ABSTRACT
The Semanticscience Integrated Ontology (SIO) is an ontology to facilitate biomedical knowledge discovery. SIO features a simple upper level comprised of essential types and relations for the rich description of arbitrary (real, hypothesized, virtual, fictional) objects, processes and their attributes. SIO specifies simple design patterns to describe and associate qualities, capabilities, functions, quantities, and informational entities including textual, geometrical, and mathematical entities, and provides specific extensions in the domains of chemistry, biology, biochemistry, and bioinformatics. SIO provides an ontological foundation for the Bio2RDF linked data for the life sciences project and is used for semantic integration and discovery for SADI-based semantic web services. SIO is freely available to all users under a creative commons by attribution license. See website for further information: http://sio.semanticscience.org.
ABSTRACT
Pharmacogenomics aims to understand pharmacological response with respect to genetic variation. Essential to the delivery of better health care is the use of pharmacogenomics knowledge to answer questions about therapeutic, pharmacological or genetic aspects. Several XML markup languages have been developed to capture pharmacogenomic and related information so as to facilitate data sharing. However, recent advances in semantic web technologies have presented exciting new opportunities for pharmacogenomics knowledge discovery by representing the information with machine understandable semantics. Progress in this area is illustrated with reference to the personalized medicine project that aims to facilitate pharmacogenomics knowledge discovery through intuitive knowledge capture and sophisticated question answering using automated reasoning over expressive ontologies.
Subject(s)
Internet , Knowledge Bases , Pharmacogenetics , Semantics , Databases, Factual , Depression/drug therapy , Drug Design , Humans , Information Dissemination/methods , Information Storage and Retrieval/methods , Systems Integration , Terminology as Topic , Vocabulary, ControlledABSTRACT
Knowledge management is an ongoing challenge for the biological community such that large, diverse and continuously growing information requires more sophisticated methods to store, integrate and query their knowledge. The semantic web initiative provides a new knowledge engineering framework to represent, share and discover information. In this paper, we describe our efforts towards the development of an ontology-based knowledge base, including aspects from ontology design and population using "semantic" data mashup, to automated reasoning and semantic query answering. Based on yeast data obtained from the Saccharomyces Genome Database and UniProt, we discuss the challenges encountered during the building of the knowledge base and how they were overcome.
