Navy operators are being inundated with intelligence information, especially text. Better methods and tools are needed to help them identify the mission-relevant"needles"within these information"haystacks."Researchers have made progress using ontologies--represented as RDF triples--to guide text extraction algorithms and as a reference model for transforming extracted information into RDF triples corresponding to the ontology. One of the major hurdles encountered in this research, however, has been identifying and eliminating redundant statements in the knowledge base. Our goal in this Phase I Project is to design a methodology and architecture for preventing the assertion of redundant RDF statements to knowledge bases based on comparing the lexical semantics of RDF statements with the lexical semantics of text statements and preventing redundant assertions to the knowledge base, based on synonymy. The concept of operations can be simplified to three steps: (1) Find mission-relevant information in text, based on ontology semantics; (2) Format the extracted information and RDF triples as lexicalized; and (3) Filter statements by comparing lexicalized triples to lexicalized text, preventing redundant RDF triples from being asserted to the knowledge base. We call this the RDF Redundancy Find, Format, and Filter process, or RDF-F3.
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