Ontologies and Information Extraction

This report argues that, even in the simplest cases, IE is an ontology-driven process. It is not a mere text filtering method based on simple pattern matching and keywords, because the extracted pieces of texts are interpreted with respect to a predefined partial domain model. This report shows that depending on the nature and the depth of the interpretation to be done for extracting the information, more or less knowledge must be involved. This report is mainly illustrated in biology, a domain in which there are critical needs for content-based exploration of the scientific literature and which becomes a major application domain for IE

Learning Semantic-Level Information Extraction Rules by Type-Oriented ILP

This paper describes an approach to using semantic representations for learning information extraction (IE) rules by a type-oriented inductive logic programming (ILP) system. NLP components of a machine trauslation system are used to automatically generate semantic representations of text corpus that can be given directly to an ILP system. The latest experimental results show high precision and recall of the learned rules

Relation extraction for information extraction from free text

Ph.DDOCTOR OF PHILOSOPH