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Communication dans un congrès

Extraction of genic interactions with the recursive logical theory of an ontology

Abstract : We introduce an Information Extraction (IE) system which uses the logical theory of an ontology as a generalisation of the typical information extraction patterns to extract biological interactions from text. This provides inferences capabilities beyond current approaches: first, our system is able to handle multiple relations; second, it allows to handle dependencies between relations, by deriving new relations from the previously extracted ones, and using inference at a semantic level; third, it addresses recursive or mutually recursive rules. In this context, automatically acquiring the resources of an IE system becomes an ontology learning task: terms, synonyms, conceptual hierarchy, relational hierarchy, and the logical theory of the ontology have to be acquired. We focus on the last point, as learning the logical theory of an ontology, and a fortiori of a recursive one, remains a seldom studied problem. We validate our approach by using a relational learning algorithm, which handles recursion, to learn a recursive logical theory from a text corpus on the bacterium Bacillus subtilis. This theory achieves a good recall and precision for the ten defined semantic relations, reaching a global recall of 67.7% and a precision of 75.5%, but more importantly, it captures complex mutually recursive interactions which were implicitly encoded in the ontology.
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Communication dans un congrès
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Déposant : Migration Prodinra <>
Soumis le : jeudi 4 juin 2020 - 03:28:23
Dernière modification le : vendredi 12 juin 2020 - 10:43:26


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  • HAL Id : hal-02758320, version 1
  • PRODINRA : 40652
  • WOS : 000280123000047



Alain Pierre Manine, Philippe Bessières, Erick Alphonse. Extraction of genic interactions with the recursive logical theory of an ontology. 11. International conference on intelligent text processing and computational linguistics (CICLing'10), 2010, Iasi, Romania. ⟨hal-02758320⟩



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