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Abstract. Measuring similarity between ontologies can be very useful for different purposes, e.g., finding an ontology to replace another, or finding an ontology in which queries can be translated. Classical measures compute similarities or distances in an ontology space by directly comparing the content of ontologies. We introduce a new family of ontology measures computed in an alignment space: they evaluate the similarity between two ontologies with regard to the available alignments between them. We define two sets of such measures relying on the existence of a path between ontologies or on the ontology entities that are preserved by the alignments. The former accounts for known relations between ontologies, while the latter reflects the possibility to perform actions such as instance import or query translation. All these measures have been implemented in the OntoSim library, that has been used in experiments which showed that entity preserving measures are comparable to the best ontology space measures. Moreover, they showed a robust behaviour with respect to the alteration of the alignment space.

Abstract. Concept naming over the taxonomic structure is a useful indicator of the quality of design as well as source of information exploitable for various tasks such as ontology refactoring and mapping. We analysed collections of OWL ontologies with the aim of determining the frequency of several combined name&graph patterns potentially indicating underlying semantic structures. Such structures range from simple set-theoretic subsumption to more complex constructions such as parallel taxonomies of different entity types. The final goal is to help refactor legacy ontologies as well as to ease automatic alignment among different models. The results show that in most ontologies there is a significant number of occurrences of such patterns. Moreover, their detection even using very simple methods has precision sufficient for a semi-automated analysis scenario.

Abstract. In this paper we describe a web-based tool that supports the human in revising ontology alignments. Our tool uses logical reasoning as a basis for detecting conflicts in mappings and exploits these conflicts to propagate user decision. The proposed approach reduces the effort of the human expert and points to logical problems that are hard to find without support. MotivationThe alignment of ontologies is a common problem on the semantic web as many tasks such as information integration or semantic search rely on integrated background knowledge. As manual ontology alignment is a difficult and timeconsuming process, a variety of algorithms and systems for computing matches between elements of different ontologies have been developed [2]. Almost all of these methods rely on linguistic or structural heuristics for deciding whether to regard elements from different ontologies as equivalent or not. These heuristics are bound to fail in many situations resulting in erroneous mappings that need to be corrected manually. As it turns out, this manual correction is a very difficult task that requires a good understanding of and sufficient information about the ontologies to be aligned. Motivated by this experience gained from five years of ontology alignment benchmarking carried out in the context of the Ontology Alignment Evaluation Initiative (OAEI) [1] we developed a web-based tool that supports the human user when evaluating a mapping. The distinguishing feature of this tool is the use of logical reasoning as a basis for detecting conflicts in mappings and implications of evaluation decisions. As shown in previous work [5] this approach can significantly reduce the effort of a manual evaluation and as argued in [4] also provides the basis for improving the correctness of mappings. In the following we first briefly discuss the kind of reasoning performed by our system. We then describe our system and present a typical usage example. We close with a brief description of the systems used in the context of the OAEI and some discussion of future work. 1 1 A demo version applied to the use case of Section 4 can be found at

Abstract. As more and more ontology designers follow the patternbased approach, automatic analysis of those structures and their exploitation in semantic tools is becoming more doable and important. We present an approach to ontology transformation based on transformation patterns, which could assist in many semantic tasks (such as reasoning, modularisation or matching). Ontology transformation can be applied on parts of ontologies called ontology patterns. Detection of ontology patterns can be specific for a given use case, or generic. We first present generic detection patterns along with some experimental results, and then detection patterns specific for ontology matching. Furthermore, we detail the ontology transformation phase along with an example of transformation pattern based on an alignment pattern.

The high expressivity of the OWL ontology language often allows to express the same conceptualisation in different ways. A simple example is the difference between 'class-centric' and 'property-centric' modelling styles, such that the same notion is modelled as a class in the former (e.g. 'Purchase') and an object property in the latter (e.g. 'bought from'). Similarly, concept subordination can be expressed via a subclass hierarchy or via individuals connected by a dedicated property (as in SKOS). Such heterogeneity is an obstacle to reusing ontologies in advanced semantic web scenarios. In particular (as mentioned in [1]), two ontologies modelled in different styles are difficult to match or to import into one another. Furthermore, opting for a style when designing an ontology may have an impact on the applicability and performance of reasoners, as some features cause performance problems for certain reasoners. Finally, human users may also prefer viewing ontologies in a certain form, possibly 'folding' parts of their complexity. Semi-automatic transformation of the modelling style of existing ontologies, with the help of tools to be presented in the demo, will alleviate such problems. In the paper we first overview the core framework and then focus on useroriented tools that allow to perform ontology transformation and edit transformation patterns in a friendly way.

a b s t r a c tIn this paper we present the MultiFarm dataset, which has been designed as a benchmark for multilingual ontology matching. The MultiFarm dataset is composed of a set of ontologies translated in different languages and the corresponding alignments between these ontologies. It is based on the OntoFarm dataset, which has been used successfully for several years in the Ontology Alignment Evaluation Initiative (OAEI). By translating the ontologies of the OntoFarm dataset into eight different languages -Chinese, Czech, Dutch, French, German, Portuguese, Russian, and Spanish -we created a comprehensive set of realistic test cases. Based on these test cases, it is possible to evaluate and compare the performance of matching approaches with a special focus on multilingualism.

This paper reports on the first usage of the MultiFarm dataset for evaluating ontology matching systems. This dataset has been designed as a comprehensive benchmark for multilingual ontology matching. In this first set of experiments, we analyze how state-of-the-art matching systems -not particularly designed for the task of multilingual ontology matching -perform on this dataset. Our experiments show the hardness of MultiFarm and result in baselines for any algorithm specifically designed for multilingual ontology matching. Moreover, this first reporting allows us to draw relevant conclusions for both multilingual ontology matching and ontology matching evaluation in general.