http://www.bibsonomy.org/burst/user/jaeschke/iccs_exampleBibSonomy BuRST Feed for /user/jaeschke/iccs_example2008-07-21T01:08:04+02:00http://www.bibsonomy.org/bibtex/2804ad41798bd794f4f85c96bd6217127/jaeschkejaeschke2008-03-03T13:28:01+01:00social network trias_example swarm cognition iccs_example <span style="color:#555555;">Vitorino <a href="http://www.bibsonomy.org/author/Ramos">Ramos</a> and Carlos <a href="http://www.bibsonomy.org/author/Fernandes">Fernandes</a> and Agostinho C. <a href="http://www.bibsonomy.org/author/Rosa">Rosa</a> </span>(<em>2006</em>) <em>arXiv:cs/0512002v1 . </em>Mon Mar 03 13:28:01 CET 2008arXiv:cs/0512002v1On Self-Regulated Swarms, Societal Memory, Speed and Dynamics2006social network trias_example swarm cognition iccs_example Wasps, bees, ants and termites all make effective use of their environment and resources by displaying collective "swarm" intelligence. Termite colonies - for instance - build nests with a complexity far beyond the comprehension of the individual termite, while ant colonies dynamically allocate labor to various vital tasks such as foraging or defense without any central decision-making ability. Recent research suggests that microbial life can be even richer: highly social, intricately networked, and teeming with interactions, as found in bacteria. What strikes from these observations is that both ant colonies and bacteria have similar natural mechanisms based on Stigmergy and Self-Organization in order to emerge coherent and sophisticated patterns of global foraging behavior. Keeping in mind the above characteristics we propose a Self-Regulated Swarm (SRS) algorithm which hybridizes the advantageous characteristics of Swarm Intelligence as the emergence of a societal environmental memory or cognitive map via collective pheromone laying in the landscape (properly balancing the exploration/exploitation nature of our dynamic search strategy), with a simple Evolutionary mechanism that trough a direct reproduction procedure linked to local environmental features is able to self-regulate the above exploratory swarm population, speeding it up globally. In order to test his adaptive response and robustness, we have recurred to different dynamic multimodal complex functions as well as to Dynamic Optimization Control problems, measuring reaction speeds and performance. Final comparisons were made with standard Genetic Algorithms (GAs), Bacterial Foraging strategies (BFOA), as well as with recent Co-Evolutionary approaches. SRS's were able to demonstrate quick adaptive responses, while outperforming the results obtained by the other approaches. Additionally, some successful behaviors were found: SRS was able to maintain a number of different solutions, while adapting to unforeseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes; the possibility to spontaneously create and maintain different subpopulations on different peaks, emerging different exploratory corridors with intelligent path planning capabilities; the ability to request for new agents (division of labor) over dramatic changing periods, and economizing those foraging resources over periods of intermediate stabilization. Finally, results illustrate that the present SRS collective swarm of bio-inspired ant-like agents is able to track about 65% of moving peaks traveling up to ten times faster than the velocity of a single individual composing that precise swarm tracking system. This emerged behavior is probably one of the most interesting ones achieved by the present work.http://www.bibsonomy.org/bibtex/27debdcf93027a77b3b928caae4121dff/jaeschkejaeschke2008-03-03T12:05:49+01:00social swarm iccs_example trias_example cognition perception network <span style="color:#555555;">Vitorino <a href="http://www.bibsonomy.org/author/Ramos">Ramos</a> and Carlos <a href="http://www.bibsonomy.org/author/Fernandes">Fernandes</a> and Agostinho C. <a href="http://www.bibsonomy.org/author/Rosa">Rosa</a> </span><em>CVRM-IST 127E-2005. </em><em>Insituto Superior T&#233;cnico, Universidade T&#233;cnica de Lisboa, </em>(<em>2005</em>)Mon Mar 03 