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DBpedia ontology enrichment for inconsistency detection

Gerald Töpper, Magnus Knuth, Harald Sack
2012 Proceedings of the 8th International Conference on Semantic Systems - I-SEMANTICS '12  
In order to enable the detection of inconsistencies this work focuses on the enrichment of the DBpedia ontology by statistical methods.  ...  Taken the enriched ontology as a basis the process of the extraction of Wikipedia data is adapted, in a way that inconsistencies are detected during the extraction.  ...  However, in this work the detection of inconsistencies within DBpedia is highlighted, which is achieved by automatic semantic enrichment of the underlying ontology.  ... 
doi:10.1145/2362499.2362505 dblp:conf/i-semantics/TopperKS12 fatcat:6ikoza5vbjftbba23e7qolp3ce

ORE - A Tool for Repairing and Enriching Knowledge Bases [chapter]

Jens Lehmann, Lorenz Bühmann
2010 Lecture Notes in Computer Science  
In this article, we present ORE, a tool for repairing and enriching OWL ontologies.  ...  ORE supports the detection of a variety of ontology modelling problems and guides the user through the process of resolving them.  ...  In [28] and [8] , methods for the detection and repair of inconsistencies in frequently changing ontologies were developed.  ... 
doi:10.1007/978-3-642-17749-1_12 fatcat:u4ch5bi4efechd3htxugxggcim

Detecting fake news for the new coronavirus by reasoning on the Covid-19 ontology [article]

Adrian Groza
2020 arXiv   pre-print
I investigate here how reasoning in Description Logics (DLs) can detect inconsistencies between trusted medical sources and not trusted ones.  ...  Racer reasoner is used to detect inconsitencies in the enriched ontology, based on some patterns manually formalised in Description Logics or SWRL Only.  ...  Translating the fact f i in DL using Fred F Checking the coherence and consistency of the merged ontology M F Fred i j 5. If conflict is detected, Verbalise explanations for the inconsistency 6.  ... 
arXiv:2004.12330v1 fatcat:ctt45of3pvbozbzchgwvh6rnhu

PatchR

Magnus Knuth, Harald Sack
2015 International Journal on Semantic Web and Information Systems (IJSWIS)  
Based on the PatchR ontology a framework is suggested that allows users to efficiently report and data publishers to handle change requests for their datasets.  ...  Indeed, we see Linked Data consumers equally responsible for the quality of the datasets they use. PatchR provides a vocabulary to report incorrect data and to propose changes to correct them.  ...  Based on an ontology enriched with assertions deduced from statistical evaluation of DBpedia entities (Töpper, have been able to identify inconsistent facts in DBpedia.  ... 
doi:10.4018/ijswis.2015010102 fatcat:uifvmn2v2vaizgfogbe2gu6634

Data Cleansing Consolidation with PatchR [chapter]

Magnus Knuth, Harald Sack
2014 Lecture Notes in Computer Science  
Likewise, Inconsistency Checker [4] detects logical inconsistencies in DBpedia using a reasoner based on an enriched ontology model with strict type constraints.  ...  Crowdsourcing is a valuable (and high quality) mean to detect inconsistency of data to the real world. TripleCheckMate [5] have collected erroneous triples in DBpedia.  ... 
doi:10.1007/978-3-319-11955-7_25 fatcat:pae3ctdsvbdm3hg2oyj4mi4e6i

Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling [chapter]

Jorge Gracia, Jochem Liem, Esther Lozano, Oscar Corcho, Michal Trna, Asunción Gómez-Pérez, Bert Bredeweg
2010 Lecture Notes in Computer Science  
These models are typically developed in isolation, unrelated to other user models, thus losing the opportunity of incorporating knowledge from other existing models or ontologies that might enrich the  ...  reasoning), to smoothly interconnect conceptual models created by different users, thus facilitating the global sharing of scientific data contained in such models and creating new learning opportunities for  ...  We thank Thanh Tu Nguyen for her valuable assistance in the implementation of the experiments.  ... 
doi:10.1007/978-3-642-17749-1_6 fatcat:w3ur3sg2gffu7lwgypqybta5o4

Knowledge Base Creation, Enrichment and Repair [chapter]

Sebastian Hellmann, Volha Bryl, Lorenz Bühmann, Milan Dojchinovski, Dimitris Kontokostas, Jens Lehmann, Uroš Milošević, Petar Petrovski, Vojtěch Svátek, Mladen Stanojević, Ondřej Zamazal
2014 Lecture Notes in Computer Science  
This chapter focuses on data transformation to RDF and Linked Data and furthermore on the improvement of existing or extracted data especially with respect to schema enrichment and ontology repair.  ...  However, schema information is needed for consistency checking and finding modelling problems.  ...  the ontology and solve the detected naming issues.  ... 
doi:10.1007/978-3-319-09846-3_3 fatcat:fuymcvwpibbvbkaooq33opmga4

Collaboratively Patching Linked Data [article]

