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Learning Ontology Relations By Combining Corpus Based Techniques And Reasoning On Data From Semantic Web Sources by Gerhard Wohlgenannt
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1Learning Ontology Relations By Combining Corpus-Based Techniques And Reasoning On Data From Semantic Web Sources
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.
“Learning Ontology Relations By Combining Corpus-Based Techniques And Reasoning On Data From Semantic Web Sources” Metadata:
- Title: ➤ Learning Ontology Relations By Combining Corpus-Based Techniques And Reasoning On Data From Semantic Web Sources
- Language: English
“Learning Ontology Relations By Combining Corpus-Based Techniques And Reasoning On Data From Semantic Web Sources” Subjects and Themes:
- Subjects: ➤ Based - Combining - Corpus - Data - from - Learning - machine learning - natural language learning - Ontology - Reasoning - relation labeling - Relations - Semantic - Sources - Techniques - Wohlgenannt - book
Edition Identifiers:
- Internet Archive ID: oapen-20.500.12657-26873
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The book is available for download in "texts" format, the size of the file-s is: 239.24 Mbs, the file-s for this book were downloaded 15 times, the file-s went public at Sat Jun 01 2024.
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