for Biomedical Knowledge Discovery
Biochemical Semantic Web
Web search engines like Google are useful in helping you find web pages that you may have an interest in, but they have little capability in answering a question you might have. The reason for this is that information on the web is not represented in a machine understandable format, and as such, cannot be complexly queried nor precisely answered. If information were better structured, then we could use computers to reason about the information and infer knowledge that is explicit in our shared conceptualization, i.e. an ontology. Thus, by combining ontologies with existing information, we will be able to discover new knowledge, in that we will discover links between things that were not explicitly stated, but would be inferred on careful analysis. The primary goal of the Semantic Web is to add semantics to the current Web, in part, by designing ontologies which describe and relate concepts and objects using formal, logic-based representations.
Our research aims to capture the semantics of biochemical knowledge such that computer-based inferences about structure and function become possible. The approach involves the design of biochemical ontologies and use of a reasoning-capable knowledge base. This research aims to form a cornerstone of the newly emerging semantic web so as to provide biochemical knowledge in a format that is amenable to computer-based reasoning. In combination with other complimentary efforts, we expect that new semantic links may be formed with our efforts, leading to improved data integration and powerful new data mining opportunities over heterogeneous biochemical knowledge.
Recent WorkSee our OWL ontologies for knowledge representation and reasoning on the semantic web.
Describing chemical functional groups in OWL-DL for the classification of chemical compounds.
Towards a Semantic Knowledge Base for Yeast Biologists.
Integrated OWL and SWRL reasoning plugin for Protege-OWL 3.2.