ABSTRACT
This article describes a high-precision semantic question answering (SQA) engine for large datasets. We employ an RDF store to index the semantic information extracted from large document collections and a natural language to SPARQL conversion module to find desired information. In order to be able to find answers to complex questions in structured/unstructured data resources, our system produces rich semantic structures from the data resources and then transforms the extracted knowledge into an RDF representation. In order to facilitate easy access to the information stored in the RDF semantic index, our system accepts a user's natural language questions, translates them into SPARQL queries and returns a precise answer back to the user. Our improvements in performance over a regular free text search index-based question answering engine prove that SQA can benefit greatly from the addition and consumption of deep semantic information.
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Index Terms
Semantic question answering on big data
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