Automatic Ontology Identification for Reuse

Patel, Yash; Darlington, John

doi:10.1109/wi.2007.79

Cited by 4 publications

(2 citation statements)

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“…Certain candidate words might be associated with more than one concept-word. To avoid introducing ambiguity in the ontology, we pair each candidate word with the associated concept-word with the highest tf * iof weight [1], where iof is a variant of idf that is calculated across our collection of 183 ontologies and increases the weights of tokens that are unique to a particular ontology.…”

Section: Common Hypernyms Approachmentioning

confidence: 99%

“…Furthermore, the lack of automatic tools makes these tasks particularly arduous to non-expert users. In a previous study we investigated [1] the scenario in which the user provides a sample documents related to a topic for which an ontological representation is desired. Using these documents, our approach attempts to select the best-matching domain ontology from a large public library.…”

Section: Introductionmentioning

confidence: 99%

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Using Text Mining to Enrich the Vocabulary of Domain Ontologies

Speretta

Gauch

2008

2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

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Users organize personal information in various ways. We believe that this process could be expedited and improved by using domain ontologies. The main problem with this idea is the lack of automatic tools that help nonexpert users to build and maintain their own ontologies. In this study we report progress in the process of adapting ontologies to better represent a given set of documents centered on a topic of interest. More specifically we investigate automatic approaches to enhance the representation of the concepts within the domain ontology. We show that our approach can enrich the vocabulary of each concept with words mined from the set of small documents provided. The method we propose is based on efficient text mining approaches combined with semantic information from WordNet.

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Section: Common Hypernyms Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%