Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources - Info and Reading Options
By Gerhard Wohlgenannt
"Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources" was published by Peter Lang International Academic Publishers in 2018 - Bern and it has 222 pages.
“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
- Author: Gerhard Wohlgenannt
- Number of Pages: 222
- Publisher: ➤ Peter Lang International Academic Publishers
- Publish Date: 2018
- Publish Location: Bern
“Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources” Subjects and Themes:
- Subjects: ➤ Conceptual structures (Information theory) - Expert systems (Computer science) - Semantic Web - Ontologies (Information retrieval) - Enterprise software - Ethical & social aspects of IT - World wide web - Information theory - Information retrieval - Expert systems (computer science)
Edition Specifications:
- Pagination: 1 electronic resource (222 p.)
Edition Identifiers:
- The Open Library ID: OL31370671M - OL16168695W
- ISBN-10: 13903
- All ISBNs: 13903
AI-generated Review of “Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources”:
"Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources" Description:
The Open Library:
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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