Inductive Learning of Disjointness Axioms

Fleischhacker, Daniel; Völker, Johanna

doi:10.1007/978-3-642-25106-1_20

Cited by 33 publications

(30 citation statements)

References 20 publications

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“…Given the amount of linked data available, it is pretty clear that the use of the full expressive power of OWL is not a realistic option. In previous work, we have therefore focussed on inductive methods for creating schema information in the EL fragment of OWL 2 [7,26]. This fragment, while having nice computational properties, has a serious drawback with respect to describing linked data in the large as it has rather limited support for exploiting property characteristics.…”

Section: Motivationmentioning

confidence: 98%

Mining RDF Data for Property Axioms

Fleischhacker

Völker

Stuckenschmidt

2012

On the Move to Meaningful Internet Systems: OTM 2012

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Abstract. The Linked Data cloud grows rapidly as more and more knowledge bases become available as Linked Data. Knowledge-based applications have to rely on efficient implementations of query languages like SPARQL, in order to access the information which is contained in large datasets such as DBpedia, Freebase or one of the many domain-specific RDF repositories. However, the retrieval of specific facts from an RDF dataset is often hindered by the lack of schema knowledge, that would allow for query-time inference or the materialization of implicit facts. For example, if an RDF graph contains information about films and actors, but only Titanic starring Leonardo DiCaprio is stated explicitly, a query for all movies Leonardo DiCaprio acted in might not yield the expected answer. Only if the two properties starring and actedIn are declared inverse by a suitable schema, the missing link between the RDF entites can be derived. In this work, we present an approach to enriching the schema of any RDF dataset with property axioms by means of statistical schema induction. The scalability of our implementation, which is based on association rule mining, as well as the quality of the automatically acquired property axioms are demonstrated by an evaluation on DBpedia.

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Section: Motivationmentioning

confidence: 98%

Mining RDF Data for Property Axioms

Fleischhacker

Völker

Stuckenschmidt

2012

On the Move to Meaningful Internet Systems: OTM 2012

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“…The ontology used in this experiment is learned automatically by analyzing statistical schema induction on DBpedia instances (see Fleischhacker et al (2012); Völker & Niepert (2011)) and generated by GoldMiner (Fleischhacker & Völker, 2011) tool. The learned axioms mostly include Disjointness axioms between concepts of DBpedia ontology and have ALCH expressiveness.…”

Section: Case 1: Debugging Automatically Learned Disjointness Ontologymentioning

confidence: 99%

Detecting hidden errors in an ontology using contextual knowledge

Teymourlouie¹,

Zaeri²,

Nematbakhsh³

et al. 2018

Expert Systems with Applications

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Due to modeling errors in designing ontologies, an ontology may carry incorrect information. Ontology debugging can be helpful in detecting errors in ontologies that are increasing in size and expressiveness day by day. 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. From the logical perspective, there are no errors in such ontologies, but this study shows some modeling errors may not break the coherency of the ontology by not participating in any contradiction. In this paper, contextual knowledge is exploited to detect such hidden errors. 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.

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“…Besides inducing domain and range restrictions of properties, in their work further axioms are induced such as subsumption axioms (e.g., (ex:A, rdfs:subClassOf, ex:B)) and transitivity axioms (e.g., (ex:P, rdf:type, owl:TransitiveProperty)). This work was subsequently extended in [4] and [5] with further types of axioms. The main difference to this work is that we induce independent domain and range restrictions as well as coupled domain/range restrictions whereas in these works only independent domain and range restrictions are induced.…”

Section: Related Workmentioning

confidence: 99%

Statistical Induction of Coupled Domain/Range Restrictions from RDF Knowledge Bases

Ell

Hakimov

Cimiano

2017

Lecture Notes in Computer Science

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Abstract. Statistical Schema Induction can be applied on an RDF dataset to induce domain and range restrictions. We extend an existing approach that derives independent domain and range restrictions to derive coupled domain/range restrictions, which may be beneficial in the context of Natural Language Processing tasks such as Semantic Parsing and Entity Classification. We provide results from an experiment on the DBpedia graph. An evaluation shows that high precision can be achieved.

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Inductive Learning of Disjointness Axioms

Cited by 33 publications

References 20 publications

Mining RDF Data for Property Axioms

Mining RDF Data for Property Axioms

Detecting hidden errors in an ontology using contextual knowledge

Statistical Induction of Coupled Domain/Range Restrictions from RDF Knowledge Bases

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