion uses a not existing type name..
belongs to
Error c
has facts
errorNature dp "Semantic"@en
errorNature dp "Syntactic"@en
errorSource dp "Instance assertion"@en
errorType dp "Assertion uses not an existing type name."@en

errISynNotPIdentni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#errISynNotPIdent

Error is of syntactic nature, afflicting instance assertions, and causing that the instance at the domain position uses not a proper instance identifier.
belongs to
Error c
has facts
errorNature dp "Syntactic"@en
errorSource dp "Instance assertion"@en
errorType dp "Instance (domain) uses not a propert instance identifier."@en

errPVSemNotADomainni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#errPVSemNotADomain

Error is of semantic nature, afflicting property-value assertions, and causing that the instance at the domain position uses a type that is not in any domain of the property.
belongs to
Error c
has facts
errorNature dp "Semantic"@en
errorSource dp "Property-value assertion"@en
errorType dp "Instance (domain) is not in any domain of property."@en

errPVSemNotARangeni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#errPVSemNotARange

Error is of semantic nature, afflicting property-value assertions, and causing that the instance at the range position uses a type that is not in any domain of the property.
belongs to
Error c
has facts
errorNature dp "Semantic"@en
errorSource dp "Property-value assertion"@en
errorType dp "Instance (range) is not in any range of property."@en

errPVSemSemWrongni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#errPVSemSemWrong

Error is of semantic nature, afflicting property-value assertions, and causing a semantically wrong property-value assertion.
belongs to
Error c
has facts
has narrower op errSystematic ni
has narrower op errTailVertical ni
errorNature dp "Semantic"@en
errorSource dp "Property-value assertion"@en
errorType dp "Assertion is semantically wrong."@en

errPVSemSynNotEPropertyni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#errPVSemSynNotEProperty

Error is of semantic or syntactic nature, afflicting property-value assertions, and causing that the assertion uses a not existing property name..
belongs to
Error c
has facts
errorNature dp "Semantic"@en
errorNature dp "Syntactic"@en
errorSource dp "Property-value assertion"@en
errorType dp "Assertion uses not an existing property name."@en

errPVSynNotPIdentIDni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#errPVSynNotPIdentID

Error is of syntactic nature, afflicting property-value assertions, and causing that the instance at the domain position uses not a proper instance identifier.
belongs to
Error c
has facts
errorNature dp "Syntactic"@en
errorSource dp "Property-value assertion"@en
errorType dp "Instance (domain) uses not a proper instance identifier."@en

errPVSynNotPIdentIRni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#errPVSynNotPIdentIR

Error is of syntactic nature, afflicting property-value assertions, and causing that the instance at the range position uses not a proper instance identifier.
belongs to
Error c
has facts
errorNature dp "Syntactic"@en
errorSource dp "Property-value assertion"@en
errorType dp "Instance (range) uses not a proper instance identifier."@en

errSystematicni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#errSystematic

A systematic error is an error that occurs during the knowledge creation process or was part of the original source. It is characterized by the same source and occurence.
belongs to
Error c
has facts
has broader op errPVSemSemWrong ni

errTailVerticalni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#errTailVertical

A tail vertical is a domain that is not well covered by a knowledge graph. Entities in these domains are typically sparse.
belongs to
Error c
has facts
has broader op errPVSemSemWrong ni

extSrcAnyni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#extSrcAny

An external source is any source outside of a given knowledge graph. This individual represents any of those sources.
belongs to
ExternalSource c
has facts
has narrower op extSrcFormalAndSyntaxSpecifications ni
has narrower op extSrcKnowledgeGraph ni
has narrower op extSrcQueryLogs ni
has narrower op extSrcTextCorpus ni
has narrower op extSrcWeb ni

extSrcDBpediani back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#extSrcDBpedia

DBpedia is the central knowledge graph of the Linked Open Data cloud. Its source is Wikipedia.
belongs to
ExternalSource c
has facts
has broader op extSrcKnowledgeGraph ni

extSrcDomaInExpertiseni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#extSrcDomaInExpertise

Human interaction is another particular type of external source. It is versatile and uses human expertise to perform any cleaning task, including setting up and maintaining more elaborate cleaning approaches. It is mostly independent of the quality of the KG, except for validation where additional contextual knowledge might be required. Human interaction is typically included to allow for semi-automatic cleaning in combination with various cleaning techniques.
belongs to
ExpertKnowledge c

extSrcExternalSourcesni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#extSrcExternalSources

External sources are any sources outside the KG. Typical background knowledge is websites, large language models, documents, other knowledge graphs, and databases. The success of using the sources for knowledge cleaning depends on the quality of the sources and the acquisition process. The latter includes the alignment of the assertion(s) with the external source and the correct interpretation of the received results.

extSrcFormalAndSyntaxSpecificationsni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#extSrcFormalAndSyntaxSpecifications

