Revision with unchanged content. In the last years, the Web has become an invaluable source of information for almost every domain of knowledge. This has motivated researchers to considering the Web as a valid repository for Information Retrieval and Knowledge Acquisition. In this sense, Ontology Learning from web resources is a particularly interesting topic as Ontologies are required to achieve interoperability and information understanding in knowledge intensive environments such as the Semantic Web. This book analyzes the ontology construction process from the automatic learning point of view. It presents a novel approach for Ontology Learning, introducing new methods for knowledge acquisition from the Web. The adaptation of several learning techniques to the web corpus and the exploitation of particular characteristics of the Web environment composing an automatic, unsupervised and domain independent approach distinguishes the present proposal from previous works. The discussion, methods and results presented in this work should help researchers interested in taking profit from the information available in the Web and to create a consistent base of ontologies required for the Semantic Web.
Cited By
- Liao X and Zhao Z (2019). Unsupervised Approaches for Textual Semantic Annotation, A Survey, ACM Computing Surveys, 52:4, (1-45), Online publication date: 31-Jul-2020.
- Ben Aouicha M and Hadj Taieb M (2016). Computing semantic similarity between biomedical concepts using new information content approach, Journal of Biomedical Informatics, 59:C, (258-275), Online publication date: 1-Feb-2016.
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