Extraction of genic interactions with the recursive logical theory of an ontology
2010
Manine , Alain Pierre (INRA , Jouy-En-Josas (France). UR 1077 Mathématique, Informatique et Génome) | Bessières , Philippe (INRA , Jouy-En-Josas (France). UR 1077 Mathématique, Informatique et Génome) | Alphonse , Erick (Université Paris 13, Villetaneuse(France). LIPN-CNRS, UMR7030)
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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