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1

Fuzzy Clustering for Semantic Knowledge Bases

Esposito F., d’Amato C., Fanizzi N.

Fundamenta Informaticae

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2010

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Vol. 99, nr 2

187-205

EN

This work focusses on the problem of clustering resources contained in knowledge bases represented throughmulti-relational standard languages that are typical for the context of the SemanticWeb, and ultimately founded in Description Logics. The proposed solution relies on effective and language-independent dissimilarity measures that are based on a finite number of dimensions corresponding to a committee of discriminating features, that stands for a context, represented by concept descriptions in Description Logics. The proposed clustering algorithm expresses the possible clusterings in tuples of central elements: in this categorical setting, we resort to the notion of medoid, w.r.t. the given metric. These centers are iteratively adjusted following the rationale of fuzzy clustering approach, i.e. one where the membership to each cluster is not deterministic but graded, ranging in the unit interval. This better copes with the inherent uncertainty of the knowledge bases expressed in Description Logics which adopt an open-world semantics. An extensive experimentation with a number of ontologies proves the feasibility of our method and its effectiveness in terms of major clustering validity indices.

2

Multistrategy Operators for Relational Learning and Their Cooperation

Esposito F., Fanizzi N., Ferilli S., Basile T.M.A., Di Mauro N.

Fundamenta Informaticae

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2006

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Vol. 69, nr 4

389-409

EN

Traditional Machine Learning approaches based on single inference mechanisms have reached their limits. This causes the need for a framework that integrates approaches based on abduction and abstraction capabilities in the inductive learning paradigm, in the light of Michalski's Inferential Theory of Learning (ITL). This work is intended as a survey of the most significant contributions that are present in the literature, concerning single reasoning strategies and practical ways for bringing them together and making them cooperate in order to improve the effectiveness and efficiency of the learning process. The elicited role of an abductive proof procedure is tackling the problem of incomplete relevance in the incoming examples. Moreover, the employment of abstraction operators based on (direct and inverse) resolution to reduce the complexity of the learning problem is discussed. Lastly, a case study that implements the combined framework into a real multistrategy learning system is briefly presented.

3

A Generalization Model Based on OI-implication for Ideal Theory Refinement

Esposito F., Fanizzi N., Ferilli S., Semeraro G.

Fundamenta Informaticae

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2001

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Vol. 47, nr 1,2

15-33

EN

A framework for theory refinement is presented pursuing the efficiency and effectiveness of learning regarded as a search process. A refinement operator satisfying these requirements is formally defined as ideal. Past results have demonstrated the impossibility of specifying ideal operators in search spaces where standard generalization models, like logical implication or q-subsumption, are adopted. By assuming the object identity bias over a space defined by a clausal language ordered by logical implication, a novel generalization model, named OI-implication, is derived and we prove that ideal operators can be defined for the resulting search space.