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Abstract:
The Web has the potential to become the world’s largest knowledge base. In
order to unleash this potential, the wealth of information available on the Web
needs to be extracted and organized. There is a need for new querying
techniques that are simple and yet more expressive than those provided by
standard keyword-based search engines. Searching for knowledge rather than Web
pages needs to consider inherent semantic structures like entities (person,
organization, etc.) and relationships (isA, locatedIn, etc.).
In this paper, we propose NAGA, a new semantic search engine. NAGA builds on a
knowledge base, which is organized as a graph with typed edges, and consists of
millions of entities and relationships extracted from Web-based corpora. A
graph-based query language enables the formulation of queries with additional
semantic information. We
introduce a novel scoring model, based on the principles of
generative language models, which formalizes several notions like confidence,
informativeness and compactness and uses them to rank query results. We
demonstrate NAGA’s
superior result quality over state-of-the-art search engines and question
answering systems.
