CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
WordNet: a lexical database for English
Communications of the ACM
Ontology Learning for the Semantic Web
IEEE Intelligent Systems
Ontology Learning and Its Application to Automated Terminology Translation
IEEE Intelligent Systems
Ontology Construction for Information Selection
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Learning concept hierarchies from text corpora using formal concept analysis
Journal of Artificial Intelligence Research
A cooperative approach for composite ontology mapping
Journal on data semantics X
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Ontology enrichment is required when the knowledge captured by the ontology is out of date or unable to capture the specified user requirements in a specific domain. In this paper we present an automatic statistical/semantic framework for enriching general-purpose ontologies from the World Wide Web (WWW). Using the massive amount of information encoded in texts on the web as a corpus, missing background knowledge such as concepts, instances and relations can be discovered and exploited to enrich general-purpose ontologies. The benefits of our approach are: (i) enabling ontology enrichment with missing background knowledge, and thus, enabling the reuse of such knowledge in future. (ii) saving time and effort required to manually enrich and update the ontologies. Experimental results indicate that the techniques used to enrich ontologies are both effective and efficient.