<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ali, Haider</style></author><author><style face="normal" font="default" size="100%">Shafait, Faisal</style></author><author><style face="normal" font="default" size="100%">Giannakidou, Eirini</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Figueroa, Nadia</style></author><author><style face="normal" font="default" size="100%">Varvadoukas, Theodoros</style></author><author><style face="normal" font="default" size="100%">Mavridis, Nikolaos</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Contextual object category recognition for RGB-D scene labeling</style></title><secondary-title><style face="normal" font="default" size="100%">Robotics and Autonomous Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">62</style></volume><pages><style face="normal" font="default" size="100%">241-256</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Giannakidou, Eirini</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Mavridis, Nikolaos</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Akerkar, Rajendra</style></author><author><style face="normal" font="default" size="100%">Bassiliades, Nick</style></author><author><style face="normal" font="default" size="100%">Davies, John</style></author><author><style face="normal" font="default" size="100%">Ermolayev, Vadim</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards a Framework for Social Semiotic Mining</style></title><secondary-title><style face="normal" font="default" size="100%">WIMS</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pages><style face="normal" font="default" size="100%">21</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4503-2538-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Giannakidou, Eirini</style></author><author><style face="normal" font="default" size="100%">Vassiliki A. Koutsonikola</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">In &amp; out zooming on time-aware user/tag clusters</style></title><secondary-title><style face="normal" font="default" size="100%">J. Intell. Inf. Syst.</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Events</style></keyword><keyword><style  face="normal" font="default" size="100%">Social tagging systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Time-aware clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">Users' interests over time</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">38</style></volume><pages><style face="normal" font="default" size="100%">685-708</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The common ground behind most approaches that analyze social taggingsystems is addressing the information challenge that emerges from the massiveactivity of millions of users who interact and share resources and/or metadata online.However, lack of any time-related data in the analysis process implicitly deniesmuch of the dynamic nature of social tagging activity. In this paper we claim thatholding a temporal dimension, allows for tracking macroscopic and microscopicusersâ€™ interests, detecting emerging trends and recognizing events. To this end, wepropose a time-aware co-clustering approach for acquiring semantic and temporalpatterns out of the tagging activity. The resulted clusters contain both users and tagsof similar patterns over time, and reveal non-obvious or â€śhiddenâ€ť relations amongusers and topics of their common interest. Zoom in &amp;amp; out views serve as visualizationmethods on different aspects of the clustersâ€™ structure, in order to evaluate theefficiency of the approach.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nikolopoulos, Spiros</style></author><author><style face="normal" font="default" size="100%">Giannakidou, Eirini</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author><author><style face="normal" font="default" size="100%">Patras, Ioannis</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Hoi, Steven C. H.</style></author><author><style face="normal" font="default" size="100%">Luo, Jiebo</style></author><author><style face="normal" font="default" size="100%">Boll, Susanne</style></author><author><style face="normal" font="default" size="100%">Xu, Dong</style></author><author><style face="normal" font="default" size="100%">Jin, Rong</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Combining Multi-modal Features for Social Media Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Social Media Modeling and Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">71-96</style></pages><isbn><style face="normal" font="default" size="100%">978-0-85729-435-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nikolopoulos, Spiros</style></author><author><style face="normal" font="default" size="100%">Chatzilari, Elisavet</style></author><author><style face="normal" font="default" size="100%">Giannakidou, Eirini</style></author><author><style face="normal" font="default" size="100%">Symeon Papadopoulos</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Bessis, Nik</style></author><author><style face="normal" font="default" size="100%">Xhafa, Fatos</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Leveraging Massive User Contributions for Knowledge Extraction</style></title><secondary-title><style face="normal" font="default" size="100%">Next Generation Data Technologies for Collective Computational Intelligence</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Studies in Computational Intelligence</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">352</style></volume><pages><style face="normal" font="default" size="100%">415-443</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-20343-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Giannakidou, Eirini</style></author><author><style face="normal" font="default" size="100%">Vassiliki A. Koutsonikola</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exploring temporal aspects in user-tag co-clustering</style></title><secondary-title><style face="normal" font="default" size="100%">WIAMIS</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">1-4</style></pages><isbn><style face="normal" font="default" size="100%">978-88-905328-0-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Tagging environments have become an interesting topic ofresearch lately, focused mainly on clustering approaches, inorder to extract emergent patterns that are derived from tagsimilarity and involve tag relations or user interconnections.Apart from tag similarity, an interesting parameter to be analyzedduring the clustering/mining process in such data isthe actual time that each tagging activity occurred. Indeed,holding a temporal dimension unfolds macroscopic and microscopicviews of tagging, highlights links between objectsfor specific time periods and, in general, lets us observe howthe usersâ€™ tagging activity changes over time. In this article,we propose a time-aware user/tag clustering approach, whichgroups together similar users and tags that are very â€śactiveâ€ťduring the same time periods. Emphasis is given on usingvarying time scales, so that we distinguish between clustersthat are robust at many time scales and clusters that are somehowoccasional, i.e. they emerge, only at a specific time period.