Formal - and syntax specifications are a particular type of external background knowledge. Both are designed to explicitly define desired aspects of a knowledge graph. The aspects can include an expected graph structure, like cardinality, syntax rules of the domain and modeling language, or semantic features of a property. Specifications are independent of the quality of the assertions. Typical formal specifications are ontologies, integrity constraints, and logic rules. Typical syntax specifications are the RDF specification , or Turtle specification .
belongs to
ExternalSource c
has facts
has broader op extSrcAny ni

extSrcHornClausesni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#extSrcHornClauses

Horn clauses, obtain from rule mining, that rewrite a fact into a set of easier-to-spot facts.
belongs to
ExternalSource c
has facts
has broader op extSrcAny ni

extSrcKnowledgeGraphni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#extSrcKnowledgeGraph

Another Knowledge graph not yet part of the given knowledge graph, in a related domain.
belongs to
ExternalSource c
has facts
has broader op extSrcAny ni
has narrower op extSrcDBpedia ni

extSrcQueryLogsni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#extSrcQueryLogs

Query logs represent the context of entities, generated by a user.
belongs to
ExternalSource c
has facts
has broader op extSrcAny ni

extSrcTextCorpusni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#extSrcTextCorpus

A text corpus that contains information about the domain of a given knowledge graph.
belongs to
ExternalSource c
has facts
has broader op extSrcAny ni

extSrcWebni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#extSrcWeb

The World Wide Web is a rich source of information on almost any topic. It can be accessed, for example, using search engines or direct calls to websites. The resulting information can include different types of noise, ranging from contradicting information to misinformation.
belongs to
ExternalSource c
has facts
has broader op extSrcAny ni
has narrower op extSrcWikipedia ni

extSrcWikipediani back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#extSrcWikipedia

Wikipedia is a free online encyclopedia that is written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and the wiki software MediaWiki. It provides structured (tables, annotations) and unstructured information about many domains.
belongs to
ExternalSource c
has facts
has broader op extSrcWeb ni

kGContextualGraphni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#kGContextualGraph

Contextual graphs are subgraphs of relevant assertions. Assertions are relevant if they are part of the context, which is used within error detection or correction. For example, the context could identify an instance to validate equality assertions, or it is identified as part of reasoning, and contains contradicting knowledge. A contextual graph can also be be beneficial to provide a context for clarifying the domain of an assertion during error detection. They are generated in various ways, for example, by selecting all assertions conforming to specific semantics, such as transitive properties, or constructing it as an undirected identity network (Raad, Beek, Harmelen, et al., 2018).
belongs to
KnowledgeGraph c

kGGraphStructureni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#kGGraphStructure

Knowledge graphs represent knowledge as a collection of entities (nodes) and relations (edges) between the entities. The graph structure is used by various cleaning techniques as background knowledge. It is an internal type of background knowledge. It is independent of assertion type and formal semantics of assertions. However, it depends on the overall quality (correctness &amp;amp; completeness) of the overall KG since it is typically used with data-driven measures.
belongs to
KnowledgeGraph c

kGValuesOfPVni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#kGValuesOfPV

The values of property-value assertions include numerical literal values and URI instances. They are an internal type of background knowledge and depend on the relevant subset of the KG for a target property-value assertion. The values are targeted using statistical approaches.
belongs to
KnowledgeGraph c

publADianReFrfoDLKBni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publADianReFrfoDLKB

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Chortis, M., & Flouris, G. (2015). A diagnosis and repair framework for DL-LiteA KBs. European Semantic Web Conference, 199–214."@en
Date Issued dp "2015"^^date

publAPEni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publAPE

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Jin, H., Hou, L., Li, J., & Dong, T. (2018). Attributed and predictive entity embedding for fine-grained entity typing in knowledge bases. Proceedings of the 27th International Conference on Computational Linguistics, 282–292."@en
Date Issued dp "2018"^^date

publAPrInToImLiDaQuUsDiOuDeni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publAPrInToImLiDaQuUsDiOuDe

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Debattista, J., Lange, C., & Auer, S. (2016). A preliminary investigation towards improving linked data quality using distance-based outlier detection. Joint International Semantic Technology Conference, 116–124."@en
Date Issued dp "2016"^^date

publAttributEni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publAttributE

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Trisedya, B. D., Qi, J., & Zhang, R. (2019). Entity alignment between knowledge graphs using attribute embeddings. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 297–304."@en
Date Issued dp "2019"^^date

publBioGRERni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publBioGRER

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Zhao, S., Qin, B., Liu, T., & Wang, F. (2020). Biomedical knowledge graph refinement with embedding and logic rules. arXiv Preprint arXiv:2012.01031."@en
Date Issued dp "2020"^^date

publCEDALni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publCEDAL

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Valdestilhas, A., Soru, T., & Ngomo, A.-C. N. (2017). Cedal: time-efficient detection of erroneous links in large-scale link repositories. Proceedings of the International Conference on Web Intelligence, 106–113."@en
Date Issued dp "2017"^^date

publChanHaInofDBni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publChanHaInofDB

