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1

Automatic Learning of Temporal Relations Under the Closed World Assumption

Nicoletti M. C., Lisboa F. O. S. S., Hruschka Jr E. R.

Fundamenta Informaticae

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2013

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Vol. 124, nr 1/2

133--151

EN

Time plays an important role in the vast majority of problems and, as such, it is a vital issue to be considered when developing computer systems for solving problems. In the literature, one of the most influential formalisms for representing time is known as Allen's Temporal Algebra based on a set of 13 relations (basic and reversed) that may hold between two time intervals. In spite of having a few drawbacks and limitations, Allen's formalism is still a convenient representation due to its simplicity and implementability and also, due to the fact that it has been the basis of several extensions. This paper explores the automatic learning of Allen's temporal relations by the inductive logic programming system FOIL, taking into account two possible representations for a time interval: (i) as a primitive concept and (ii) as a concept defined by the primitive concept of time point. The goals of the experiments described in the paper are (1) to explore the viability of both representations for use in automatic learning; (2) compare the facility and interpretability of the results; (3) evaluate the impact of the given examples for inducing a proper representation of the relations and (4) experiment with both representations under the assumption of a closed world (CWA), which would ease continuous learning using FOIL. Experimental results are presented and discussed as evidence that the CWA can be a convenient strategy when learning Allen's temporal relations.

2

A Logic-based Computational Method for the Automated Induction of Fuzzy Ontology Axioms

Lisi F. A., Straccia U.

Fundamenta Informaticae

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2013

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Vol. 124, nr 4

503--519

EN

Fuzzy Description Logics (DLs) are logics that allow to deal with structured vague knowledge. Although a relatively important amount of work has been carried out in the last years concerning the use of fuzzy DLs as ontology languages, the problem of automatically managing the evolution of fuzzy ontologies has received very little attention so far. We describe here a logic-based computational method for the automated induction of fuzzy ontology axioms which follows the machine learning approach of Inductive Logic Programming. The potential usefulness of the method is illustrated by means of an example taken from the tourism application domain.

3

Multi-Dimensional Relational Sequence Mining

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

Fundamenta Informaticae

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2008

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Vol. 89, nr 1

23-43

EN

The issue addressed in this paper concerns the discovery of frequent multi-dimensional patterns from relational sequences. The great variety of applications of sequential pattern mining, such as user profiling, medicine, local weather forecast and bioinformatics, makes this problem one of the central topics in data mining. Nevertheless, sequential information may concern data on multiple dimensions and, hence, the mining of sequential patterns from multi-dimensional information results very important. In a multi-dimensional sequence each event depends on more than one dimension, such as in spatio-temporal sequences where an event may be spatially or temporally related to other events. In literature, the multi-relational data mining approach has been successfully applied to knowledge discovery fromcomplex data. However, there exists no contribution to manage the general case of multi-dimensional data in which, for example, spatial and temporal information may co-exist. This work takes into account the possibility to mine complex patterns, expressed in a first-order language, in which events may occur along different dimensions. Specifically, multidimensional patterns are defined as a set of atomic first-order formulae in which events are explicitly represented by a variable and the relations between events are represented by a set of dimensional predicates. A complete framework and an Inductive Logic Programming algorithm to tackle this problem are presented along with some experiments on artificial and real multi-dimensional sequences proving its effectiveness.

4

A Multiple-Clause Folding Rule Using Instantiation and Generalization

Rosenblueth D.A.

Fundamenta Informaticae

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2006

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Vol. 69, nr 1,2

219-249

EN

A program-transformation system is determined by a repertoire of correctness-preserving rules, such as folding and unfolding. Normally, we would like the folding rule to be in some sense the inverse of the unfolding rule. Typically, however, the folding rule of logic program transformation systems is an inverse of a limited kind of unfolding. In many cases this limited kind of folding suffices. We argue, nevertheless, that it is both important and possible to extend such a folding so as to be able to fold the clauses resulting from any unfolding of a positive literal. This extended folding rule allows us to derive some programs underivable by the existing version of this rule alone. In addition, our folding rule has applications to decompilation and reengineering, where we are interested in obtaining high-level program constructs from low-level program constructs. Moreover, we establish a connection between logic program transformation and inductive logic programming. This connection stems from viewing our folding rule as a common extension of the existing multiple-clause folding rule, on the one hand, and an operator devised in inductive logic programming, called ``intra-construction,'' on the other hand. Hence, our folding rule can be regarded as a step towards incorporating inductive inference into logic program transformation. We prove correctness with respect to Dung and Kanchanasut's semantic kernel.

5

Learning Recursive Theories in the Normal ILP Setting

Malerba D.

Fundamenta Informaticae

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2003

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Vol. 57, nr 1

39--77

EN

Induction of recursive theories in the normal ILP setting is a difficult learning task whose complexity is equivalent to multiple predicate learning. In this paper we propose computational solutions to some relevant issues raised by the multiple predicate learning problem. A separate-and-parallel-conquer search strategy is adopted to interleave the learning of clauses supplying predicates with mutually recursive definitions. A novel generality order to be imposed on the search space of clauses is investigated, in order to cope with recursion in a more suitable way. The consistency recovery is performed by reformulating the current theory and by applying a layering technique, based on the collapsed dependency graph. The proposed approach has been implemented in the ILP system ATRE and tested on some laboratory-sized and real-world data sets. Experimental results demonstrate that ATRE is able to learn correct theories autonomously and to discover concept dependencies. Finally, related works and their main differences with our approach are discussed.

6

Algorytm generowania reguł pierwszego rzędu wykorzystujący metody zbiorów przybliżonych

Stepaniuk J, Góralczuk L.

Zeszyty Naukowe Politechniki Białostockiej. Informatyka

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2002

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Z.1

235-250

PL

W pracy przedstawiono algorytm generowania reguł pierwszego rzędu, tzn. zależności, które w poprzedniku mają koniunkcję formuł atomowych bądź ich negacji a w następniku formułę atomową. Technikę zbiorów przybliżonych wykorzystano w procesie doboru literałów mogących wchodzić w skład przesłanki generowanej reguły. Kryterium doboru opiera się na tym, aby reguła po dołączeniu do jej przesłanki kandydującego literału jak najlepiej rozróżniała przykłady pozytywne i negatywne, które do tej pory nie były rozróżnialne.

EN

The aim of this paper is to introduce and investigate an algorithm for finding first order rules. Rough set theory is used in the process of selecting literals, which may be part of the rule. The criterion of selecting literals reads as follows: only those literals are selected, which adding to the rule makes that the rule discerns the most examples from those, which were yet undiscerned.

7

Prediction of Ordinal Classes Using Regression Trees

Kramer S., Widmer G., Pfahringer B., De Groeve M.

Fundamenta Informaticae

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2001

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Vol. 47, nr 1,2

1-13

EN

This paper is devoted to the problem of learning to predict ordinal (i.e., ordered discrete) classes using classification and regression trees. We start with S-CART, a tree induction algorithm, and study various ways of transforming it into a learner for ordinal classification tasks. These algorithm variants are compared on a number of benchmark data sets to verify the relative strengths and weaknesses of the strategies and to study the trade-off between optimal categorical classification accuracy (hit rate) and minimum distance-based error. Preliminary results indicate that this is a promising avenue towards algorithms that combine aspects of classification and regression.