OBIRS – Ontology Based Information Retrieval System

To take advantages of knowledge models (ontologies) information retrieval systems may use the relationships between concepts to extend or reformulate queries. Our ontology based information retrieval system (OBIRS) relies on a domain ontology and on resources that are indexed using its concepts (e.g. genes annotated by concepts of the Gene Ontology or PubMed articles annotated using the MeSH, Medical Subject Headings). To fully benefit of this system,

queries have to be expressed using concepts of the same ontology. OBIRS’ interface thus provides query formulation assistance through auto-completion and ontology browsing. It then estimates the overall relevance of each resource w.r.t. a given query. The retrieved resources are ordered according to their overall scores, so that the most relevant resources (indexed with the exact query concepts) are ranked higher than the least relevant ones (indexed with hypernyms or hyponyms of query concepts). We provide visual results thanks to pictogram displayed on an interactive semantic map.
Two versions have been developped:

User Centered and Ontology Based Information Retrieval System for Life Sciences 
Mohameth-François Sy, Sylvie Ranwez, Jacky Montmain, Armelle Regnault, Michel Crampes, Vincent Ranwez. 
In BMC Bioinformatics, 13(Suppl 1):S4, 2012

We also use it in an hybrid approach, that benefits from both lexical and ontological document description, and combines them in a software architecture dedicated to information retrieval and rendering in specific domains. Relevant documents are first identified via their conceptual indexing based on domain ontology, and then segmented to highlight text fragments that deal with users’ information needs.

How ontology based information retrieval systems may benefit from lexical text analysis. 
Sylvie Ranwez, Benjamin Duthil, Mohameth François Sy, Jacky Montmain, Patrick Augereau, Vincent Ranwez.
In "New Trends of Research in Ontologies and Lexical Resources", chapter 11, pp. 209-230, 
Series: Theory and Applications of Natural Language Processing, Springer, February 2013.