Abstract. While the increasing popularity of folksonomies has lead to a vast quantity of tagged data, resource retrieval in folksonomies is limited by being agnostic to the meaning (i.e., semantics) of tags. Our goal is to automatically enrich folksonomy tags (and implicitly the related resources) with formal semantics by associating them to relevant concepts defined in online ontologies. We introduce FLOR, a method that performs automatic folksonomy enrichment by combining knowledge from WordNet and online available ontologies. Experimentally testing FLOR, we found that it correctly enriched 72 % of 250 Flickr photos. 1

Abstract. A new paradigm in Semantic Web research focuses on the development of a new generation of knowledge-based problem solvers, which can exploit the massive amounts of formally specified information available on the Web, to produce novel intelligent functionalities. An important example of this paradigm can be found in the area of Ontology Matching, where new algorithms, which derive mappings from an exploration of multiple and heterogeneous online ontologies, have been proposed. While these algorithms exhibit very good performance, they rely on merely syntactical techniques to anchor the terms to be matched to those found on the Semantic Web. As a result, their precision can be affected by ambiguous words. In this paper, we aim to solve these problems by introducing techniques from Word Sense Disambiguation, which validate the mappings by exploring the semantics of the ontological terms involved in the matching process. Specifically we discuss how two techniques, which exploit the ontological context of the matched and anchor terms, and the information provided by WordNet, can be used to filter out mappings resulting from the incorrect anchoring of ambiguous terms. Our experiments show that each of the proposed disambiguation techniques, and even more their combination, can lead to an important increase in precision, without having too negative an impact on recall.

Abstract. Ontology matching, the task of determining relations that hold among terms of two different ontologies, is a key issue in the Semantic Web and other related fields. In order to compare the behaviour of different ontology matching systems, the Ontology Alignment Evaluation Initiative (OAEI) has established a periodical controlled evaluation that comes in a yearly event. We present here our participation in the 2008 initiative. Our schema-based alignment algorithm compares each pair of ontology terms by, firstly, extracting their ontological contexts up to a certain depth (enriched by using transitive entailment) and, secondly, combining different elementary ontology matching techniques (e.g., lexical distances and vector space modelling). Benchmark results show a very good behaviour in terms of precision, while preserving an acceptable recall. Based on our experience, we have also included some remarks about the nature of benchmark test cases that, in our opinion, could help improving the OAEI tests in the future. 1 Presentation of the system In [7] we presented a system that analyzes a keyword-based user query, in order to automatically extract and make explicit, without ambiguities, its semantics. Firstly, it discovers and extracts candidate senses (expressed as ontology terms)

Abstract. Semantic relatedness measures quantify the degree in which some words or concepts are related, considering not only similarity but any possible semantic relationship among them. Relatedness computation is of great interest in different areas, such as Natural Language Processing, Information Retrieval, or the Semantic Web. Different methods have been proposed in the past; however, current relatedness measures lack some desirable properties for a new generation of Semantic Web applications: maximum coverage, domain independence, and universality. In this paper, we explore the use of a semantic relatedness measure between words, that uses the Web as knowledge source. This measure exploits the information about frequencies of use provided by existing search engines. Furthermore, taking this measure as basis, we define a new semantic relatedness measure among ontology terms. The proposed measure fulfils the above mentioned desirable properties to be used on the Semantic Web. We have tested extensively this semantic measure to show that it correlates well with human judgment, and helps solving some particular tasks, as word sense disambiguation or ontology matching.

Abstract. Organizations working in a multilingual environment demand multilingual ontologies. To solve this problem we propose LabelTranslator, a system that automatically localizes ontologies. Ontology localization consists of adapting an ontology to a concrete language and cultural community. LabelTranslator takes as input an ontology whose labels are described in a source natural language and obtains the most probable translation into a target natural language of each ontology label. Our main contribution is the automatization of this process which reduces human efforts to localize an ontology manually. First, our system uses a translation service which obtains automatic translations of each ontology label (name of an ontology term) from/into English, German, or Spanish by consulting different linguistic resources such as lexical databases, bilingual dictionaries, and terminologies. Second, a ranking method is used to sort each ontology label according to similarity with its lexical and semantic context. The experiments performed in order to evaluate the quality of translation show that our approach is a good approximation to automatically enrich an ontology with multilingual information.

Abstract. With the large majority of existing matching systems focusing on deriving equivalence mappings, OAEI has been primarily focused on assessing such kind of relations. As the field inevitably advances towards the discovery of more complex mappings, the contest will need to reflect such changes as well. In this paper we present Spider, a system that provides alignments containing not only equivalence mappings, but also a variety of different mapping types (namely, subsumption, disjointness and named relations). Our goal is both to get an insight into the functioning of our system and, more importantly, to assess the current support for dealing with non-equivalence mappings in the OAEI contest. We hope that our observations will contribute to further enhance the procedure of the contest. 1 Presentation of the system 1.1 State, purpose, general statement Our purpose was to investigate two concrete issues related to non-equivalence mappings. 1. Do non-equivalence mappings bring a good addition to alignments made up

Abstract. This demo proposal briefly presents LabelTranslator, a system that suggests translations of ontology labels, with the purpose of localizing ontologies. LabelTranslator takes as input an ontology whose labels are described in a source natural language and obtains the most probable translation of each ontology label into a target natural language. Our main contribution is the automatization of this process, which reduces human efforts to localize manually the ontology.

Nowadays, the increasing amount of semantic data available on the Web leads to a new stage in the potential of Semantic Web applications. However, it also introduces new issues due to the heterogeneity of the available semantic resources. One of the most remarkable is redundancy, that is, the excess of different semantic descriptions, coming from different sources, to describe the same intended meaning. In this paper, we propose a technique to perform a large scale integration of senses (expressed as ontology terms), in order to cluster the most similar ones, when indexing large amounts of online semantic information. It can dramatically reduce the redundancy problem on the current Semantic Web. In order to make this objective feasible, we have studied the adaptability and scalability of our previous work on sense integration, to be translated to the much larger scenario of the Semantic Web. Our evaluation shows a good behaviour of these techniques when used in large scale experiments, then making feasible the proposed approach.

Abstract. The usability and the strong social dimension of the Web2.0 applications has encouraged users to create, annotate and share their content thus leading to a rich and content-intensive Web. Despite that, the Web2.0 content lacks the explicit semantics that would allow it to be used in large-scale intelligent applications. At the same time the advances in Semantic Web technologies imply a promising potential for intelligent applications capable to integrate distributed content and knowledge from various heterogeneous resources. We present FLOR a tool that performs semantic enrichment of folksonomy tagspaces by exploiting online ontologies, thesauri and other knowledge sources. 1 Background and Research Problem The large-scale content annotation and metadata generation has been realised as Web2.0 applications have become very popular. Despite that, Web2.0 lacks the explicit semantics that would allow the content to be used in large-scale intelligent applications. At the same time the advances in Semantic Web technologies

The ability of computers to automatically determine the right sense of words, according to the context where they appear, can help bridge the gap between syntax and semantics required for the full development of the Semantic Web. However, the applicability of these techniques is sometimes hampered by the unrestricted way in which humans annotate web resources, especially in folksonomies. In such cases many context words are useless (or even harmful) to determine the right meaning of another one. Furthermore, these contexts lack well-formed sentences, thus preventing syntactic analysis and other features exploited by traditional disambiguation techniques from being used. In this paper we propose a technique for intelligent context selection, based on semantic relatedness computation, to detect the set of words that could induce an effective disambiguation. We use this technique as starting point of a disambiguation process that receives an