Abstract
Predicate Invention aims at discovering new emerging concepts in a logic theory. Since there is usually a combinatorial explosion of candidate concepts to be invented, only those that are really relevant should be selected, which cannot be done manually due to the huge number of candidates. While purely logical automatic approaches may be too rigid, statistical solutions provide more flexibility in assigning a degree of relevance to the various candidates in order to select the best ones. This paper proposes a new Statistical Relational Learning approach to Predicate Invention. It was implemented and tested on a traditional problem, yielding interesting results.
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In a normal procedure, the learned theory is to be tested on the remaining example; however, for the purposes of this paper we are not interested in the predictive capabilities of the theories, so we skip this step.
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Acknowledgments
This work was partially funded by the Italian PON 2007–2013 project PON02\(_{-}\)00563\(_{-}\)3489339 ‘Puglia@Service’.
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Ferilli, S., Fatiguso, G. (2015). WPI: Markov Logic Network-Based Statistical Predicate Invention. In: Esposito, F., Pivert, O., Hacid, MS., Rás, Z., Ferilli, S. (eds) Foundations of Intelligent Systems. ISMIS 2015. Lecture Notes in Computer Science(), vol 9384. Springer, Cham. https://doi.org/10.1007/978-3-319-25252-0_12
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