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\iteman{io-port 05949514}
\itemau{Ferilli, S.; Basile, T.M.A.; Di Mauro, N.; Esposito, F.}
\itemti{Plugging numeric similarity in first-order logic Horn clauses comparison.}
\itemso{Pirrone, Roberto (ed.) et al., AI*IA 2011: Artificial intelligence around man and beyond. XIIth international conference of the Italian association for artificial intelligence, Palermo, Italy, September 15--17, 2011. Proceedings. Berlin: Springer (ISBN 978-3-642-23953-3/pbk). Lecture Notes in Computer Science 6934. Lecture Notes in Artificial Intelligence, 33-44 (2011).}
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Summary: Horn clause Logic is a powerful representation language exploited in Logic Programming as a computer programming framework and in Inductive Logic Programming as a formalism for expressing examples and learned theories in domains where relations among objects must be expressed to fully capture the relevant information. While the predicates that make up the description language are defined by the knowledge engineer and handled only syntactically by the interpreters, they sometimes express information that can be properly exploited only with reference to a suitable background knowledge in order to capture unexpressed and underlying relationships among the concepts described. This is typical when the representation includes numerical information, such as single values or intervals, for which simple syntactic matching is not sufficient. This work proposes an extension of an existing framework for similarity assessment between First-Order Logic Horn clauses, that is able to handle numeric information in the descriptions. The viability of the solution is demonstrated on sample problems.
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\itemli{doi:10.1007/978-3-642-23954-0\_6}
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