http://repub.eur.nl/res/aut/24718/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/39916/ 2013-09-01T00:00:00Z This paper proposes a semantic Web service discovery framework for finding semantic Web services by making use of natural language processing techniques. The framework allows searching through a set of semantic Web services in order to find a match with a user query consisting of keywords. By specifying the search goal using keywords, end-users do not need to have knowledge about semantic languages, which makes it easy to express the desired semantic Web services. For matching keywords with semantic Web service descriptions given in WSMO, techniques like part-of-speech tagging, lemmatization, and word sense disambiguation are used. After determining the senses of relevant words gathered from Web service descriptions and the user query, a matching process takes place. The performance evaluation shows that the three proposed matching algorithms are able to effectively perform matching and approximate matching. http://repub.eur.nl/res/pub/37692/ 2012-02-22T00:00:00Z The Semantic Web aims to extend the World Wide Web with a layer of semantic information, so that it is understandable not only by humans, but also by computers. At its core, the Semantic Web consists of ontologies that describe the meaning of concepts in a certain domain or across domains. The domain ontologies are mostly created and maintained by domain experts using manual, time-intensive processes. In this paper, we propose a rule-based method for learning ontology instances from text that helps domain experts with the ontology population process. In this method we define a lexico-semantic pattern language that, in addition to the lexical and syntactical information present in lexico-syntactic rules, also makes use of semantic information. We show that the lexico-semantic patterns are superior to lexico-syntactic patterns with respect to efficiency and effectivity. When applied to event relation recognition in text-based news items in the domains of finance and politics using Hermes, an ontology-driven news personalization service, our approach has a precision and recall of approximately 80% and 70%, respectively. http://repub.eur.nl/res/pub/31385/ 2011-07-26T00:00:00Z When recommending news items, most of the traditional algorithms are based on TF-IDF, i.e., a term-based weighting method which is mostly used in information retrieval and text mining. However, many new technologies have been made available since the introduction of TF-IDF. This paper proposes a new method for recommending news items based on TF-IDF and a domain ontology. It is demonstrated that adapting TF-IDF with the semantics of a domain ontology, resulting in Concept Frequency - Inverse Document Frequency (CF-IDF), yields better results than using the original TF-IDF method. CF-IDF is built and tested in Athena, a recommender extension to the Hermes news personalization framework. Athena employs a user profile to store concepts or terms found in news items browsed by the user. The framework recommends new articles to the user using a traditional TF-IDF recommender and the CF-IDF recommender. A statistical evaluation of both methods shows that the use of an ontology significantly improves the performance of a traditional recommender. http://repub.eur.nl/res/pub/26653/ 2011-07-18T00:00:00Z Due to the increasing popularity of tagging, it is important to overcome challenges resulting from the free nature of tagging, such as the use of synonyms, homonyms, syntactic variations, etc. The Semantic Tag Clustering Search (STCS) framework deals with these challenges by detecting syntactic variations of tags and by clustering semantically related tags. We evaluate our framework using Flickr data from 2009 and compare the STCS framework to two previously introduced tag clustering techniques. We conclude that our framework performs significantly better in terms of cluster precision compared to one method and has a better average precision compared to the other method. http://repub.eur.nl/res/pub/26606/ 2011-06-23T00:00:00Z One of the latest developments for the Semantic Web is Google Rich Snippets, a service that uses Web page annotations for displaying search results in a visually appealing manner. In this paper we propose the Automatic Review Recognition and annOtation of Web pages (ARROW) framework, which is able to identify reviews on Web pages and to annotate them using RDFa attributes. The ARROW framework consists of four steps: hotspot identification, subjectivity analysis, information extraction, and page annotation. We evaluate an implementation of the framework by using various Web sites. Based on the evaluation we conclude that our framework is able to properly identify the majority of reviews, reviewed items, and review dates. http://repub.eur.nl/res/pub/26607/ 2011-06-23T00:00:00Z Many of the existing cloud tagging systems are unable to cope with the syntactic and semantic tag variations during user search and browse activities. As