12:05:49 CET 2008arXiv:nlin/0502057v1CVRM-IST 127E-2005Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes2005social swarm iccs_example trias_example cognition perception network Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of (unsophisticated) entities interacting locally with their environment cause coherent functional global patterns to emerge. SI provides a basis with wich it is possible to explore collective (or distributed) problem solving without centralized control or the provision of a global model. To tackle the formation of a coherent social collective intelligence from individual behaviors, we discuss several concepts related to Self-Organization, Stigmergy and Social Foraging in animals. Then, in a more abstract level we suggest and stress the role played not only by the environmental media as a driving force for societal learning, as well as by positive and negative feedbacks produced by the many interactions among agents. Finally, presenting a simple model based on the above features, we will adress the collective adaptation of a social community to a cultural (environmenatl, contextual) or media informational dynamical landscape, represented here - for the purpose of different experiments - by several three-dimensional mathematical functions that suddenly change over time. Results indicate that the collective intelligence is able to cope and quickly adapt to unforseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes.http://www.bibsonomy.org/bibtex/2e387c294129e11f4221514d5fa807e26/jaeschkejaeschke2007-07-10T16:33:17+02:002006 12 iccs_example l3s fca myown trias algorithm trias_example <span style="color:#555555;">Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Bernhard <a href="http://www.bibsonomy.org/author/Ganter">Ganter</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 06), </em><em>page907-911. </em><em>Hong Kong, </em><em>IEEE Computer Society, </em><em>December2006. </em>Tue Jul 10 16:33:17 CEST 2007Hong KongProceedings of the 6th IEEE International Conference on Data Mining (ICDM 06)December907-911TRIAS - An Algorithm for Mining Iceberg Tri-Lattices20062006 12 iccs_example l3s fca myown trias algorithm trias_example http://www.bibsonomy.org/bibtex/22cbd8e3236adea7c54779605a5aa4fd6/jaeschkejaeschke2007-07-10T16:17:49+02:00myown folksonomy iccs bookmarking 2006 l3s trias_example social iccs_example bibsonomy <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proceedings of the Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures, </em><em>Aalborg, Denmark, </em><em>Aalborg University Press, </em><em>July2006. </em>Tue Jul 10 16:17:49 CEST 2007Aalborg, DenmarkProceedings of the Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual StructuresJuly{BibSonomy}: A Social Bookmark and Publication Sharing System2006myown folksonomy iccs bookmarking 2006 l3s trias_example social iccs_example bibsonomy http://www.bibsonomy.org/bibtex/27f502f47bd0e584190337e3e2d4eba9e/jaeschkejaeschke2007-02-01T14:04:37+01:00mining rule association 2006 trias_example folksonomy l3s iccs_example myown <span style="color:#555555;">Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Data Science and Classification, </em><em>page261--270. </em><em>Berlin, Heidelberg, </em><em>Springer, </em>(<em>2006</em>)Thu Feb 01 14:04:37 CET 2007Berlin, HeidelbergData Science and Classification261--270Studies in Classification, Data Analysis, and Knowledge OrganizationMining Association Rules in Folksonomies2006mining rule association 2006 trias_example folksonomy l3s iccs_example myown http://www.bibsonomy.org/bibtex/29a2c77c7c7a1b19cd16df08cca65f706/jaeschkejaeschke2007-02-01T14:04:37+01:00iccs_example analysis ontology semantic network myown trias_example 2006 l3s <span style="color:#555555;">Bettina <a href="http://www.bibsonomy.org/author/Hoser">Hoser</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>The Semantic Web: Research and Applications, </em><em>Springer, </em><em>June2006. </em><em>Proceedings of the 3rd European Semantic Web Conference, Budva, Montenegro . </em>Thu Feb 01 14:04:37 CET 2007The