Abstract
Few works are available in the literature to define similarity criteria between First-Order Logic formulæ, where the presence of relations causes various portions of one description to be possibly mapped in different ways onto another description, which poses serious computational problems. Hence, the need for a set of general criteria that are able to support the comparison between formulæ. This could have many applications; this paper tackles the case of two descriptions (e.g., a definition and an observation) to be generalized, where the similarity criteria could help in focussing on the subparts of the descriptions that are more similar and hence more likely to correspond to each other, based only on their syntactic structure. Experiments on real-world datasets prove the effectiveness of the proposal, and the efficiency of the corresponding implementation in a generalization procedure.
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Ferilli, S., Basile, T.M.A., Di Mauro, N., Biba, M., Esposito, F. (2007). Similarity-Guided Clause Generalization. In: Basili, R., Pazienza, M.T. (eds) AI*IA 2007: Artificial Intelligence and Human-Oriented Computing. AI*IA 2007. Lecture Notes in Computer Science(), vol 4733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74782-6_25
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DOI: https://doi.org/10.1007/978-3-540-74782-6_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74781-9
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