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Sheng, Z., Wang, X., Shi, H., & Feng, Z. (2012). Checking and handling inconsistency of DBpedia. International Conference on Web Information Systems and Mining, 480–488."@en
Date Issued dp "2012"^^date

publCoCKGni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publCoCKG

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Melo, A., & Paulheim, H. (2020). Automatic detection of relation assertion errors and induction of relation constraints. Semantic Web, 11(5), 801–830."@en
Date Issued dp "2020"^^date

publCOLIBRIni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publCOLIBRI

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Ngonga Ngomo, A.-C., Sherif, M. A., & Lyko, K. (2014). Unsupervised link discovery through knowledge base repair. European Semantic Web Conference, 380–394."@en
Date Issued dp "2014"^^date

publCOPAALni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publCOPAAL

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Syed, Z. H., Röder, M., & Ngomo, A.-C. N. (2019). Unsupervised discovery of corroborative paths for fact validation. International Semantic Web Conference, 630–646."@en
Date Issued dp "2019"^^date

publCorhistni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publCorhist

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Pellissier Tanon, T., Bourgaux, C., & Suchanek, F. (2019). Learning how to correct a knowledge base from the edit history. The World Wide Web Conference, 1465–1475."@en
Date Issued dp "2019"^^date

publDBOnEnfoInDeni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publDBOnEnfoInDe

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Töpper, G., Knuth, M., & Sack, H. (2012). DBpedia ontology enrichment for inconsistency detection. Proceedings of the 8th International Conference on Semantic Systems, 33–40."@en
Date Issued dp "2012"^^date

publDeanCoTyErinOpKnGrUsSeReofEnni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publDeanCoTyErinOpKnGrUsSeReofEn

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Caminhas, D. D. (2019). Detecting and correcting typing errors in open-domain knowledge graphs using semantic representation of entities."@en
Date Issued dp "2019"^^date

publDecefitogrni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publDecefitogr

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Fan, W., Lu, P., Tian, C., & Zhou, J. (2019). Deducing certain fixes to graphs. Proceedings of the VLDB Endowment, 12(7), 752–765."@en
Date Issued dp "2019"^^date

publDECIDEni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publDECIDE

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Raad, J., Pernelle, N., & Saı̈s, F. (2017). Detection of contextual identity links in a knowledge base. Proceedings of the Knowledge Capture Conference, 1–8."@en
Date Issued dp "2017"^^date

publDeerinnulidauscroudeni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publDeerinnulidauscroude

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Fleischhacker, D., Paulheim, H., Bryl, V., Völker, J., & Bizer, C. (2014). Detecting errors in numerical linked data using cross-checked outlier detection. International Semantic Web Conference, 357–372."@en
Date Issued dp "2014"^^date

publDeFactoni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publDeFacto

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Gerber, D., Esteves, D., Lehmann, J., Bühmann, L., Usbeck, R., Ngomo, A.-C. N., & Speck, R. (2015). Defacto—temporal and multilingual deep fact validation. Journal of Web Semantics, 35, 85–101."@en
Date Issued dp "2015"^^date

publDeInNuDainDBni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publDeInNuDainDB

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Wienand, D., & Paulheim, H. (2014). Detecting incorrect numerical data in dbpedia. European Semantic Web Conference, 504–518."@en
Date Issued dp "2014"^^date

publDojuanenbyitnaEntyuslamoni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publDojuanenbyitnaEntyuslamo

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Biswas, R., Sofronova, R., Alam, M., Heist, N., Paulheim, H., & Sack, H. (2021). Do judge an entity by its name! Entity typing using language models. European Semantic Web Conference, 65–70."@en
Date Issued dp "2021"^^date

publExfaktni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publExfakt

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Gad-Elrab, M. H., Stepanova, D., Urbani, J., & Weikum, G. (2019). Exfakt: A framework for explaining facts over knowledge graphs and text. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 87–95."@en
Date Issued dp "2019"^^date

publFachvievpani back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publFachvievpa

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Fionda, V., & Pirrò, G. (2018). Fact checking via evidence patterns. Proceedings of the 27th International Joint Conference on Artificial Intelligence, 3755–3761."@en
Date Issued dp "2018"^^date

publFactCheckni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publFactCheck

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Syed, Z. H., Röder, M., & Ngonga Ngomo, A.-C. (2018). Factcheck: Validating rdf triples using textual evidence. Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 1599–1602."@en
Date Issued dp "2018"^^date

publFacTifyni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publFacTify

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Ercan, G., Elbassuoni, S., & Hose, K. (2019). Retrieving textual evidence for knowledge graph facts. European Semantic Web Conference, 52–67."@en
Date Issued dp "2019"^^date

publFACTYni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publFACTY

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Li, F., Dong, X. L., Langen, A., & Li, Y. (2017). Knowledge verification for long-tail verticals. Proceedings of the VLDB Endowment, 10(11), 1370–1381."@en
Date Issued dp "2017"^^date

publFaVawiKnGrEmni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publFaVawiKnGrEm