Semantic Web: Research and ApplicationsJuneProceedings of the 3rd European Semantic Web Conference, Budva, MontenegroLecture Notes in Computer ScienceSemantic Network Analysis of Ontologies2006iccs_example analysis ontology semantic network myown trias_example 2006 l3s A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures currently receive high attention in the Semantic Web community, there are only very few SNA applications, and virtually none for analyzing the structure of ontologies. We illustrate the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size.http://www.bibsonomy.org/bibtex/205043cc20f1e0f5a612135c970e4f1ac/jaeschkejaeschke2007-02-01T14:04:37+01:002006 emergent bibsonomy l3s iccs_example folksonomy trias_example semantic myown <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Informatik 2006 - Informatik f&#252;r Menschen. Band 2, </em><em>volumeP-94ofLecture Notes in Informatics, </em><em>Bonn, </em><em>oct2006. </em><em>Proc. Workshop on Applications of Semantic Technologies, Informatik 2006 . </em>Thu Feb 01 14:04:37 CET 2007BonnInformatik 2006 - Informatik für Menschen. Band 2octProc. Workshop on Applications of Semantic Technologies, Informatik 2006Lecture Notes in InformaticsEmergent Semantics in BibSonomyP-9420062006 emergent bibsonomy l3s iccs_example folksonomy trias_example semantic myown Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. In this paper we specify a formal model for folksonomies, briefly describe our own system BibSonomy, which allows for sharing both bookmarks and publication references, and discuss first steps towards emergent semantics.http://www.bibsonomy.org/bibtex/204c7d86337d68e4ed9ae637029c43414/jaeschkejaeschke2007-02-01T14:04:37+01:00iccs_example clustering trias_example wordnet text <span style="color:#555555;">A <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and S. <a href="http://www.bibsonomy.org/author/Staab">Staab</a> and G. <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proc. SIGIR Semantic Web Workshop, </em><em>Toronto, </em>(<em>2003</em>)Thu Feb 01 14:04:37 CET 2007TorontoProc. SIGIR Semantic Web WorkshopWordnet improves text document clustering2003iccs_example clustering trias_example wordnet text http://www.bibsonomy.org/bibtex/20ef00fe39718eae61dca4d251b14578d/jaeschkejaeschke2007-02-01T14:04:37+01:00semantic trias_example iccs_example web mining usage <span style="color:#555555;">Bettina <a href="http://www.bibsonomy.org/author/Berendt">Berendt</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Data Mining Next Generation Challenges and Future Directions, </em><em>AAAI Press, </em><em>Boston, </em>(<em>2004</em>)Thu Feb 01 14:04:37 CET 2007BostonData Mining Next Generation Challenges and Future Directions461-481Usage Mining for and on the Semantic Web2004semantic trias_example iccs_example web mining usage Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of data Web mining operates on, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web resources and navigation behavior are increasingly being used. This fits exactly with the aims of the Semantic Web: the Semantic Web enriches the WWW by machine-processable information which supports the user in his tasks. In this paper, we discuss the interplay of the Semantic Web with Web Mining, with a specific focus on usage mining.Preliminary version of http://www.bibsonomy.org/bibtex/0a3c7992f2f6d8ecf7adc04aa6c2d5a22/stummehttp://www.bibsonomy.org/bibtex/24a68d1443065dcd7980989e97cb0af69/jaeschkejaeschke2007-02-01T14:04:37+01:00trias_example semantic usage web mining iccs_example <span style="color:#555555;">G. <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> and B. <a href="http://www.bibsonomy.org/author/Berendt">Berendt</a> and A. <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> </span><em>Proc. NSF Workshop on Next Generation Data Mining, </em><em>page77-86. </em><em>Baltimore, </em><em>November2002. </em>Thu Feb 01 14:04:37 CET 2007BaltimoreProc. NSF Workshop on Next Generation Data MiningNovember77-86Usage Mining for and on the Semantic Web2002trias_example semantic