1003170.pdf
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 relation...
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Peter Lang International Academic Publishers
2019
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oapen-20.500.12657-268732022-04-26T12:36:42Z Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources Wohlgenannt, Gerhard Based Combining Corpus Data from Learning machine learning natural language learning Ontology Reasoning relation labeling Relations Semantic Sources Techniques Wohlgenannt bic Book Industry Communication::U Computing & information technology::UB Information technology: general issues::UBJ Ethical & social aspects of IT bic Book Industry Communication::U Computing & information technology::UF Business applications::UFL Enterprise software 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. 2019-01-10 23:55 2018-12-01 23:55:55 2020-01-14 16:18:01 2020-04-01T11:28:34Z 2020-04-01T11:28:34Z 2018 book 1003170 OCN: 1082971313 9783631753842 http://library.oapen.org/handle/20.500.12657/26873 eng Forschungsergebnisse der Wirtschaftsuniversitaet Wien application/pdf n/a 1003170.pdf Peter Lang International Academic Publishers 10.3726/b13903 10.3726/b13903 e927e604-2954-4bf6-826b-d5ecb47c6555 9783631753842 44 222 Bern open access |
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English |
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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 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. |
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Peter Lang International Academic Publishers |
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2019 |
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