Nicola Fanizzi

An algorithm based on counterfactuals for concept learning in the Semantic Web

In the line of realizing the Semantic-Web by means of mechanized practices, we tackle the problem of building ontologies, assisting the knowledge engineers’ job by means of Machine Learning techniques. In particular, we investigate on solutions for the induction of concept descriptions in a semi-automatic fashion. In particular, we present an algorithm that is able to infer definitions in the

Multistrategy Theory Revision: Induction and Abductionin INTHELEX

\mathcal{ALC}

A dissimilarity measure for ALC concept descriptions

Description Logic (a sub-language of OWL-DL) from instances made available by domain experts. The effectiveness of the method with respect to past algorithms is also empirically evaluated with an experimentation in the document image understanding domain.

A Logic Framework for the Incremental Inductive Synthesis of Datalog Theories

In this paper we focus on learning concept descriptions expressed in Description Logics. After stating the learning problem in this context, a FOIL-like algorithm is presented that can be applied to general DL languages, discussing related theoretical aspects of learning with the inherent incompleteness underlying the semantics of this representation. Subsequently we present an experimental evaluation of the implementation of this algorithm performed on some real ontologies in order to empirically assess its performance.

Query answering and ontology population: an inductive approach

This paper presents an integration of induction and abduction in INTHELEX, a prototypical incremental learning system. The refinement operators perform theory revision in a search space whose structure is induced by a quasi-ordering, derived from Plotkins θ-subsumption, compliant with the principle of Object Identity. A reduced complexity of the refinement is obtained, without a major loss in terms of expressiveness. These inductive operators have been proven ideal for this search space. Abduction supports the inductive operators in the completion of the incoming new observations. Experiments have been run on a standard dataset about family trees as well as in the domain of document classification to prove the effectiveness of such multistrategy incremental learning system with respect to a classical batch algorithm.

On the Influence of Description Logics Ontologies on Conceptual Similarity

In the Semantic Web vision of the World Wide Web, content will not only be accessible to humans but will also be available in machine interpretable form as ontological knowledge bases. Ontological knowledge bases enable formal querying and reasoning and, consequently, a main research focus has been the investigation of how deductive reasoning can be utilized in ontological representations to enable more advanced applications. However, purely logic methods have not yet proven to be very effective for several reasons: First, there still is the unsolved problem of scalability of reasoning to Web scale. Second, logical reasoning has problems with uncertain information, which is abundant on Semantic Web data due to its distributed and heterogeneous nature. Third, the construction of ontological knowledge bases suitable for advanced reasoning techniques is complex, which ultimately results in a lack of such expressive real-world data sets with large amounts of instance data. From another perspective, the more expressive structured representations open up new opportunities for data mining, knowledge extraction and machine learning techniques. If moving towards the idea that part of the knowledge already lies in the data, inductive methods appear promising, in particular since inductive methods can inherently handle noisy, inconsistent, uncertain and missing data. While there has been broad coverage of inducing concept structures from less structured sources (text, Web pages), like in ontology learning, given the problems mentioned above, we focus on new methods for dealing with Semantic Web knowledge bases, relying on statistical inference on their standard representations. We argue that machine learning research has to offer a wide variety of methods applicable to different expressivity levels of Semantic Web knowledge bases: ranging from weakly expressive but widely available knowledge bases in RDF to highly expressive first-order knowledge bases, this paper surveys statistical approaches to mining the Semantic Web. We specifically cover similarity and distance-based methods, kernel machines, multivariate prediction models, relational graphical models and first-order probabilistic learning approaches and discuss their applicability to Semantic Web representations. Finally we present selected experiments which were conducted on Semantic Web mining tasks for some of the algorithms presented before. This is intended to show the breadth and general potential of this exiting new research and application area for data mining.

Tractable Reasoning with Bayesian Description Logics

This work presents a dissimilarity measure for Description Logics that are the theoretical counterpart of the standard representations for ontological knowledge. The focus is on the definition of a dissimilarity measure for ALC concept descriptions, based both on the syntax and on the semantics of the descriptions. An extension of the measure is proposed for involving individuals and then for evaluating their dissimilarity.

Knowledge-intensive induction of terminologies from metadata

This paper presents a logic framework for the incremental inductive synthesis of Datalog theories. It allows us to cast the problem as a process of abstract diagnosis and debugging of an incorrect theory. This process involves a search in a space, whose algebraic structure (conferred by the notion of object identity) makes easy the definition of algorithms that meet several properties which are deemed as desirable from the point of view of the theoretical computer science. Such algorithms embody two ideal refinement operators, one for generalizing incomplete clauses, and the other one for specializing inconsistent clauses.