DBpediaSearch : an effective search engine for DBpedia -

Abstract

The active progress of knowledge-sharing communities like Wikipedia have made it achievable to build large knowledge-bases, such as DBpedia. These knowledge-bases use the Resource Description Framework (RDF) as a flexible data model for representing the information in the Web. The semantic query language for RDF is known as SPARQL. Although querying with SPARQL gives very specific results, the users knowledge of the underlying data is a must. In this thesis, we propose an effective search engine for DBpedia. The search sys- tem takes SPARQL queries augmented with keywords as input and gives the most relevant results as output. To be able to do this, we develop a novel rank- ing model based on statistical machine translation for both triple-pattern queries and keyword-augmented triple-pattern queries. Our system supports automatic query relaxation in case no results were found. Our ranking model also takes into consideration result diversity in order to ensure that the user is provided with a wide range of aspects of the query results. We develop a diversity-aware evaluation metric based on the Discounted Cumulative Gain to evaluate diversified result sets. Finally, we build an evaluation benchmark on DBpedia, which we use to evaluate the effectiveness of our search engine.