usage web mining iccs_example http://www.bibsonomy.org/bibtex/2fc1c88be5f8c2640ca6e9a40b5fa1c7b/jaeschkejaeschke2007-02-01T14:04:37+01:00trias_example iccs_example mining web semantic <span style="color:#555555;">B. <a href="http://www.bibsonomy.org/author/Berendt">Berendt</a> and A. <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and G. <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>The Semantic Web -- ISWC 2002, </em><em>page264-278. </em><em>Heidelberg, </em><em>Springer, </em>(<em>2002</em>)Thu Feb 01 14:04:37 CET 2007HeidelbergThe Semantic Web -- ISWC 2002264-278LNCSTowards Semantic Web Mining2002trias_example iccs_example mining web semantic posted on 2006-11-22http://www.bibsonomy.org/bibtex/22216064c82c0d31a0b2ae930a955328d/jaeschkejaeschke2007-02-01T14:04:37+01:00iccs_example trias_example format wiki interchange <span style="color:#555555;">Max <a href="http://www.bibsonomy.org/author/V{\&#034;{o}}lkel">V&#246;lkel</a> and Eyal <a href="http://www.bibsonomy.org/author/Oren">Oren</a> </span><em>Proceedings of the First Workshop on Semantic Wikis -- From Wiki To Semantics, </em>(<em>2006</em>)Thu Feb 01 14:04:37 CET 2007Proceedings of the First Workshop on Semantic Wikis -- From Wiki To SemanticsTowards a Wiki Interchange Format (WIF)2006iccs_example trias_example format wiki interchange http://www.bibsonomy.org/bibtex/2850949481723b7dd03768ccd96b25cb9/jaeschkejaeschke2007-02-01T14:04:37+01:00trias_example learning iccs_example ontology tool <span style="color:#555555;">Julien <a href="http://www.bibsonomy.org/author/Tane">Tane</a> and Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> and Steffen <a href="http://www.bibsonomy.org/author/Staab">Staab</a> and R. <a href="http://www.bibsonomy.org/author/Studer">Studer</a> </span><em>Mobiles Lernen und Forschen - Beitr&#228;ge der Fachtagung an der Universit&#228;t, </em><em>page93-104. </em><em>Kassel University Press, </em><em>November2003. </em>Thu Feb 01 14:04:37 CET 2007Mobiles Lernen und Forschen - Beiträge der Fachtagung an der UniversitätNovember93-104The Courseware Watchdog: an Ontology-based tool for finding and organizing learning material2003trias_example learning iccs_example ontology tool Topics in education are changing with an ever faster pace. E-Learning resources tend to be more and more decentralised. Users need increasingly to be able to use the resources of the web. For this, they should have tools for finding and organizing information in a decentral way. In this, paper, we show how an ontology-based tool suite allows to make the most of the resources available on the web.http://www.bibsonomy.org/bibtex/2983a9fa2be0271f92d0dfd8a5cd16935/jaeschkejaeschke2007-02-01T14:04:37+01:00trias_example semantic iccs_example proceeding wiki format <span style="color:#555555;">Anita de <a href="http://www.bibsonomy.org/author/Waard">Waard</a> and Gerard <a href="http://www.bibsonomy.org/author/Tel">Tel</a> </span><em>Proceedings of the First Workshop on Semantic Wikis -- From Wiki To Semantics, </em><em>ESWC2006, </em><em>June2006. </em>Thu Feb 01 14:04:37 CET 2007Proceedings of the First Workshop on Semantic Wikis -- From Wiki To SemanticsSemWiki2006-proceedingsJuneWorkshop on Semantic WikisThe ABCDE Format Enabling Semantic Conference Proceedings2006trias_example semantic iccs_example proceeding wiki format -We believe that the best way to present a narrative to a computer is to let the author explicitly create a rich semantic structure for the article during writing. – We propose an open-standard, widely (re)useable format, the ABCDE format for proceedings and workshop contributions that can be easily mined, integrated and consumed by semantic browsers and wikis. – There need not be an abstract in an ABCDE document - instead, the author denotes core sentences within the B,C and D sections, which are compiled through a macro to form a structured abstract. – We believe a LATEX stylesheet provides a suitable input format for providing authors with a semantic structure to work from. – We provide the abcde.sty LATEX file as an appendix to this paper. – Macros