Adaptation of an Open Source Semantic and Conceptual Retrieval Framework to the Astrobiological Domain

Introduction: Astrobiology is by nature a system-level science, meaning that it is concerned with complex , multidisciplinary, multi-phenomena behaviors of large physical and biological systems. Due to the breadth of the undertakings in astrobiological inquiry, researchers in the field must rely heavily on information technology to consolidate and represent knowledge and data from across many disciplines [1]. AIR-Frame, the Astrobiology Integrative Research Framework , is an integrative knowledge framework under active development by the University of Hawaii NASA Astrobiology Institute. By leveraging the power of new standards and technologies developed for the Semantic Web, AIRFrame allows diverse research concepts to be related and discovered as a high-level activity. Using a combination of state of the art semantic retrieval methods and pattern classification algorithms , AIRFrame permits users be they researchers, students, or the general public to search for a meaning rather than attempting to guess the correct combination of keywords to get search results on a desired topic. By eliminating the need to search for data using various combinations of specific keywords that have no set standard, differ across disciplines, and change over time and by easing the burden of sifting through vast amounts of rapidly accumulating data, AIRFrame can reduce the cognitive load on researchers and other users and allow discovery of otherwise unfindable resources , embodying many of the recommendations on computer-supported collaborative work coming from research in the social informatics field [2]. Discussion: AIRFrame is intended to be an integrated discovery framework which is able to show users both conceptual and functional relationships between diverse research documents. By employing an ontology and semantic markup of documents, AIR-Frame allows discovery of related concepts which might be invisible to users of standard search engines unfamiliar with key-terms used by diverse disciplines. We have based the underlying functional-ity of AIRFrame on Textpresso [3], a freely available, open-source information retrieval and extraction system which has been successfully implemented on 17 different sets of literatures within the biology community , from C. elegans genome research to pharmage-nomics. The AIRFrame/Textpresso system consists of two major components, a database of full-text scholarly documents and an ontology. The system's ontology