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

Inside this Book

If you make use of this material, you may credit the authors as follows:

Wohlgenannt Gerhard, "Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources", Peter Lang International Academic Publishing Group, 2018, DOI: 10.3726/b13903, License: https://creativecommons.org/licenses/by/4.0/legalcode

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.

Keywords

Based, Combining, Corpus, Data, From, Learning, Machine Learning, Natural Language Learning, Ontology, Reasoning, Relation Labeling, Relations, Semantic, Sources, Techniques, Wohlgenannt

Rights | License

Except where otherwise noted, this item has been published under the following license:

Takedown policy

If you believe that this publication infringes copyright, please contact us at info@jecasa-ltd.com and provide relevant details so that we can investigate your claim.