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Giannakidou, Eirini</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Auer, S'oren</style></author><author><style face="normal" font="default" size="100%">Decker, Stefan</style></author><author><style face="normal" font="default" size="100%">Hauswirth, Manfred</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Integrating Web 20 Data into Linked Open Data Cloud via Clustering</style></title><secondary-title><style face="normal" font="default" size="100%">CEUR Workshop Proceedings ISSN 1613-0073</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">FIA-LOD2010 imported</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">February</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">700</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stampouli, Anastasia</style></author><author><style face="normal" font="default" size="100%">Giannakidou, Eirini</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Yoshikawa, Masatoshi</style></author><author><style face="normal" font="default" size="100%">Meng, Xiaofeng</style></author><author><style face="normal" font="default" size="100%">Yumoto, Takayuki</style></author><author><style face="normal" font="default" size="100%">Ma, Qiang</style></author><author><style face="normal" font="default" size="100%">Sun, Lifeng</style></author><author><style face="normal" font="default" size="100%">Watanabe, Chiemi</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Tag Disambiguation through Flickr and Wikipedia</style></title><secondary-title><style face="normal" font="default" size="100%">DASFAA Workshops</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">DBpedia project</style></keyword><keyword><style  face="normal" font="default" size="100%">flick</style></keyword><keyword><style  face="normal" font="default" size="100%">mashup</style></keyword><keyword><style  face="normal" font="default" size="100%">term disambiguation</style></keyword><keyword><style  face="normal" font="default" size="100%">Wikipedia</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">6193</style></volume><pages><style face="normal" font="default" size="100%">252-263</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-14588-9</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Given the popularity of social tagging systems and the limitationsthese systems have, due to lack of any structure, a common issue that arises involves the low retrieval quality in such systems due to ambiguities of certain terms. In this paper, an approach for improving the retrieval in these systems, in case of ambiguous terms, is presented that attempts to perform tag disambiguation and, at the same time, provide users with relevant content. The idea is based on a mashup that combines data and functionality of two major web 2.0 sites, namely Flickr and Wikipedia and aims at enhancing content retrieval for web users. A case study with the ambiguous notion â€śAppleâ€ť illustrates the value of the proposed approach.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vassiliki A. Koutsonikola</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Giannakidou, Eirini</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Vossen, Gottfried</style></author><author><style face="normal" font="default" size="100%">Long, Darrell D. E.</style></author><author><style face="normal" font="default" size="100%">Yu, Jeffrey Xu</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Clustering of Social Tagging System Users: A Topic and Time Based Approach</style></title><secondary-title><style face="normal" font="default" size="100%">WISE</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Social tagging systems</style></keyword><keyword><style  face="normal" font="default" size="100%">time</style></keyword><keyword><style  face="normal" font="default" size="100%">topic</style></keyword><keyword><style  face="normal" font="default" size="100%">user clustering</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5802</style></volume><pages><style face="normal" font="default" size="100%">75-86</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-04408-3</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Under Social Tagging Systems, a typical Web 2.0 application,users label digital data sources by using freely chosen textual descriptions(tags). Mining tag information reveals the topic-domain ofusers interests and significantly contributes in a profile construction process.In this paper we propose a clustering framework which groups usersaccording to their preferred topics and the time locality of their taggingactivity. Experimental results demonstrate the efficiency of the proposedapproach which results in more enriched time-aware users profiles.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Giannakidou, Eirini</style></author><author><style face="normal" font="default" size="100%">Vassiliki A. Koutsonikola</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Co-Clustering Tags and Social Data Sources</style></title><secondary-title><style face="normal" font="default" size="100%">WAIM</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">317-324</style></pages><isbn><style face="normal" font="default" size="100%">978-0-7695-3185-4</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Under social tagging systems, a typical Web 2.0 application,users label digital data sources by using freely chosentextual descriptions (tags). Poor retrieval in the aforementionedsystems remains a major problem mostly due toquestionable tag validity and tag ambiguity. Earlier clusteringtechniques have shown limited improvements, since theywere based mostly on tag co-occurrences. In this paper,a co-clustering approach is employed, that exploits jointgroups of related tags and social data sources, in whichboth social and semantic aspects of tags are consideredsimultaneously. Experimental results demonstrate the effi-ciency and the beneficial outcome of the proposed approachin correlating relevant tags and resources.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Giannakidou, Eirini</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SEMSOC: SEMantic, SOcial and Content-Based Clustering in Multimedia Collaborative Tagging Systems</style></title><secondary-title><style face="normal" font="default" size="100%">ICSC</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE Computer Society</style></publisher><pages><style face="normal" font="default" size="100%">128-135</style></pages><isbn><style face="normal" font="default" size="100%">978-0-7695-3279-0</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>