Ontology Learning for the Semantic Web
Ontology Learning for the Semantic Web
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Ontological Engineering
Ontology Learning from Text Using Relational Concept Analysis
MCETECH '08 Proceedings of the 2008 International MCETECH Conference on e-Technologies
Learning concept hierarchies from text corpora using formal concept analysis
Journal of Artificial Intelligence Research
FCA-MERGE: bottom-up merging of ontologies
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
A proposal for combining formal concept analysis and description logics for mining relational data
ICFCA'07 Proceedings of the 5th international conference on Formal concept analysis
What's happening in semantic web: and what FCA could have to do with it
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
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Building a domain ontology usually requires several resources of different types, e.g. thesaurus, object taxonomies, terminologies, data-bases, sets of documents, etc. where objects are described in terms of attributes and relations with other objects. One important and hard problem is to be able to combine and merge knowledge units extracted from these different resources within the representation formalism supporting the ontology. The purpose of this paper is to show which kinds of resources can be taken as starting points for building an ontology, using FCA and its extension RCA. A real-world example in microbiology is proposed, detailing the interaction with domain experts during the ontology design process. Finally, an evaluation based on recall and precision gives an idea of the efficiency of the approach and points out several research perspectives.