Magnus Knuth, Johannes Hercher, Harald Sack
2012 arXiv   pre-print
The feasibility of our approach is demonstrated by example of a collaborative game that patches statements in DBpedia data and provides notifications for relevant changes.  ...  Since erroneous data often is duplicated and (re-)distributed by mashup applications it is not only the responsibility of a few original publishers to keep their data tidy, but progresses to be a mission for  ...  Furthermore, inconsistencies for single entities of the DBpedia dataset can be displayed. Patching DBpedia can be regarded as a special case, since this data is based on editable Wikipedia pages.  ... 
arXiv:1204.2715v1 fatcat:dv2getm3knhcngwvp5rwki5rwq

Universal OWL Axiom Enrichment for Large Knowledge Bases [chapter]

Lorenz Bühmann, Jens Lehmann
2012 Lecture Notes in Computer Science  
This allows to semiautomatically create schemata, which we evaluate and discuss using DBpedia.  ...  In this article, we present a light-weight method to enrich knowledge bases accessible via SPARQL endpoints with almost all types of OWL 2 axioms.  ...  Clearly, in those cases data errors cause problems and our enrichment tool can be used to detect those.  ... 
doi:10.1007/978-3-642-33876-2_8 fatcat:7eup7i35d5cqhh2eepfsf6s6qm

Enriching Wikidata with Linked Open Data [article]

Bohui Zhang, Filip Ilievski, Pedro Szekely
2022 arXiv   pre-print
We evaluate our enrichment method with two complementary LOD sources: a noisy source with broad coverage, DBpedia, and a manually curated source with narrow focus on the art domain, Getty.  ...  We present a novel workflow that includes gap detection, source selection, schema alignment, and semantic validation.  ...  Ontology enrichment deals with noise, incompleteness, and inconsistencies of ontologies, by discovering association rules [8] , or by extracting information from WWW documents [13] .  ... 
arXiv:2207.00143v1 fatcat:isf3jv3xs5e3hoc7n4ztoobhzy

A Comparison of Word Embeddings and N-gram Models for DBpedia Type and Invalid Entity Detection

Hanqing Zhou, Amal Zouaq, Diana Inkpen
2018 Information  
We tackle two challenges: (a) the detection of entity types, which can be used to detect invalid DBpedia types and assign DBpedia types for type-less entities; and (b) the detection of invalid entities  ...  This article presents and evaluates a method for the detection of DBpedia types and entities that can be used for knowledge base completion and maintenance.  ...  Acknowledgments: We thank the Natural Sciences and Engineering Research Council of Canada (NSERC) for the financial support. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/info10010006 fatcat:ezgwswjckzd27br2yf373d42ba

Detecting hidden errors in an ontology using contextual knowledge

Mehdi Teymourlouie, Ahmad Zaeri, Mohammadali Nematbakhsh, Matthias Thimm, Steffen Staab
2018 Expert systems with applications  
While current ontology debugging methods can detect logical errors (incoherences and inconsistencies), they are incapable of detecting hidden modeling errors in coherent and consistent ontologies.  ...  Our experiments show that adding general ontologies like DBpedia as contextual knowledge in the ontology debugging process results in detecting contradictions in ontologies that are coherent.  ...  Since the dataset enriched the DBpedia ontology, it has the same signature as DBpedia ontology. Therefore, there is no need to use alignment between them.  ... 
doi:10.1016/j.eswa.2017.11.034 fatcat:cipreaijxfazxnasw3v5zvn73q

WhoKnows? Evaluating linked data heuristics with a quiz that cleans up DBpedia

Jörg Waitelonis, Nadine Ludwig, Magnus Knuth, Harald Sack, Markus Ketterl
2011 Interactive Technology and Smart Education  
has been developed for the evaluation of property relevance ranking heuristics on DBpedia data with the convenient side effect of detecting inconsistencies and doubtful facts.  ...  Data cleansing approaches enable to detect inconsistencies and to overhaul affected data sets, but they are difficult to apply automatically.  ...  Acknowledgement We would like to thank our students Emilia Wittmers, Eyk Kny, Sebastian Kölle, and Gerald Töpper for their outstanding contribution for WhoKnows?  ... 
doi:10.1108/17415651111189478 fatcat:jlexxgxkvvd7tgqyus762orh3q

A Novel Vision for Navigation and Enrichment in Cultural Heritage Collections [chapter]

Joffrey Decourselle, Audun Vennesland, Trond Aalberg, Fabien Duchateau, Nicolas Lumineau
2015 Communications in Computer and Information Science  
Later, an automatic enrichment is proposed, but limited to four properties, for instance places (with links to Geonames) or agents (with links to DBPedia).  ...  detected.  ... 
doi:10.1007/978-3-319-23201-0_49 fatcat:5vph37jtpfgcfkkmf2byni2dum

Fast Approximate A-Box Consistency Checking Using Machine Learning [chapter]

Heiko Paulheim, Heiner Stuckenschmidt
2016 Lecture Notes in Computer Science  
For example, this allows for validating 293M Microdata documents against the schema.org ontology in less than 90 minutes, compared to 18 days required by a state of the art ontology reasoner.  ...  ., A-box consistency checking, by training a machine learning model which approximates the behavior of that reasoner for a specific ontology.  ...  Acknowledgements The authors would like to thank Aldo Gangemi for providing the DOLCE mappings for DBpedia and YAGO, and Robert Meusel for his assistance in providing suitable samples from the WebDataCommons  ... 
doi:10.1007/978-3-319-34129-3_9 fatcat:7ae3cx556rhw3aorpgujijvmdy
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