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Ammar, A., & Celebi, R. (2019). Fact Validation with Knowledge Graph Embeddings. ISWC (Satellites), 125–128."@en
Date Issued dp "2019"^^date

publFEAni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publFEA

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Pirrò, G. (2020). Fact Checking via Path Embedding and Aggregation. arXiv Preprint arXiv:2011.08028."@en
Date Issued dp "2020"^^date

publGATni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publGAT

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., & Bengio, Y. (2017). Graph attention networks. arXiv Preprint arXiv:1710.10903."@en
Date Issued dp "2017"^^date

publGeneralCorrectionFrameworkni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publGeneralCorrectionFramework

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Chen, J., Jiménez-Ruiz, E., Horrocks, I., Chen, X., & Myklebust, E. B. (2021). Correcting Assertions and Alignments of Large Scale Knowledge Bases."@en
Date Issued dp "2021"^^date

publGltyfoliinrdtrwiaptoensuni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publGltyfoliinrdtrwiaptoensu

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Gunaratna, K., Thirunarayan, K., Sheth, A., & Cheng, G. (2016). Gleaning types for literals in rdf triples with application to entity summarization. European Semantic Web Conference, 85–100."@en
Date Issued dp "2016"^^date

publGRRni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publGRR

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Cheng, Y., Chen, L., Yuan, Y., & Wang, G. (2018). Rule-based graph repairing: Semantic and efficient repairing methods. 2018 IEEE 34th International Conference on Data Engineering (ICDE), 773–784."@en
Date Issued dp "2018"^^date

publHMGCNni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publHMGCN

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Jin, H., Hou, L., Li, J., & Dong, T. (2019). Fine-grained entity typing via hierarchical multi graph convolutional networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 4969–4978."@en
Date Issued dp "2019"^^date

publHybridFCni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publHybridFC

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Qudus, U., Röder, M., Saleem, M., & Ngonga Ngomo, A.-C. (2022). HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs. International Semantic Web Conference, 462–480."@en
Bibliographic Citation dp "Qudus, U., Röder, M., Saleem, M., &amp;amp; Ngonga Ngomo, A.-C. (2022). HybridFC: A hybrid fact-checking approach for knowledge graphs. In International Semantic Web Conference (pp. 462–480). Springer. - annotation"@en
Date Issued dp "2022"^^date
title dp "HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs - datatype"@en

publIDLabTurtleValidatorni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publIDLabTurtleValidator

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "error-cit"@en
Date Issued dp "null"^^date

publIdwrlibedabymuoudeni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publIdwrlibedabymuoude

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Paulheim, H. (2014). Identifying wrong links between datasets by multi-dimensional outlier detection. WoDOOM, 27–38."@en
Date Issued dp "2014"^^date

publIncoinOWni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publIncoinOW

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Tao, J., Sirin, E., Bao, J., & McGuinness, D. (2010). Integrity constraints in OWL. Proceedings of the AAAI Conference on Artificial Intelligence, 24(1), 1443–1448."@en
Date Issued dp "2010"^^date

publIOni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publIO

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "McCusker, J. P., & McGuinness, D. L. (2010). Towards Identity in Linked Data. OWLED."@en
Date Issued dp "2010"^^date

publIOTWni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publIOTW

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Beek, W., Schlobach, S., & Harmelen, F. van. (2016). A contextualised semantics for owl: sameAs. European Semantic Web Conference, 405–419."@en
Date Issued dp "2016"^^date

publJSAEni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publJSAE

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Munne, R. F., & Ichise, R. (2020). Joint entity summary and attribute embeddings for entity alignment between knowledge graphs. Hybrid Artificial Intelligent Systems: 15th International Conference, HAIS 2020, Gijón, Spain, November 11-13, 2020, Proceedings, 107–119."@en
Date Issued dp "2020"^^date

publKD2Rni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publKD2R

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Pernelle, N., Saı̈s, F., & Symeonidou, D. (2013). An automatic key discovery approach for data linking. Journal of Web Semantics, 23, 16–30."@en
Date Issued dp "2013"^^date

publKefogrni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publKefogr

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Fan, W., Fan, Z., Tian, C., & Dong, X. L. (2015). Keys for graphs. Proceedings of the VLDB Endowment, 8(12), 1590–1601."@en
Date Issued dp "2015"^^date

publKGCleanni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publKGClean

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Ge, C., Gao, Y., Weng, H., Zhang, C., Miao, X., & Zheng, B. (2020). Kgclean: An embedding powered knowledge graph cleaning framework. arXiv Preprint arXiv:2004.14478."@en
Date Issued dp "2020"^^date

publKGTtmni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publKGTtm

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Jia, S., Xiang, Y., Chen, X., & Wang, K. (2019). Triple trustworthiness measurement for knowledge graph. The World Wide Web Conference, 2865–2871."@en
Date Issued dp "2019"^^date

publKSni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publKS

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Shiralkar, P., Flammini, A., Menczer, F., & Ciampaglia, G. L. (2017). Finding streams in knowledge graphs to support fact checking. 2017 IEEE International Conference on Data Mining (ICDM), 859–864."@en
Date Issued dp "2017"^^date

publKV-ruleni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publKV-rule

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Kim, J.-S., & Choi, K.-S. (2021). Fact checking in knowledge graphs by logical consistency."@en
Date Issued dp "2021"^^date

publLediaxwiasrumianitaptoindeoflidani back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publLediaxwiasrumianitaptoindeoflida

