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Abstract: 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.
Keywords: time representation, Allen's temporal algebra, automatic learning of temporal relations, inductive logic programming, FOIL, time points, time intervals, learning under closed world assumption