a solution to this problem, in this paper, we propose the Semantic Tag Clustering Search, a framework able to cope with these needs. The framework consists of three parts: removing syntactic variations, creating semantic clusters, and utilizing the obtained clusters to improve search and exploration of tag spaces. For removing syntactic variations, we use the normalized Levenshtein distance, and the cosine similarity measure based on tag co-occurrences. For creating semantic clusters, we improve an existing non-hierarchical clustering technique. Using our framework, we are able to find more clusters and achieve a higher precision than the original method. The advantages of a cluster-based approach for searching and browsing through tag spaces have been exploited in Xplore-Flickr.com, the implementation of our framework. http://repub.eur.nl/res/pub/23889/ 2010-12-01T00:00:00Z The Region Connection Calculus based on 8 relations (RCC8) is one of several extensively researched methods to use for qualitative spatial representation and reasoning. We discuss several issues arising when representing RCC8 in OWL DL, a decidable fragment of OWL. There is no direct encoding of such a calculus in OWL DL, as the language lacks required features such as role reflexivity, role Boolean operators, and role inclusion axioms. Some of these features are to be included in the new version of the OWL standard, OWL 2, but this language still lacks the expressive power to support role negations, conjunctions, disjunctions, and complex role inclusion axioms. Recently, advances in description logics languages as SROIQBs have made possible expressing some of the above constructs, while maintaining the decidability of the language. In this paper, we exploit these new opportunities by providing qualitative spatial knowledge representation on the Semantic Web. http://repub.eur.nl/res/pub/23890/ 2010-12-01T00:00:00Z Many of the existing cloud tagging systems are unable to cope with the syntactic and semantic tag variations during user search and browse activities. As a solution to this problem, we propose the Semantic Tag Clustering Search, a framework which is able to cope with these needs. The framework consists of two parts: removing syntactic variations and creating semantic clusters. For removing syntactic variations, we use the normalized Levenshtein distance and the cosine similarity measure based on tag co-occurrences. For creating semantic clusters, we improve an existing non-hierarchical clustering technique. Using our framework, we are able to find more clusters and achieve a higher precision than the original method. http://repub.eur.nl/res/pub/20929/ 2010-08-26T00:00:00Z Recommending news items is traditionally done by term-based algorithms like TF-IDF. This paper concentrates on the benefits of recommending news items using a domain ontology instead of using a term-based approach. For this purpose, we propose Athena, which is an extension to the existing Hermes framework. Athena employs a user profile to store terms or concepts found in news items browsed by the user. Based on this information, the framework uses a traditional method based on TF-IDF, and several ontology-based methods to recommend new articles to the user. The paper concludes with the evaluation of the different methods, which shows that the new ontology-based method that we propose in this paper performs better (w.r.t. accuracy, precision, and recall) than the traditional method and, with the exception of one measure (recall), also better than the other considered ontology-based approaches. http://repub.eur.nl/res/pub/20545/ 2010-07-23T00:00:00Z Hermes is an ontology-based framework for building news personalization services. This framework consists of a news classification phase, which classifies the news, a knowledge base updating phase, which keeps the knowledge base up-to-date, a news querying phase, allowing the user to search the news for concepts of interest, and a results presentation phase, showing the returned news items. The focus of this paper is on how to keep the knowledge base up-to-date. For this purpose, we elaborate on the updating phase that searches for key events in the news. Using rules based on patterns and actions, these events can be extracted and the knowledge base is updated. This is a semi-automatic process since user validation is required before updating the knowledge base. http://repub.eur.nl/res/pub/20580/ 2010-07-23T00:00:00Z This paper investigates several approaches for qualitative spatial knowledge representation on the Semantic Web, by using RCC8 relations. We discuss several issues arising when representing RCC8 in OWL DL, e.g., the lack of required features like role reflexivity, role Boolean operators, and role inclusion axioms. We conclude that, although some of these features are to be included in the new version of the OWL standard, OWL 2, this language still lacks the expressive power to support role negations, conjunctions, and disjunctions, and complex role inclusion axioms.