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Ma, Y., Gao, H., Wu, T., & Qi, G. (2014). Learning disjointness axioms with association rule mining and its application to inconsistency detection of linked data. Chinese Semantic Web and Web Science Conference, 29–41."@en
Date Issued dp "2014"^^date

publLeRiXtni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publLeRiXt

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Tonon, A., Catasta, M., Demartini, G., & Cudré-Mauroux, P. (2015). Fixing the Domain and Range of Properties in Linked Data by Context Disambiguation. LDOW@ WWW, 1409."@en
Date Issued dp "2015"^^date

publLOD-Community-Detectionni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publLOD-Community-Detection

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Raad, J., Beek, W., Harmelen, F. van, Pernelle, N., & Saı̈s, F. (2018). Detecting erroneous identity links on the web using network metrics. International Semantic Web Conference, 391–407."@en
Date Issued dp "2018"^^date

publLodeofinSastinRDdani back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publLodeofinSastinRDda

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Papaleo, L., Pernelle, N., Saı̈s, F., & Dumont, C. (2014). Logical detection of invalid sameas statements in RDF data. International Conference on Knowledge Engineering and Knowledge Management, 373–384."@en
Date Issued dp "2014"^^date

publLODLaundromatni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publLODLaundromat

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Beek, W., Rietveld, L., Bazoobandi, H. R., Wielemaker, J., & Schlobach, S. (2014). LOD laundromat: a uniform way of publishing other people’s dirty data. International Semantic Web Conference, 213–228."@en
Date Issued dp "2014"^^date

publMTMni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publMTM

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Yu, D., Huang, H., Cassidy, T., Ji, H., Wang, C., Zhi, S., Han, J., Voss, C., & Magdon-Ismail, M. (2014). The wisdom of minority: Unsupervised slot filling validation based on multi-dimensional truth-finding. Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, 1567–1578."@en
Date Issued dp "2014"^^date

publNotquitethesameni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publNotquitethesame

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "De Melo, G. (2013). Not quite the same: Identity constraints for the web of linked data. Twenty-Seventh AAAI Conference on Artificial Intelligence."@en
Date Issued dp "2013"^^date

publOGFCni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publOGFC

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Lin, P., Song, Q., & Wu, Y. (2018). Fact checking in knowledge graphs with ontological subgraph patterns. Data Science and Engineering, 3(4), 341–358."@en
Date Issued dp "2018"^^date

publOptimalABoxRepairw.r.t.StaticELTBoxesni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publOptimalABoxRepairw.r.t.StaticELTBoxes

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Baader, F., Koopmann, P., Kriegel, F., & Nuradiansyah, A. (n.d.). Optimal ABox Repair wrt Static EL TBoxes: from Quantified ABoxes back to ABoxes."@en
Date Issued dp "2022"^^date

publPredPathni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publPredPath

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Shi, B., & Weninger, T. (2016). Discriminative predicate path mining for fact checking in knowledge graphs. Knowledge-Based Systems, 104, 123–133."@en
Date Issued dp "2016"^^date

publPrerdemofokngrreni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publPrerdemofokngrre

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Jeyaraj, M. N., Perera, S., Jayasinghe, M., & Jihan, N. (2019). Probabilistic error detection model for knowledge graph refinement. Proceedings of the International Conference on Computational Linguistics and Intelligent Text Processing (CiCLing’19)."@en
Date Issued dp "2019"^^date

publRDD-Checkerni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publRDD-Checker

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Fischer, P. M., Lausen, G., Schätzle, A., & Schmidt, M. (2015). RDF constraint checking."@en
Date Issued dp "2015"^^date

publRDFDoctorni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publRDFDoctor

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Hemid, A., Halilaj, L., Khiat, A., & Lohmann, S. (2019). RDF Doctor: A Holistic Approach for Syntax Error Detection and Correction of RDF Data. KEOD, 508–516."@en
Date Issued dp "2019"^^date

publRDFUnitni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publRDFUnit

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Kontokostas, D., Westphal, P., Auer, S., Hellmann, S., Lehmann, J., Cornelissen, R., & Zaveri, A. (2014). Test-driven evaluation of linked data quality. Proceedings of the 23rd International Conference on World Wide Web, 747–758."@en
Date Issued dp "2014"^^date

publrdfvalidatorni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publrdfvalidator

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Hartmann, T. (2016). Validation framework for rdf-based constraint languages [Phdthesis]. Karlsruhe Institute of Technology, Germany."@en
Date Issued dp "2016"^^date

publRDvaseni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publRDvase

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Prud’hommeaux, E. (2007). RDF validation service. W3C. https://www.w3.org/RDF/Validator/documentation"@en
Date Issued dp "null"^^date

publReRaViinDBni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publReRaViinDB

