Abstract. We argue for the usefulness of abductive reasoning in the context of ontologies. We discuss several applicaton scenarios in which various forms of abduction would be useful, introduce corresponding abductive reasoning tasks, give examples, and begin to develop the formal apparatus needed to employ abductive inference in expressive description logics. 1

Abstract. Ontologies are usually considered as static data structures representing conceptual knowledge of humans. For certain types of applications it would be desirable to develop an algorithmic adaptation process that allows dynamic modifications of the ontology in the case new information is available. Dynamic updates can generate conflicts between old and new information resulting in inconsistencies. We propose an algorithm that can model the adaptation processes for conflicting and non-conflicting updates defined on ALE-TBoxes. 1

Abstract. The design of comprehensive ontologies is a serious challenge. Therefore, it is necessary to support the ontology designer by providing him with design methodologies, ontology editors, and automated reasoning tools that explicate the consequences of his design decisions. Currently, reasoning tools are largely limited to the reasoning services (i) computing the subsumption hierarchy of the classes in an ontology and (ii) determining the consistency of these classes. In this paper, we survey the most important tasks that arise in ontology design and discuss how they can be supported by automated reasoning tools. In particular, we show that it is beneficial to go beyond the usual reasoning services (i) and (ii). 1

Abstract. A clustering method is presented which can be applied to relational knowledge bases. It can be used to discover interesting groupings of resources through their (semantic) annotations expressed in the standard languages employed for modeling concepts in the Semantic Web. The method exploits a simple (yet effective and language-independent) semi-distance measure for individuals, that is based on the resource semantics w.r.t. a number of dimensions corresponding to a committee of features represented by a group of concept descriptions (discriminating features). The algorithm is an fusion of the classic Bisecting k-Means with approaches based on medoids since they are intended to be applied to relational representations. We discuss its complexity and the potential applications to a variety of important tasks. 1 Learning Methods for Concept Languages In the inherently distributed applications related to the Semantic Web (henceforth SW) there is an extreme need of automatizing those activities which are

The exploration-exploitation tradeoff is crucial to reinforcement-learning (RL) agents, and a significant number of sample complexity results have been derived for agents in propositional domains. These results guarantee, with high probability, near-optimal behavior in all but a polynomial number of timesteps in the agent’s lifetime. In this work, we prove similar results for certain relational representations, primarily a class we call “relational action schemas”. These generalized models allow us to specify state transitions in a compact form, for instance describing the effect of picking up a generic block instead of picking up 10 different specific blocks. We present theoretical results on crucial subproblems in action-schema learning using the KWIK framework, which allows us to characterize the sample efficiency of an agent learning these models in a reinforcement-learning setting. These results are extended in an apprenticeship learning paradigm where and agent has access not only to its environment, but also to a teacher that can demonstrate traces of state/action/state sequences. We show that the class of action schemas that are efficiently learnable in this paradigm is strictly larger than those learnable in the online setting. We link

This work presents a framework, founded on multi-relational instancebased learning, for inductive (memory-based) reasoning on knowledge bases expressed in Description Logics. The procedure, which exploits a relational dissimilarity measure based on the notion of Information Content, can be employed both to answer to class-membership queries and to predict assertions, that may not be logically entailed by the knowledge base. These tasks may be the baseline for other inductive methods for ontology construction and evolution. In a preliminary experimentation, we show that the method is sound. Besides it is actually able to induce new knowledge that might be acquired in the knowledge base.

This work presents a method for retrieval in knowledge bases expressed in Description Logics, founded in the instancebased learning. The procedure implements the disjunctive version space approach exploiting a notion of semantic difference. The method can be employed both to answer to class-membership queries, even though the answers are not logically entailed by the knowledge base, e.g. there are some inconsistent assertions due to heterogeneous sources. In addition, it may also predict/suggest new assertions The method has been implemented and tested in an experimentation, where we show that it is sound and effective.

Abstract. We present a context-based method, founded in the instance-based learning and the disjunctive version space approach, for performing approximate query answering of knowledge bases expressed in Description Logics. Differently from the current approaches, it is able to supply answers, even though they are not logically entailed by the knowledge base (e.g. there are inconsistent assertions). Moreover, the method could be also able to induce new knowledge. that can be employed to suggest to the knowledge engineer new instances for a considered ontology (making the ontology population task semi-automatic). The method has been experimentally tested showing that it is sound and effective. 1

Abstract not found