Using text processing techniques to automatically enrich a domain ontology
Proceedings of the international conference on Formal Ontology in Information Systems - Volume 2001
Ontology Learning for the Semantic Web
IEEE Intelligent Systems
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
The state of the art in ontology learning: a framework for comparison
The Knowledge Engineering Review
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
RelExt: a tool for relation extraction from text in ontology extension
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Using social media for ontology enrichment
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part II
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With the development of the Semantic Web technology, the use of ontologies to store and retrieve information covering several domains has increased. However, very few ontologies are able to cope with the ever-growing need of frequently updated semantic information or specific user requirements in specialized domains. As a result, a critical issue is related to the unavailability of relational information between concepts, also coined missing background knowledge. One solution to address this issue relies on the manual enrichment of ontologies by domain experts which is however a time consuming and costly process, hence the need for dynamic ontology enrichment. In this paper we present an automatic coupled statistical/semantic framework for dynamically enriching large-scale generic ontologies from the World Wide Web. Using the massive amount of information encoded in texts on the Web as a corpus, missing background knowledge can therefore be discovered through a combination of semantic relatedness measures and pattern acquisition techniques and subsequently exploited. The benefits of our approach are: (i) proposing the dynamic enrichment of large-scale generic ontologies with missing background knowledge, and thus, enabling the reuse of such knowledge, (ii) dealing with the issue of costly ontological manual enrichment by domain experts. Experimental results in a precision-based evaluation setting demonstrate the effectiveness of the proposed techniques.