AutoSPARQL: Let Users Query Your Knowledge Base
An advantage of Semantic Web standards like RDF and OWL is their flexibility in modifying the structure of a knowledge base. To turn this flexibility into a practical advantage, it is of high importance to have tools and methods, which offer similar flexibility in exploring information in a knowledge base. This is closely related to the ability to easily formulate queries over those knowledge bases. We explain benefits and drawbacks of existing techniques in achieving this goal and then present the QTL algorithm, which fills a gap in research and practice. It uses supervised machine learning and allows users to ask queries without knowing the schema of the underlying knowledge base beforehand and without expertise in the SPARQL query language. We then present the AutoSPARQL user interface, which implements an active learning approach on top of QTL. Finally, we evaluate the approach based on a benchmark data set for question answering over Linked Data.
- 2.Bryant, C.H., Muggleton, S., Oliver, S.G., Kell, D.B., Reiser, P.G.K., King, R.D.: Combining inductive logic programming, active learning and robotics to discover the function of genes. Electron. Trans. Artif. Intell. 5(B), 1–36 (2001)Google Scholar
- 4.Clark, L.: Sparql views: A visual sparql query builder for drupal. In: 9th International Semantic Web Conference (ISWC 2010) (Posters&Demo track) (November 2010)Google Scholar
- 15.Settles, B.: Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison (2009)Google Scholar