Linking biological databases semantically for knowledge discovery

Many important life sciences questions are aimed at studying the relationships and interactions between biological functions/processes and biological entities such as genes. The answers may be found by examining diverse types of biological/genomic databases. Finding these answers, however, requires accessing, and retrieving data, from diverse biological data sources. More importantly, sophisticated knowledge discovery processes involve traversing through large numbers of inherent links among various data sources. Currently, the links among data are either implemented as hyperlinks without explicitly indicating their meanings and labels, or hidden in a seemingly simple text format. Consequently, biologists spend numerous hours identifying potentially useful links and following each lead manually, which is time-consuming and error-prone. Our research is aimed at constructing semantic relationships among all biological entities. We have designed a semantic model to categorize and formally define the links. By incorporating ontologies such as Gene or Sequence ontology, we propose techniques to analyze the links embedded within and among data records, to explicitly label their semantics, and to facilitate link traversal, querying, and data sharing. Users may then ask complicated and ad hoc questions and even design their own workflow to support their knowledge discovery processes. In addition, we have performed an empirical analysis to demonstrate that our method can not only improve the efficiency of querying multiple databases, but also yield more useful information.