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Ontologies are widely used in several areas with applications including knowledge Management, Web commerce and electronic business. An ontology provides a consensus of concept specifications for a specific domain shared by a group of people. In this paper we deal with Ontology Learning, specifically we aim to adapt the WordNet ontology, a general source of lexical knowledge, to the medical domain. We use for this task a combination of lexicosyntactic pattern, mainly conjunctions of the form "Noun_CJC_Noun", where CJC can be {and, or, but}. Pairs of words extracted in this fashion are compared to find their similarity in the WordNet noun hierarchy, using a form of the Resnik similarity method. Large scale experiments were conducted by extracting many such pairs of nouns from the Ohsumed corpus and mapping them into WordNet. For a noun pattern like "A or B" we find the lowest common ancestor of A and B by using the hypernym and hyponym links. This enables us to keep the appropriate medical sense of the two words A and B.

Intensive efforts are made in the Web services selection literature by these days mainly because of the overwhelming interest of the community in the services provided over the Web. In selecting best services, the non functional properties of such services are proven to be more and more important; these describe the Quality of service that is the key factor in the selection process.In this paper, we discuss a Web service selection based on both the context and the QoS ontology. We propose an architecture that makes an automatic selection of best service provider that is based on mixed context and QoS ontology for a given set of parameters of QoS. The key idea is to rely on multi dimensional QoS. Finally, some experiments are run so to demonstrate consistency and effectiveness of the proposed method.

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Abstract-This work presents a method that enables Arabic NLP community to build scalable lexical resources. The proposed method is low cost and efficient in time in addition to its scalability and extendibility. The latter is reflected in the ability for the method to be incremental in both aspects, processing resources and generating lexicons. Using a corpus; firstly, tokens are drawn from the corpus and lemmatized. Secondly, finite state transducers (FSTs) are generated semi-automatically. Finally, FSTs are used to produce all possible inflected verb forms with their full morphological features. Among the algorithm's strength is its ability to generate transducers having 184 transitions, which is very cumbersome, if manually designed. The second strength is a new inflection scheme of Arabic verbs; this increases the efficiency of FST generation algorithm. The experimentation uses a representative corpus of Modern Standard Arabic. The number of semi-automatically generated transducers is 171. The resulting open lexical resources coverage is high. Our resources cover more than 70% Arabic verbs. The built resources contain 16,855 verb lemmas and 11,080,355 fully, partially and not vocalized verbal inflected forms. All these resources are being made public and currently used as an open package in the Unitex framework available under the LGPL license.