@inproceedings{tane04semantic, author = {Tane, Julien and Schmitz, Christoph and Stumme, Gerd}, bibsource = {DBLP, http://dblp.uni-trier.de}, booktitle = {Proc. 13th International World Wide Web Conference (WWW 2004)}, ee = {http://doi.acm.org/10.1145/1013369}, interhash = {92089bd1e3e798b50fe7c82cd8333e7b}, intrahash = {10efb9940c6f2881cd6c84b6c52bebd8}, pages = {1-10}, title = {Semantic resource management for the web: an e-learning application}, url = {http://www.www2004.org/proceedings/docs/2p1.pdf}, year = 2004 } @techreport{hotho03textclustering, abstract = {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.}, author = {Hotho, Andreas and Staab, Steffen and Stumme, Gerd}, comment = {alpha}, institution = {University of Karlsruhe, Institute AIFB}, interhash = {0bc7c3fc1273355f45c8970a7ea58f97}, intrahash = {61d58db419af0dbc3681432588219c3d}, title = {Text Clustering Based on Background Knowledge}, type = {Technical Report }, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003text.pdf}, volume = 425, year = 2003 } @incollection{stumme02using, address = {Heidelberg}, author = {Stumme, G.}, booktitle = {Wissensmanagement mit Referenzmodellen -- Konzepte für die Anwendungssystem- und Organisationsgestaltung}, comment = {alpha}, editor = {Becker, J. and Knackstedt, R.}, interhash = {89b56b4b45d3c9256355080ce94045e0}, intrahash = {8cd4f719765abd1c46d28f200327d935}, pages = {163-174}, publisher = {Physica}, title = {Using Ontologies and Formal Concept Analysis for Organizing Business Knowledge}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2001/REFMOD01.ps}, year = 2002 } @inproceedings{schmitz2006mining, abstract = {Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics. We demonstrate our approach on a large scale dataset stemming from an online system.}, address = {Heidelberg}, author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, booktitle = {Data Science and Classification. Proceedings of the 10th IFCS Conf.}, editor = {Batagelj, V. and Bock, H.-H. and Ferligoj, A. and Žiberna, A.}, interhash = {20650d852ca3b82523fcd8b63e7c12d7}, intrahash = {11b2a59a568d246d7f36cb68169a464a}, month = {July}, pages = {261--270}, publisher = {Springer}, series = {Studies in Classification, Data Analysis, and Knowledge Organization}, title = {Mining Association Rules in Folksonomies}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006mining.pdf}, year = 2006 } @inproceedings{stumme01bottom, address = {Seattle, WA, USA}, author = {Stumme, G. and Maedche, A.}, booktitle = {Proc. 17th Intl. Conf. on Artificial Intelligence (IJCAI '01)}, comment = {alpha}, editor = {Nebel, B.}, interhash = {2ff2aa6492ba1857d18042fa4d57111d}, intrahash = {cd6d6c64f79d13d7cf394d3a6b3858ba}, pages = {225-230}, title = {{FCA}-{M}erge: Bottom-Up Merging of Ontologies.}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2001/IJCAI01.pdf}, year = 2001 } @inproceedings{stumme00towardsanorder, address = {Aachen}, author = {Stumme, G. and Studer, R. and Sure, Y.}, booktitle = {Verbundtagung Wirtschaftsinformatik 2000}, comment = {alpha}, editor = {Bodendorf, F. and Grauer, M.}, interhash = {9042498686a1b2c000cd4b0007a76bf8}, intrahash = {83cc12ae3979a3e4108e5442b7da0b4f}, pages = {136-149}, publisher = {Shaker}, title = {Towards an Order-Theoretical Foundation for Maintaining and Merging Ontologies}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2000/REFMOD00.pdf}, year = 2000 } @inproceedings{hotho03explaining, abstract = {Common text clustering techniques offer rather poor capabilities for explaining to their users why a particular result has been achieved. They have the disadvantage that they do not relate semantically nearby terms and that they cannot explain how resulting clusters are related to each other. In this paper, we discuss a way of integrating a large thesaurus and the computation of lattices of resulting clusters into common text clustering in order to overcome these two problems. As its major result, our approach achieves an explanation using an appropriate level of granularity at the concept level as well as an appropriate size and complexity of the explaining lattice of resulting clusters.}, address = {Heidelberg}, author = {Hotho, Andreas and Staab, Steffen and Stumme, Gerd}, booktitle = {Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases}, comment = {alpha}, editor = {Lavra\v{c}, Nada and Gamberger, Dragan and Todorovski, Hendrik BlockeelLjupco}, interhash = {cf66183151a5d94a0941ac6d5089ae89}, intrahash = {53a943b6be4b34cf4e5329d0b58e99f6}, pages = {217-228}, publisher = {Springer}, series = {LNAI}, title = {Explaining Text Clustering Results using Semantic Structures}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003explaining.pdf}, volume = 2838, year = 2003 } @inproceedings{schmitz02accessing, address = {Norfolk}, author = {Schmitz, C. and Staab, S. and Studer, R. and Stumme, G. and Tane, J.}, booktitle = {Proc. of E-Learning 2002 World Conference on E-Learning in Corporate, Government, Healthcare and Higher Education on (E-Learning 2002)}, editor = {Driscoll, M. and Reeves, T.C.}, interhash = {654b9a08b2bc09cf44dfe51840371e23}, intrahash = {d366e42880a2d2991cbfa6abc53b8fe4}, note = {{A}warded paper}, pages = {909-915}, title = {Accessing Distributed Learning Repositories through a Courseware Watchdog}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2002/E-Learn02.pdf}, volume = {AACE}, year = 2002 }