are provided to specify Dublin Core Elements, and to print a list of those that are specified. – Our section division into Background, Contribution, and Discussion is backed by a number of emperical studies. – We aim to work on different incarnations of this format and open it up to modification and development.http://www.bibsonomy.org/bibtex/261d58db419af0dbc3681432588219c3d/jaeschkejaeschke2007-02-01T14:04:37+01:00trias_example iccs_example knowledge text clustering background <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Steffen <a href="http://www.bibsonomy.org/author/Staab">Staab</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Technical Report, </em><em>volume425. </em><em>University of Karlsruhe, Institute AIFB, </em>(<em>2003</em>)Thu Feb 01 14:04:37 CET 2007Text Clustering Based on Background KnowledgeTechnical Report 4252003trias_example iccs_example knowledge text clustering background Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. Standard partitional or agglomerative clustering methods efficiently compute results to this end. However, the bag of words representation used for these clustering methods is often unsatisfactory as it ignores relationships between important terms that do not co-occur literally. Also, it is mostly left to the user to find out why a particular partitioning has been achieved, because it is only specified extensionally. In order to deal with the two problems, we integrate background knowledge into the process of clustering text documents. First, we preprocess the texts, enriching their representations by background knowledge provided in a core ontology — in our application Wordnet. Then, we cluster the documents by a partitional algorithm. Our experimental evaluation on Reuters newsfeeds compares clustering results with pre-categorizations of news. In the experiments, improvements of results by background knowledge compared to the baseline can be shown for many interesting tasks. Second, the clustering partitions the large number of documents to a relatively small number of clusters, which may then be analyzed by conceptual clustering. In our approach, we applied Formal Concept Analysis. Conceptual clustering techniques are known to be too slow for directly clustering several hundreds of documents, but they give an intensional account of cluster results. They allow for a concise description of commonalities and distinctions of different clusters. With background knowledge they even find abstractions like “food” (vs. specializations like “beef” or “corn”). Thus, in our approach, partitional clustering reduces first the size of the problem such that it becomes tractable for conceptual clustering, which then facilitates the understanding of the results.http://www.bibsonomy.org/bibtex/276b6d7afdb3d4f4c19cb02a60c41bb0c/jaeschkejaeschke2007-02-01T14:04:37+01:00trias_example iccs_example semantic wiki web <span style="color:#555555;">Michel <a href="http://www.bibsonomy.org/author/Buffa">Buffa</a> and Ga&#235;l <a href="http://www.bibsonomy.org/author/Crova">Crova</a> and Fabien <a href="http://www.bibsonomy.org/author/Gandon">Gandon</a> and Claire <a href="http://www.bibsonomy.org/author/Lecompte">Lecompte</a> and Jeremy <a href="http://www.bibsonomy.org/author/Passeron">Passeron</a> </span><em>Proceedings of the First Workshop on Semantic Wikis -- From Wiki To Semantics, </em><em>ESWC2006, </em><em>June2006. </em>Thu Feb 01 14:04:37 CET 2007Proceedings of the First Workshop on Semantic Wikis -- From Wiki To SemanticsSemWiki2006-proceedingsJuneWorkshop on Semantic WikisSweetWiki : Semantic WEb Enabled Technologies in Wiki2006trias_example iccs_example semantic wiki web Wikis are social web sites enabling a potentially large number of participants to modify any page or create a new page using their web browser. As they grow, wikis suffer from a number of problems (anarchical structure, large number of pages, aging navigation paths, etc.). We believe that semantic wikis can improve navigation and search. In SweetWiki we investigate the use of semantic web technologies to