The description logic has recently proved practically useful in the life science domain with presence of several large-scale biomedical ontologies such as Snomed ct. To deal with ontologies of this scale, standard reasoning of classification is essential but not sufficient. The ability to extract relevant fragments from a large ontology and to incrementally classify it has become more crucial to support ontology design, maintenance and re-use. In this paper, we propose a pragmatic approach to module extraction and incremental classification for ontologies and report on empirical evaluations of our algorithms which have been implemented as an extension of the CEL reasoner. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Suntisrivaraporn, B. (2008). Module extraction and incremental classification: A pragmatic approach for ε ℒ+ ontologies. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5021 LNCS, pp. 230–244). https://doi.org/10.1007/978-3-540-68234-9_19
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