From Questions to Effective Answers: On the Utility of Knowledge-Driven Querying Systems for Life Sciences Data
We compare two distinct approaches for querying data in the context of the life sciences. The first approach utilizes conventional databases to store the data and intuitive form-based interfaces to facilitate easy querying of the data. These interfaces could be seen as implementing a set of 'pre-canned' queries commonly used by the life science researchers that we study. The second approach is based on semantic Web technologies and is knowledge (model) driven. It utilizes a large OWL ontology and same datasets as before but associated as RDF instances of the ontology concepts. An intuitive interface is provided that allows the formulation of RDF triples-based queries. Both these approaches are being used in parallel by a team of cell biologists in their daily research activities, with the objective of gradually replacing the conventional approach with the knowledge-driven one. This provides us with a valuable opportunity to compare and qualitatively evaluate the two approaches. We describe several benefits of the knowledge-driven approach in comparison to the traditional way of accessing data, and highlight a few limitations as well. We believe that our analysis not only explicitly highlights the specific benefits and limitations of semantic Web technologies in our context but also contributes toward effective ways of translating a question in a researcher's mind into precise computational queries with the intent of obtaining effective answers from the data. While researchers often assume the benefits of semantic Web technologies, we explicitly illustrate these in practice.
Kno.e.sis Research Center Technical Report, 2010.
© Asiaee, A. H., Doshi, P., Minning, T., Sahoo, S. S., Parikh, P., Sheth, A. P., & Tarleton, R. L., 2010
Asiaee, A. H., Doshi, P., Minning, T., Sahoo, S. S., Parikh, P., Sheth, A. P., & Tarleton, R. L. (2010). From Questions to Effective Answers: On the Utility of Knowledge-Driven Querying Systems for Life Sciences Data.