support and ease the lifecycle of the wiki. The very model of wikis was declaratively described: an OWL schema captures concepts such as WikiWord, wiki page, forward and backward link, author, etc. This ontology is then exploited by an embedded semantic search engine (Corese). In addition, SweetWiki integrates a standard WYSIWYG editor (Kupu) that we extended to support semantic annotation following the "social tagging" approach made popular by web sites such as flickr.com. When editing a page, the user can freely enter some keywords in an AJAX-powered textfield and an auto-completion mechanism proposes existing keywords by issuing SPARQL queries to identify existing concepts with compatible labels. Thus tagging is both easy (keyword-like) and motivating (real time display of the number of related pages) and concepts are collected as in folksonomies. To maintain and reengineer the folksonomy, we reused a web-based editor available in the underlying semantic web server to edit semantic web ontologies and annotations. Unlike in other wikis, pages are stored directly in XHTML ready to be served and semantic annotations are embedded in the pages themselves using RDF/A. If someone sends or copy a page, the annotations follow it, and if an application crawls the wiki site it can extract the metadata and reuse them.http://www.bibsonomy.org/bibtex/2a5f1a8b42409b96271bc5c671deceea9/jaeschkejaeschke2007-02-01T14:04:37+01:00mining iccs_example trias_example semantic web <span style="color:#555555;">J. <a href="http://www.bibsonomy.org/author/Hartmann">Hartmann</a> and A. <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and G. <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proc. Arbeitskreistreffen Knowledge Discovery, </em><em>Oldenburg, </em><em>September2002. </em>Thu Feb 01 14:04:37 CET 2007OldenburgProc. Arbeitskreistreffen Knowledge DiscoverySeptemberSemantic Web Mining for Building Information Portals (Position Paper)2002mining iccs_example trias_example semantic web http://www.bibsonomy.org/bibtex/2f8826ba2790eeb857dd4becb31a08225/jaeschkejaeschke2007-02-01T14:04:37+01:00trias_example mining web semantic iccs_example <span style="color:#555555;">Bettina <a href="http://www.bibsonomy.org/author/Berendt">Berendt</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proc. of the 1st Intl. Workshop on Representation and Analysis of Web Space, </em><em>page1--16. </em><em>Technical University of Ostrava, </em>(<em>2005</em>)Thu Feb 01 14:04:37 CET 2007Proc. of the 1st Intl. Workshop on Representation and Analysis of Web Space1--16Semantic Web Mining and the Representation, Analysis, and Evolution of Web Space2005trias_example mining web semantic iccs_example Proc. of the Semantic Web Mining Workshop of the 12th Europ. Conf. on Machine Learning (ECML'01) / 5th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'01)http://www.bibsonomy.org/bibtex/2f6d06d221aab066b6cae38b595d35ffc/jaeschkejaeschke2007-02-01T14:04:37+01:00workshop iccs_example trias_example proceeding mining web semantic <span style="color:#555555;">G. <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> and A. <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and B. <a href="http://www.bibsonomy.org/author/Berendt">Berendt</a> (eds.). </span><em>Freiburg, </em><em>September 3rd,2001. </em>Thu Feb 01 14:04:37 CET 2007FreiburgSeptember 3rd,Semantic Web Mining2001workshop iccs_example trias_example proceeding mining web semantic http://www.bibsonomy.org/bibtex/210efb9940c6f2881cd6c84b6c52bebd8/jaeschkejaeschke2007-02-01T14:04:37+01:00web semantic learning management trias_example iccs_example <span style="color:#555555;">Julien <a href="http://www.bibsonomy.org/author/Tane">Tane</a> and Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proc. 13th International World Wide Web Conference (WWW 2004), </em><em>page1-10. </em>(<em>2004</em>)Thu Feb 01 14:04:37 CET 2007Proc. 13th International World Wide Web Conference (WWW 2004)1-10Semantic resource management for the web: an e-learning application2004web semantic learning management trias_example iccs_example