Learning ontological rules to extract multiple relations of genic interactions from text.

Abstract:

INTRODUCTION:Information extraction (IE) systems have been proposed in recent years to extract genic interactions from bibliographical resources. They are limited to single interaction relations, and have to face a trade-off between recall and precision, by focusing either on specific interactions (for precision), or general and unspecified interactions of biological entities (for recall). Yet, biologists need to process more complex data from literature, in order to study biological pathways. An ontology is an adequate formal representation to model this sophisticated knowledge. However, the tight integration of IE systems and ontologies is still a current research issue, a fortiori with complex ones that go beyond hierarchies. METHOD:We propose a rich modeling of genic interactions with an ontology, and show how it can be used within an IE system. The ontology is seen as a language specifying a normalized representation of text. First, IE is performed by extracting instances from natural language processing (NLP) modules. Then, deductive inferences on the ontology language are completed, and new instances are derived from previously extracted ones. Inference rules are learnt with an inductive logic programming (ILP) algorithm, using the ontology as the hypothesis language, and its instantiation on an annotated corpus as the example language. Learning is set in a multi-class setting to deal with the multiple ontological relations. RESULTS:We validated our approach on an annotated corpus of gene transcription regulations in the Bacillus subtilis bacterium. We reach a global recall of 89.3% and a precision of 89.6%, with high scores for the ten semantic relations defined in the ontology.

journal_name

Int J Med Inform

authors

Manine AP,Alphonse E,Bessières P

doi

10.1016/j.ijmedinf.2009.03.005

subject

Has Abstract

pub_date

2009-12-01 00:00:00

pages

e31-8

issue

12

eissn

1386-5056

issn

1872-8243

pii

S1386-5056(09)00042-2

journal_volume

78

pub_type

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