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Lertvittayakumjorn, P., Kertkeidkachorn, N., & Ichise, R. (2017). Resolving range violations in DBpedia. Joint International Semantic Technology Conference, 121–137."@en
Date Issued dp "2017"^^date

publRUGEni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publRUGE

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Guo, S., Wang, Q., Wang, L., Wang, B., & Guo, L. (2018). Knowledge graph embedding with iterative guidance from soft rules. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1)."@en
Date Issued dp "2018"^^date

publS3Kni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publS3K

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Metzger, S., Elbassuoni, S., Hose, K., & Schenkel, R. (2011). S3K: seeking statement-supporting top-K witnesses. Proceedings of the 20th ACM International Conference on Information and Knowledge Management, 37–46."@en
Date Issued dp "2011"^^date

publSDTypeni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publSDType

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Paulheim, H., & Bizer, C. (2013). Type inference on noisy RDF data. International Semantic Web Conference, 510–525."@en
Date Issued dp "2013"^^date

publSDValidateni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publSDValidate

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Paulheim, H., & Bizer, C. (2014). Improving the quality of linked data using statistical distributions. International Journal on Semantic Web and Information Systems (IJSWIS), 10(2), 63–86."@en
Date Issued dp "2014"^^date

publServDBpediaDOLCEni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publServDBpediaDOLCE

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Paulheim, H., & Gangemi, A. (2015). Serving DBpedia with DOLCE–more than just adding a cherry on top. International Semantic Web Conference, 180–196."@en
Date Issued dp "2015"^^date

publSiApCofoRDMoni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publSiApCofoRDMo

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Simister, S., & Brickley, D. (2013). Simple application-specific constraints for rdf models. RDF Validation Workshop. Practical Assurances for Quality RDF Data, Cambridge, Ma, Boston."@en
Date Issued dp "2013"^^date

publSLCNni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publSLCN

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Melo, A., Völker, J., & Paulheim, H. (2017). Type prediction in noisy RDF knowledge bases using hierarchical multilabel classification with graph and latent features. International Journal on Artificial Intelligence Tools, 26(02), 1760011."@en
Date Issued dp "2017"^^date

publTISCOni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publTISCO

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Rula, A., Palmonari, M., Rubinacci, S., Ngomo, A.-C. N., Lehmann, J., Maurino, A., & Esteves, D. (2019). TISCO: Temporal scoping of facts. Journal of Web Semantics, 54, 72–86."@en
Date Issued dp "2019"^^date

publToDeSePrfoRDusSPasInLani back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publToDeSePrfoRDusSPasInLa

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Bosch, T., & Eckert, K. (2014). Towards description set profiles for RDF using SPARQL as intermediate language. International Conference on Dublin Core and Metadata Applications, 129–137."@en
Date Issued dp "2014"^^date

publTripleNetni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publTripleNet

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Liu, Y. (2021). Error Detection in Knowledge Graphs [Phdthesis]."@en
Date Issued dp "2021"^^date

publTypingErrorsinFactualKnowledgeGraphsni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publTypingErrorsinFactualKnowledgeGraphs

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Yao, P., & Barbosa, D. (2021). Typing errors in factual knowledge graphs: Severity and possible ways out. Proceedings of the Web Conference 2021, 3305–3313."@en
Date Issued dp "null"^^date

publTyPrfoEfCoReinHeSeGrni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publTyPrfoEfCoReinHeSeGr

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Sleeman, J., & Finin, T. (2013). Type prediction for efficient coreference resolution in heterogeneous semantic graphs. 2013 IEEE Seventh International Conference on Semantic Computing, 78–85."@en
Date Issued dp "2013"^^date

publUsReofInKnBani back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publUsReofInKnBa

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Arioua, A., & Bonifati, A. (2018). User-guided repairing of inconsistent knowledge bases. EDBT 2018-21st International Conference on Extending Database Technology, 133–144."@en
Date Issued dp "null"^^date

publValidatani back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publValidata

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Baungard Hansen, J., Beveridge, A., Farmer, R., Gehrmann, L., Gray, A., Khutan, S., Robertson, T., Val, J., Malone, J., Stevens, R., & others. (2015). Validata: an online tool for testing RDF data conformance. Proceedings of the 8th Semantic Web Applications and Tools for Life Sciences International Conference, Cambridge UK, 1546, 157–166."@en
Date Issued dp "2015"^^date

publValidatrrni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publValidatrr

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "De Meester, B., Heyvaert, P., Arndt, D., Dimou, A., & Verborgh, R. (2021). RDF graph validation using rule-based reasoning. Semantic Web, 12(1), 117–142."@en
Date Issued dp "2021"^^date

publVeriGraphni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publVeriGraph

