Information Extraction as an Ontology Population Task and Its Application to Genic Interactions

Ontologies are a well-motivated formal representation to model knowledge needed to extract and encode data from text. Yet, their tight integration with Information Extraction (IE) systems is still a research issue, a fortiori with complex ones that go beyond hierarchies. In this paper, we introduce an original architecture where IE is specified by designing an ontology, and the extraction process is seen as an Ontology Population (OP) task. Concepts and relations of the ontology define a normalized text representation. As their abstraction level is irrelevant for text extraction, we introduced a Lexical Layer (LL) along with the ontology, i.e. relations and classes at an intermediate level of normalization between raw text and concepts. On the contrary to previous IE systems, the extraction process only involves normalizing the outputs of Natural Language Processing (NLP) modules with instances of the ontology and the LL. All the remaining reasoning is left to a query module, which ...

• Type: Conference
• Year: 2008
• Where: ICTAI
• Authors: Alain-Pierre Manine, Érick Alphonse, Philippe Bessières
• Keywords: Artificial Intelligence, Extraction Process, ICTAI 2008, Inference Rules, Ontology