Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources

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The manual construction of formal domain conceptualizations (ontologies) is labor-intensive. Ontology learning, by contrast, provides (semi- )automatic ontology generation from input data such as domain text. This thesis proposes a novel approach for learning labels of non-taxonomic ontology relatio...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Wohlgenannt, Gerhard (Autor)
Formato: Tesis Electrónico eBook
Idioma:Inglés
Publicado: Frankfurt a.M. Peter Lang GmbH, Internationaler Verlag der Wissenschaften [2018], ©2011.
Edición:1st, New ed.
Colección:Free online access: JSTOR.
Temas:
Conceptual structures (Information theory)
Ontologies (Information retrieval)
Expert systems (Computer science)
Semantic Web.
Structures conceptuelles.
Ontologies (Recherche de l'information)
Systèmes experts (Informatique)
Web sémantique.
Semantic Web
dissertations.
Academic theses
Academic theses.
Thèses et écrits académiques.
Acceso en línea:Texto completo
Descripción
Sumario:The manual construction of formal domain conceptualizations (ontologies) is labor-intensive. Ontology learning, by contrast, provides (semi- )automatic ontology generation from input data such as domain text. This thesis proposes a novel approach for learning labels of non-taxonomic ontology relations. It combines corpus-based techniques with reasoning on Semantic Web data. Corpus-based methods apply vector space similarity of verbs co-occurring with labeled and unlabeled relations to calculate relation label suggestions from a set of candidates. A meta ontology in combination with Semantic Web sources such as DBpedia and OpenCyc allows reasoning to improve the suggested labels. An extensive formal evaluation demonstrates the superior accuracy of the presented hybrid approach.
Descripción Física:1 online resource.
ISBN:9783631753842
3631753845

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