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Fensel, D., Şimşek, U., Angele, K., Huaman, E., Kärle, E., Panasiuk, O., & Omar, H. (2020). VeriGraph: A verification framework for Knowledge Integrity [Report]. MindLab."@en
Date Issued dp "2020"^^date

publVGFDni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publVGFD

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Yu, Y., & Heflin, J. (2011). Extending functional dependency to detect abnormal data in RDF graphs. International Semantic Web Conference, 794–809."@en
Date Issued dp "2011"^^date

publVRPni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publVRP

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Tolle, K. (2000). Validating RDF Parser (VRP)\pmAnalyzing and Parsing RDF. Heraklion, April."@en
Date Issued dp "2000"^^date

publWhenowlni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publWhenowl

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Halpin, H., Hayes, P. J., McCusker, J. P., McGuinness, D. L., & Thompson, H. S. (2010). When owl: sameas isn’t the same: An analysis of identity in linked data. International Semantic Web Conference, 305–320."@en
Date Issued dp "2010"^^date

publWhEvlidahewiaquthclupDBni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#publWhEvlidahewiaquthclupDB

belongs to
Bibliographic Resource c
has facts
Bibliographic Citation dp "Waitelonis, J., Ludwig, N., Knuth, M., & Sack, H. (2011). Whoknows? evaluating linked data heuristics with a quiz that cleans up dbpedia. Interactive Technology and Smart Education."@en
Date Issued dp "2011"^^date

tecCrowdsourcing-basedni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#tecCrowdsourcing-based

Crowdsourcing-based techniques incorporate human interaction into the knowledge-cleaning process. A crowd can be engaged to detect and correct errors, leveraging either (1) domain experts or (2) anonymous contributors. Domain experts are capable of setting up and maintaining the cleaning process as well as handling complex, domain-specific issues. Anonymous crowds, on the other hand, excel at completing simpler, more generalized tasks that typically involve common knowledge and do not require specialized expertise. The technique allows to target all types of assertions, i.e., instance-, property-value -, and equality assertions.
belongs to
Technique c
has facts
usesBackgroundKnowledge op extSrcDomaInExpertise ni

tecEmbeddingAndNeuralNetwork-basedni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#tecEmbeddingAndNeuralNetwork-based

Embedding- and neural network-based techniques use supervised machine-learning, involving a training phase and an inference phase. In the training phase, a curated dataset is used to train a model for a specific cleaning task, i.e., error detection or correction of a specific type of error. During inference, the trained model is applied to unseen data to perform the trained task. Neural networks that process knowledge graphs embed the graph into a lower-dimensional vector space, also known as knowledge graph embeddings. This embedding process is based on selected features—for example, a node’s incoming and outgoing relationships as in TransE. The technique allows to target any type of assertion, i.e., instance -, property-value -, and equality assertion.
belongs to
Technique c
has facts
usesBackgroundKnowledge op extSrcExternalSources ni
usesBackgroundKnowledge op kGGraphStructure ni

tecHybridni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#tecHybrid

Hybrid techniques combine multiple other methods to detect and correct errors in any type of assertion— instance, property-value, or equality. They merge outputs from independent processes into a single evaluation, enhancing accuracy and increasing complexity due to the need to maintain and integrate multiple techniques.
belongs to
Technique c
has facts
usesBackgroundKnowledge op extSrcDomaInExpertise ni
usesBackgroundKnowledge op extSrcExternalSources ni
usesBackgroundKnowledge op extSrcFormalAndSyntaxSpecifications ni
usesBackgroundKnowledge op kGContextualGraph ni
usesBackgroundKnowledge op kGGraphStructure ni
usesBackgroundKnowledge op kGValuesOfPV ni

tecIntegrityConstraint-basedni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#tecIntegrityConstraint-based

Integrity constraint-based techniques use constraints to verify the correctness of assertions, i.e., detect errors. Integrity constraints can be provided, for example, via SHACL shapes, or generated from the ontology. They define a desired graph structure and the detection process verifies that the assertions conform to this structure. This technique allows to target instance - and property-value assertions.
belongs to
Technique c
has facts
usesBackgroundKnowledge op extSrcFormalAndSyntaxSpecifications ni

tecOntology-basedni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#tecOntology-based

Backward compatible with:
Ontology-based techniques use an ontology that describes assertions in order to detect and correct errors. Error detection typically involves reasoning over the ontology and the associated assertions. To identify inconsistencies, the ontology must include &amp;apos;negative inclusions&amp;apos; (disjoint axioms) that specify which entities or classes cannot co-occur—for example, the classes Human and Boat are usually mutually exclusive, so no individual should be assigned both types. When correcting errors in equality assertions, new or more appropriate relations may be introduced into the ontology if two instances are not semantically identical. This technique allows to target any type of assertion, i.e., instance -, property-value -, and equality assertion.
belongs to
Technique c
has facts
has related op tecIntegrityConstraint-based ni
usesBackgroundKnowledge op extSrcFormalAndSyntaxSpecifications ni
backward Compatible With ap "Ontology-based techniques use an ontology that describes assertions in order to detect and correct errors. Error detection typically involves reasoning over the ontology and the associated assertions. To identify inconsistencies, the ontology must include &amp;apos;negative inclusions&amp;apos; (disjoint axioms) that specify which entities or classes cannot co-occur—for example, the classes Human and Boat are usually mutually exclusive, so no individual should be assigned both types. When correcting errors in equality assertions, new or more appropriate relations may be introduced into the ontology if two instances are not semantically identical. This technique allows to target any type of assertion, i.e., instance -, property-value -, and equality assertion."@en

tecPath-basedni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#tecPath-based

