01808nas a2200181   4500008004100000245009800041210006900139260000900208300001400217490000700231520117600238653003001414653002301444653001201467100001501479700001501494856011701509       2012                        eng d00aExploring lexicographic ontologies for hierarchically organizing the greek wikipedia articles0 aExploring lexicographic ontologies for hierarchically organizing  c2012  a157 - 1670 v103 aTo effectively manage the proliferating online content, it is imperative that we come up with efficient data structuring and organization methods. Based on the findings of previous research [6] [7] that the most flexible and useful way to organize the online content is via the use of taxonomies and/or ontologies, we carried out the present study, which aims at structuring the content of the Greek Wikipedia via the use of the Greek WordNet. In particular, our study objective is to design a model that can automatically organize the Greek Wikipedia categories into a thematic taxonomy and based on the derived organization, to implicitly assign hierarchical structure to the encyclopedia articles that have been classified to the respective categories. To this end, we relied on the data encoded in Greek WordNet out of which we harvested the hierarchical relations that hold between the terms used to verbalize the Wikipedia categories. The effectiveness of our model is verified by the findings of several experimental evaluations conducted, which demonstrate that semantic networks are powerful resources for hierarchically organizing large volumes of dynamic data.10aHierarchical organization10aWikipedia articles10aWordNet1 aNiarou, M.1 aStamou, S.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84866437570&partnerID=40&md5=f78ff732a8358e3c52247f353ea4acc201994nas a2200181   4500008004100000245006700041210006600108260000900174300001400183490000600197520137800203653002101581653004601602653001801648653001201666100001701678856011701695       2007                        eng d00aPersonalized searching by learning wordnet-based user profiles0 aPersonalized searching by learning wordnetbased user profiles  c2007  a309 - 3220 v53 aThe amount of information available on the Web and in Digital Libraries is increasing over time. In this context, the role of user modeling and personalized information access is becoming crucial: Users need a personalized support in sifting through large amounts of retrieved information according to their interests. Information filtering and retrieval systems relying on this idea adapt their behavior to individual users by learning their preferences during the interaction in order to construct a profile of the user that can be later exploited in the search process. We propose a novel technique to learn user profiles which exploits word sense disambiguation based on the WordNet lexical database, in an attempt to produce semantic user profiles that might discover topics semantically closer to the user interests. Semantic profiles are used in the definition of a retrieval model that turns the traditional document-query search paradigm into a novel document-query-profile paradigm. As an example of this paradigm, we present an extension of the vector space model in which profiles are used to modify the ranking of search results obtained in response to a query, hopefully putting personally relevant items on the top of the result list. Experimental results in a movie retrieval scenario indicate that the proposed model to personalize Web search is effective.10aMachine learning10aPersonalization and information filtering10aUser modeling10aWordNet1 aSemeraro, G.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-66149191527&partnerID=40&md5=750d800edd5f7fe76d849dd465dafff901610nas a2200229   4500008004100000245005700041210005700098260000900155300001400164490000600178520091100184653002901095653001301124653001701137653001701154653003001171653001201201100001701213700001601230700001701246856011701263       2006                        eng d00aUsing BFA with wordnet based model for web retrieval0 aUsing BFA with wordnet based model for web retrieval  c2006  a107 - 1110 v43 aIn the area of information retrieval, the dimension of document vectors plays an important role. We may need to find a few words or concepts, which characterize the document based on its contents, to overcome the problem of the "curse of dimensionality", which makes indexing of high-dimensional data problematic. To do so, we earlier proposed a Wordnet and Wordnet+LSI (Latent Semantic Indexing) based model for dimension reduction. While LSI works on the whole collection, another procedure of feature extraction (and thus dimension reduction) exists, using binary factorization. The procedure is based on the search of attractors in Hopfield-like associative memory. Being applied to textual data the procedure conducted well and even more it showed sensitivity to the context in which the words were used. In this paper, we suggest that the binary factorization may benefit from the Wordnet filtration.10aLatent semantic indexing10aOntology10aSemantic web10aVector model10aWeb information retrieval10aWordNet1 aSnášel, V.1 aMoravec, P.1 aPokorný, J.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-44649186320&partnerID=40&md5=84c389a9e2d0c1be0cd8ecdb6b1534ec01899nas a2200229   4500008004100000245009100041210006900132260000900201300001400210490000600224520117600230653002101406653000901427653001301436653001401449653001201463100002101475700001601496700002001512700002001532856011701552       2006                        eng d00aX-Similarity: Computing Semantic Similarity between concepts from different ontologies0 aXSimilarity Computing Semantic Similarity between concepts from   c2006  a233 - 2370 v43 aSemantic Similarity relates to computing the similarity between concepts (terms) which are not necessarily lexically similar. We investigate approaches for computing semantic similarity by mapping terms to an ontology and by examining their relationships in that ontology. More specifically, we investigate approaches to computing the semantic similarity between natural language terms (using WordNet as the underlying reference ontology) and between medical terms (using the MeSH ontology of medical and biomedical terms). The most popular semantic similarity methods are implemented and evaluated using WordNet and MeSH. The focus of this work is also on cross ontology methods which are capable of computing the semantic similarity between terms stemming from different ontologies (WordNet and MeSH in this work). This is a far more difficult problem (than the single ontology one referred to above) which has not been investigated adequately in the literature. X-Similarity, a novel cross-ontology similarity method is also a contribution of this work. All methods examined in this work are integrated into a semantic similarity system which is accessible on the Web.10aContent analysis10aMeSH10aOntology10aSemantics10aWordNet1 aPetrakis, E.G.M.1 aVarelas, G.1 aHliaoutakis, A.1 aRaftopoulou, P.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-33845952568&partnerID=40&md5=5bd213d7f778b7c4e7b32133f4b456f8