Abstract
The problem of predicting the membership w.r.t. a target concept for individuals of Semantic Web knowledge bases can be cast as a concept learning problem, whose goal is to induce intensional definitions describing the available examples. However, the models obtained through the methods borrowed from Inductive Logic Programming e.g. Terminological Decision Trees, may be affected by two crucial aspects: the refinement operators for specializing the concept description to be learned and the heuristics employed for selecting the most promising solution (i.e. the concept description that describes better the examples). In this paper, we started to investigate the effectiveness of Terminological Decision Tree and its evidential version when a refinement operator available in DL-Learner and modified heuristics are employed. The evaluation showed an improvement in terms of the predictiveness.
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Notes
- 1.
The refinement operator was originally devised to consider \(\mathcal {ALC}\) expressiveness.
- 2.
The length of a concept C, len(C) can be defined inductively as:
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len(A) = len(\(\top \)) = len(\(\bot \)) = 1
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len(\(\lnot D\)) = len(D) + 1
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len(\(D \sqcap E\)) = len(\(D \sqcup E\)) = len(D) + len(E) + 1
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len(\(\exists R.D\)) = len(\(\forall R.D\)) + 1.
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Acknowledgements
This work fulfills the objectives of the PON 02005633489339 project “Puglia@Service - Internet-based Service Engineering enabling Smart Territory structural development” funded by the Italian Ministry of University and Research (MIUR).
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Rizzo, G., Fanizzi, N., Lehmann, J., Bühmann, L. (2016). Integrating New Refinement Operators in Terminological Decision Trees Learning. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds) Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science(), vol 10024. Springer, Cham. https://doi.org/10.1007/978-3-319-49004-5_33
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