We describe techniques for the automatic detection of relationships among domain entities (e.g. genes, proteins, diseases) mentioned in the biomedical literature. Our approach is based on the adaptive selection of candidate interactions sentences, which are then parsed using our own dependency parser. Specific syntax-based filters are used to limit the number of possible candidate interacting pairs. The approach has been implemented as a demonstrator over a corpus of 2000 richly annotated MedLine abstracts, and later tested by participation to a text mining competition. In both cases, the results obtained have proved the adequacy of the proposed approach to the task of interaction detection.

One of the major obstacles for knowledge management remains MultiWord Terminology (MWT). This paper explores the difficulties that arise and describes real world solutions implemented as part of the Parmenides project. Parmenides is being built as an integrated knowledge management package that combines information, MWT and ontology extraction methods in a semi-automated framework. The focus of this paper is on eliciting ontological fragments based on dedicated MWT processing.