By Jonathan Gemmell, Thomas Schimoler, Bamshad Mobasher, Robin Burke (auth.), Francesco Buccafurri, Giovanni Semeraro (eds.)
After the lesson discovered in the course of final years and following the profitable variation of EC-Web 2009, for its eleventh variation EC-Web attempted to supply a clearer description of the digital trade universe targeting a few suitable issues. the main target was once not just on Internet-related recommendations and methods. the purpose of EC-Web 2010 was once to additionally disguise features relating to theoretical foundations of e-commerce, enterprise techniques in addition to new techniques exploiting lately emerged applied sciences and eventualities equivalent to the Semantic internet, net providers, SOA architectures, cellular and ubiquitous computing, simply to cite a number of. because of their critical function in any life like e-commerce infrastructure, protection and privateness matters have been greatly thought of, with no aside from felony and regulatory points. the alternative of the above correct issues at once displays the truth that digital trade (EC), within the previous few years, has replaced and developed right into a well-established and based truth either from a technological standpoint and from a systematic one. however, including its evolution, new demanding situations and subject matters have emerged in addition to new questions were raised with regards to many facets of EC. conserving in brain the event of the final version of EC-Web, we maintained, for its eleventh variation, the constitution and the clinical association of EC-Web 2009, aiming to spotlight the self sustaining function of the various (sometimes heterogeneous) facets of EC, with out lacking their interdisciplinary scope.
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Extra resources for E-Commerce and Web Technologies: 11th International Conference, EC-Web 2010, Bilbao, Spain, September 1-3, 2010. Proceedings
We run the content-based (respectively, the collaborative) tag extraction algorithm when the collaborative (respectively, the content-based) one is not able to return any tag. As regards the collaborative model, this happens when no similar documents are retrieved by querying the corpus containing previously annotated resources, while in the content-based model, this happens when the HTML source does not include any meta-tag or when the words contained in the set of Content-based Candidate Tags do not occur in the HTML source code.
Moreover, when it queries external information sources, instead of the forp 1 ,uri2 mula (1), it uses the co-occurrence formula: puri +puri uri2 −puri1 ,uri2 1 Algo5 is equivalent to Algo4, but it uses similarity distance  instead of co-occurrence. We did not choose to use either co-occurrence or similarity distance in DBpediaRanker since they do not work well when one of the two resources is extremely more popular than the other, while formula (1) allows to catch this situation. In order to assess the quality of our algorithm we conducted a study where we asked to participants to rate the results returned by each algorithm.
Moreover, each concept in DBpedia is referred by its own URI. This allows to precisely get a resource with no ambiguity. For example, the American corporation Google Inc. org/resource/Barney_Google_and_Snuffy_Smith. The main idea behind our approach is the following: keywords can be mapped to corresponding DBpedia resources. After this mapping, we are able to associate a well deﬁned semantics to keywords and we can enrich the “meaning” of the keywords by exploiting the ontological nature of DBpedia.