Schema-Free Structured Querying of DBpedia Data

Abstract

We need better ways to query large linked data collections such as DBpedia. Using the SPARQL query language requires not only mastering its syntax but also understanding the RDF data model, large ontology vocabularies and URIs for denoting entities. Natural language interface systems address the problem, but are still subjects of research. We describe a compromise in which non-experts specify a graphical query "skeleton'' and annotate it with freely chosen words, phrases and entity names. The combination reduces ambiguity and allows the generation of an interpretation that can be translated into SPARQL. Key research contributions are the robust methods that combine statistical association and semantic similarity to map user terms to the most appropriate classes and properties in the underlying ontology.