Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
Learning non-taxonomic relationships from web documents for domain ontology construction
Data & Knowledge Engineering
Learning to link with wikipedia
Proceedings of the 17th ACM conference on Information and knowledge management
Unsupervised Learning of Semantic Relations for Molecular Biology Ontologies
Proceedings of the 2008 conference on Ontology Learning and Population: Bridging the Gap between Text and Knowledge
Automatically Harvesting and Ontologizing Semantic Relations
Proceedings of the 2008 conference on Ontology Learning and Population: Bridging the Gap between Text and Knowledge
Ontology Learning and Population: Bridging the Gap between Text and Knowledge - Volume 167 Frontiers in Artificial Intelligence and Applications
Automated construction of domain ontology taxonomies from wikipedia
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
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Automated construction of ontologies from text corpora, which saves both time and human effort, is a principal condition for realizing the idea of the Semantic Web. However, the recently proposed automated techniques are still limited in the scope of context that can be captured. Moreover, the source corpora generally lack the consensus of ontology users regarding the understanding and interpretation of ontology concepts. In this paper we introduce an unsupervised method for learning domain n-ary relations from Wikipedia articles, thus harvesting the consensus reached by the largest world community engaged in collecting and classifying knowledge. Providing ontologies with n-ary relations instead of the standard binary relations built on the subject-verb-object paradigm results in preserving the initial context of time, space, cause, reason or quantity that otherwise would be lost irreversibly. Our preliminary experiments with a prototype software tool show highly satisfactory results when extracting ternary and quaternary relations, as well as the traditional binary ones.