Knowledge models for integrating and querying biological data: a semantic approach

25 March 2005

Erik Brauner


Human knowledge about biological systems is inherently complicated by the primary medium for biological knowledge exchange: human language. Machine representations of complex systems such as signal transduction pathways face the problem of capturing the nuances of language, which can often be context dependent. Protein A activates Protein B could have any number of contextually dependent meanings. Traditional approaches of creating relational data stores for this sort of information have proven problematic, and with the ever increasing desire to integrate multiple data sources new approaches are needed. Presenting a real world example I will start by reviewing some of the alternative approaches to knowledge representation currently in use, including ontologies, frame based languages, semantic networks, and the semantic web. Finally I will present a hybrid semantic network based approach that we are currently utilizing to build a knowledge base of molecular targets and cancer relevant pathways.

current theory lunch schedule