Abstract. OntoTrack is a new browsing and editing “in-one-view” ontology authoring tool that combines a hierarchical graphical layout and instant reasoning feedback for (the most rational fraction of) OWL Lite. OntoTrack provides an animated and zoomable view with context sensitive features like click-able miniature branches or selective detail views together with drag-and-drop editing. Each editing step is instantly synchronized with an external reasoner in order to provide appropriate graphical feedback about relevant modeling consequences. The most recent feature of OntoTrack is an on demand textual explanation for subsumption and equivalence between or unsatisfiability of classes. This paper describes the key features of the current implementation and discusses future work as well as some development issues. 1

OntoTrack is an ontology authoring tool that combines a graph-based hierarchical layout and instant reasoning feedback within one single view. Currently OntoTrack can handle ontologies with an expressivity almost comparable to OWL Lite. The graphical representation provides an animated and zoomable subsumption graph with context sensitive features such as click-able miniature branches or selective detail views, together with drag-and-drop editing. Each editing step is instantly synchronised with an external reasoner in order to provide appropriate graphical feedback about relevant modelling consequences. A recent extention of OntoTrack provides an on-demand textual explanation for subsumption relationships between classes. This paper describes the key features of the current implementation and discusses future work, as well as some development issues. OntoTrack can be downloaded at

Abstract. The least common subsumer (lcs) of a set of concept descriptions is the most specific concept description that subsumes all of the concept descriptions in the given set. By computing the lcs, commonalities between concept descriptions can be made explicit. This is an important inference task useful in several applications, including, for instance, the bottom-up construction of description logic knowledge bases.

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

Similarity measures play a key role in the Semantic Web perspective. Indeed, most of the ontology related operations such as ontology learning, ontology alignment, ontology ranking and ontology population are grounded on the notion of similarity. In the last few years several similarity functions have been proposed for measuring both concept similarity and ontology similarity. However, they lack of a comprehensive formal characterization that is able to explain their behavior and value added, in particular when the ontologies are formulated in description logics languages like OWL-DL. Concept similarity functions need to be able to deal with the high expressive power of the ontology representation language, and to convey the underlying semantics of the ontology to which concepts refer. We propose a semantic similarity measure for complex Description Logics concept descriptions that elicits the underlying ontology semantics. Furthermore, we theorize a set of criteria that a measure has to satisfy in order to be compliant with a semantic expected behavior.

Abstract. Semantic service descriptions are frequently given using expressive ontology languages based on description languages. The expressiveness of these languages, however, often implies problems for efficient service discovery, especially when increasing numbers of services become available in large organizations and on the Web. To remedy this problem, we propose an efficient service discovery/retrieval method grounded on a conceptual clustering approach, where services are specified in Description Logics as class definitions [10] and they are retrieved by defining a class expression as a query and by computing the individual subsumption relationship between the query and the available descriptions. We present a new conceptual clustering method that constructs tree indices for clustered services, where available descriptions are the leaf nodes, while inner nodes are intensional descriptions (generalization) of their children nodes. The matchmaking is performed by following the tree branches whose nodes might satisfy the query. The query answering time may strongly improve, since the number of retrieval steps may decrease from O(n) to O(log n) for concise queries. We also show that the proposed method is sound and complete. 1

Least Common Subsumers (LCS) have been proposed in Description Logics (DL) to capture the commonalities between two or more concepts. Since its introduction in 1992, LCS have been successfully employed as a logical tool for a variety of applications, spanning from inductive learning, to bottom-up construction of knowledge bases, information retrieval, to name a few. The best known algorithm for computing LCS uses structural comparison on normal forms, and the most expressive DL it is applied to is ALEN. We provide a general tableau-based calculus for computing LCS, via substitutions on concept terms containing concept variables. We show the applicability of our method to an expressive DL (but without disjunction and full negation), discuss complexity issues, and show the generality of our proposal.

Abstract. This paper reports on extensions of the Description Logics non-standard inference system Sonic. The recent contributions to the system are two-fold. Firstly, Sonic is extended by two new of non-standard inferences, namely, implementations of the good common subsumer w.r.t. a background terminology and a heuristics for computing a minimal rewriting. Secondly, Sonic is available as a plugin for the wellknown ontology editor Protégé [11]. 1

Semantic service descriptions are frequently given using expressive ontology languages based on description languages. The expressiveness of these languages, however, often implies problems for efficient service discovery, especially when increasing numbers of services become available in large organizations and on the Web. To remedy this problem, we propose an efficient service discovery/retrieval method grounded on a conceptual clustering approach, where services are specified in Description Logics as class definitions [10] and they are retrieved by defining a class expression as a query and by computing the individual subsumption relationship between the query and the available descriptions. We present a new conceptual clustering method that constructs tree indices for clustered services, where available descriptions are the leaf nodes, while inner nodes are intensional descriptions (generalization) of their children nodes. The matchmaking is performed by following the tree branches whose nodes might satisfy the query. The query answering time may strongly improve, since the number of retrieval steps may decrease from O(n) to O(log n) for concise queries. We also show that the proposed method is sound and complete.

Abstract. The Description Logic EL has recently drawn considerable attention since, on the one hand, important inference problems such as the subsumption problem are polynomial. On the other hand, EL is used to define large biomedical ontologies. Unification in Description Logics has been proposed as a novel inference service that can, for example, be used to detect redundancies in ontologies. The main result of this paper is that unification in EL is decidable. More precisely, we show that EL-unification is NP-complete, and thus has the same complexity as EL-matching. 1