Extraction of Genic Interactions with the Recursive Logical Theory of an Ontology

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 han...

• Type: Conference
• Year: 2010
• Where: CICLING
• Authors: Alain-Pierre Manine, Érick Alphonse, Philippe Bessières
• Keywords: CICLING 2010, Logical Theory, Natural Language Processing, Ontology, Recursive Logical Theory