Facilitating Efficient Knowledge Management and Discovery in the Agronomic Domain

Agronomy is an overarching field that consists of various areas of research such as genetics, plant molecular biology, ecology and earth science. To effectively develop applications improve crop production through sustainable methods it is important to over lay research findings from these fields as they are highly inter connected. We are currently witnessing rapid advancements in information technologies that continue to drive a flood of data and analysis techniques within the domains mentioned above. In particular, number initiatives have been taken for systematically store and to share information pertaining to agronomy over the web such as the Gramene, TAIR, OryzaBase and the South Green bioinformatics platform, to name a few.

These initiatives provide clear indications of the amount of data being generated in these fields. However, the data currently available are highly distributed and patchy in nature. Using these resources more effectively and taking advantage of associated cross-disciplinary research opportunities poses a major challenge to both domain scientists and information technologists. Effective data integration and management allows a broader perspective spatio-temporally, and across many disciplines, than is possible from one or a series of individual studies. In the long run, this allows information to be used for purposes other than those for which they were originally intended, to address questions that were unknown or unapproachable at the time the data were collected, strategically filling knowledge gaps.

 A solution for the data integration challenges is offered by the Semantic Web technologies (SW). SW was proposed, to remedy the fragmentation of all the potentially very useful information on the web. This involved the development of knowledge representation languages (e.g. RDF: Resource Description Framework, and OWL: Web Ontology Language) and tools that will allow the annotation of this information in a computer understandable form. Through a successfully implemented SW application, scientists would be able to pose complex questions to ‘the Web' through a computer program (a query) that would then assess various ‘computer understandable' information resources to return highly relevant answers to those questions. This is useful, as the assessment of biological findings against prior knowledge fundamentally relies on efficient knowledge management that interlinks information and knowledge at various levels, after which the best supported hypotheses can be selected for further testing.

 The ‘Biological data and knowledge integration’ group at Institut de Biologie Computationnelle (IBC, www.ibc-montpellier.fr/wp/wp5), Montpellier, have been active in exploiting the advantages offered by SW.  we are currently developing a RDF knowledge base, Agronomic Linked Data (AgroLD). The knowledge base is designed to integrate data from various publically available plant centric data sources. The aim of AgroLD project is to collaborate with domain experts in bridging the gap between technology and its potential users to enhance biological research. Here I will present an overview of the project.