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

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Tác giả: Gerhard Wohlgenannt

Ngôn ngữ: eng

ISBN-13: 978-3631753842

Ký hiệu phân loại:

Thông tin xuất bản: Bern : Peter Lang International Academic Publishing Group, 2018

Mô tả vật lý: 1 electronic resource (222 p.)

Bộ sưu tập: Tài liệu truy cập mở

ID: 248463

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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