Path-based techniques use a knowledge graph&amp;apos;s structure or semantic features to detect errors, focusing on both property-value and equality assertions. To validate property-value assertions, these techniques identify alternative paths between the relation’s two nodes, considering measures such as path length and path count. The paths can also be constrained by semantic attributes or direction. To validate equality assertions, path-based techniques construct contextual subgraphs around the involved instances—based, for example, on distance or on semantic properties such as transitivity. These subgraphs are then compared against each other to determine whether the two instances truly represent the same object of discourse.
belongs to
Technique c
has facts
has related op tecEmbeddingAndNeuralNetwork-based ni
usesBackgroundKnowledge op kGContextualGraph ni
usesBackgroundKnowledge op kGGraphStructure ni

tecRuleMining-basedni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#tecRuleMining-based

Rule mining-based techniques can detect and correct errors in both property-value and instance assertions by identifying recurring patterns in the knowledge graph’s structure. These patterns serve as the contextual ‘rules’ for detecting or correcting errors. For example, it may be typical for a Human entity to have a social security number and a date of birth; these attributes can then be used as a rule for a common graph structure around humans. In this case, counterexamples are humans that do not conform to this common structure. These examples could indicate errors or exceptions to the rule.
belongs to
Technique c
has facts
has related op tecOntology-based ni
usesBackgroundKnowledge op kGGraphStructure ni

tecStatisticalni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#tecStatistical

Statistical techniques address both instance and property-value assertions by leveraging a statistical context derived from the knowledge graph. This context may include other property-value assertions or the incoming and outgoing relations of a node. By comparing observed data to expected statistical patterns, outliers—unexpected deviations from the context—can be identified and flagged as potential errors.
belongs to
Technique c

tecSyntacticni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#tecSyntactic

Syntactic techniques take advantage of syntax specifications to parse knowledge graph serializations. Serializations are &amp;quot;downloaded&amp;quot; versions of a knowledge graph, i.e., a Resource Description Framework graph. Typical formats are Turtle or OWL/RDF. The syntax specifications define expected symbols and their arrangement. A parser loads a specification and then parses an input, for example, a Turtle file. It reports violations, i.e., detects syntactic errors; sometimes, a correction is attempted by updating a specific syntax part.
belongs to
Technique c
has facts
usesBackgroundKnowledge op extSrcFormalAndSyntaxSpecifications ni

tecVerbalization-basedni back to ToC or Named Individual ToC

IRI: https://purl.archive.org/akco#tecVerbalization-based

Verbalization-based techniques are based on external natural language sources, such as the Web or documents. They validate property-value assertions by translating them, i.e., verbalizing, into a natural language claim. This process can, for example, use templates or machine learning. The claim is then used to search a given external source. The result is natural language evidence, which is then analyzed, using typically crowdsourcing or machine learning. This technique includes using any large-language model, like ChatGPT.
belongs to
Technique c
has facts
usesBackgroundKnowledge op extSrcAny ni

terms:ni back to ToC or Named Individual ToC

IRI: http://purl.org/dc/terms/

has facts
title dp "DCMI Metadata Terms - other"@en

Legend back to ToC

c: Classes
op: Object Properties
dp: Data Properties
ni: Named Individuals

References back to ToC

[Chávez-Feria, et al., 2021] Chávez-Feria, S., García-Castro, R., & Poveda-Villalón, M. (2021). Converting UML-based ontology conceptualizations to OWL with Chowlk. In European Semantic Web Conference (pp. 44–48). Springer.

[BERT] Devlin, J., et al. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

[TransE] Bordes, A., et al. (2013). Translating Embeddings for Modeling Multi-relational Data.

[Paulheim, 2017] Heiko Paulheim. Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic web, 8(3):489–508, 2017.

[Fensel et al., 2020] Dieter Fensel, Umutcan Şimşek, Kevin Angele, Elwin Huaman, Elias Kärle, Oleksandra Panasiuk, Ioan Toma, Jürgen Umbrich, and Alexander Wahler. Knowledge Graphs: Methodology, Tools and Selected Use Cases. Springer Nature, 2020.

[Acosta, et al., 2013] Maribel Acosta, Amrapali Zaveri, Elena Simperl, Dimitris Kontokostas, Sören Auer, and Jens Lehmann. Crowdsourcing linked data quality assessment. In International semantic web conference, pages 260–276. Springer, 2013.

Acknowledgments back to ToC

The authors would like to thank Silvio Peroni for developing LODE, a Live OWL Documentation Environment, which is used for representing the Cross Referencing Section of this document and Daniel Garijo for developing Widoco, the program used to create the template used in this documentation.