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Automatic Strengthening of GraphStructured Knowledge Bases
"... ( † = corresponding author) We address two problems in underspecified graphstructured knowledge bases (GSKBs): the coreference and the provenance problem. Both problems are important for a variety of reasons. The former asks “Which existentially quantified variables in different but related axi ..."
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( † = corresponding author) We address two problems in underspecified graphstructured knowledge bases (GSKBs): the coreference and the provenance problem. Both problems are important for a variety of reasons. The former asks “Which existentially quantified variables in different but related axioms of a GSKB possibly denote identical domain individuals?”, and the latter “From which axioms in a GSKB is a piece of knowledge getting derived? ” To decide the former, we need to be able to prove equality between different variables – a GSKB in which this is possible is called a strenghtened GSKB, and an underspecified GSKB otherwise. The latter occur naturally in many knowledge acquisition contexts, and are also easier to author. We hence present an algorithm which rewrites an underspecified GSKB into a strengthened GSKB, by virtue of Skolemization and addition of equality atoms such that the coreference information can be drawn from it. This enlarges the logical theory (the deductive closure) of the GSKB and strengthens its inferential power, hence affecting the provenance information. Our algorithm is modeltheoretic in nature and exploits a novel class of desirable, preferred models, which capture the desired coreferences. The algorithm is a logical reconstruction of an implemented algorithm that we successfully applied to a largescale biological knowledge base, in which it identified more that 22,000 equality atoms. 1