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Abstract:
The proliferation of knowledge-sharing communities likeWikipedia and the
advances in automated information extraction from Web pages enable the
construction of large knowledge bases with facts about entities and their
relationships. The facts can be represented in the RDF data model, as so-called
subject-property-object triples, and can thus be queried by structured query
languages like SPARQL. In principle, this allows precise querying in the
database spirit. However, RDF data may be highly diverse and queries may return
way too many results, so that ranking by informativeness measures is crucial to
avoid overwhelming users. Moreover, as facts are extracted from textual
contexts or have community-provided annotations, it can be beneficial to
consider also keywords for formulating search requests. This paper gives an
overview of recent and ongoing work on ranked retrieval of RDF data with
keyword-augmented structured queries. The ranking method is based on
statistical language models, the state-of-the-art paradigm in information
retrieval. The paper develops a novel form of language models for the
structured, but schema-less setting of RDF triples and extended SPARQL queries.
