@inproceedings{hotho03ontologies, address = {Melbourne, Florida}, author = {Hotho, Andreas and Staab, Steffen and Stumme, Gerd}, booktitle = {Proceedings of the 2003 IEEE International Conference on Data Mining}, comment = {alpha}, interhash = {b56c36d6d9c9ca9e6bd236a0f92415a5}, intrahash = {57a39c81cff1982dbefed529be934bee}, month = {November 19-22,}, pages = {541-544 (Poster}, publisher = {IEEE {C}omputer {S}ociety}, title = {Ontologies improve text document clustering}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003ontologies.pdf}, year = 2003 } @inproceedings{ganter03creation, abstract = {We provide a new method for systematically structuring the top-down level of ontologies. It is based on an interactive, top--down knowledge acquisition process, which assures that the knowledge engineer considers all possible cases while avoiding redundant acquisition. The method is suited especially for creating/merging the top part(s) of the ontologies, where high accuracy is required, and for supporting the merging of two (or more) ontologies on that level.}, address = {Heidelberg}, author = {Ganter, Bernhard and Stumme, Gerd}, booktitle = {Conceptual Structures for Knowledge Creation and Communication.}, comment = {alpha}, editor = {de Moor, Aldo and Lex, Wilfried and Ganter, Bernhard}, interhash = {e7193e685762aca1a3f855b06e1e4289}, intrahash = {63bd63cb06802a5308959d611c1a017a}, pages = {131-145}, publisher = {Springer}, series = {LNAI}, title = {Creation and Merging of Ontology Top-Levels}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/ganter2003creation.pdf}, volume = 2746, year = 2003 } @inproceedings{tane03courseware, abstract = {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.}, author = {Tane, Julien and Schmitz, Christoph and Stumme, Gerd and Staab, Steffen and Studer, R.}, booktitle = {Mobiles Lernen und Forschen - Beiträge der Fachtagung an der Universität}, comment = {alpha}, editor = {David, Klaus and Wegner, Lutz}, interhash = {7f33080bb78d089b24bf51c059f8f018}, intrahash = {850949481723b7dd03768ccd96b25cb9}, month = {November}, pages = {93-104}, publisher = {Kassel University Press}, title = {The Courseware Watchdog: an Ontology-based tool for finding and organizing learning material}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/tane2003courseware.pdf}, year = 2003 } @inproceedings{agarwal03semantic, abstract = {The paper describes a set of approaches for representing and accessing information within a semantically structured information portal, while offering the possibility to integrate own information. It discusses research performed within the project `Semantic Methods and Tools for Information Portals (SemIPort)'. In particular, it focuses on (1) the development of scalable storing, processing and querying methods for semantic data, (2) visualization and browsing of complex data inventories, (3) personalization and agent-based interaction, and (4) the enhancement of web mining approaches for use within a semantics-based portal.}, address = {Bonn}, author = {Agarwal, Sudhir and Fankhauser, Peter and Gonzalez-Ollala, Jorge and Hartmann, Jens and Hollfelder, Silvia and Jameson, Anthony and Klink, Stefan and Lehti, Patrick and Ley, Michael and Rabbidge, Emma and Schwarzkopf, Eric and Shrestha, Nitesh and Stojanovic, Nenad and Studer, Rudi and Stumme, Gerd and Walter, Bernd}, booktitle = {INFORMATIK 2003 -- Innovative Informatikanwendungen (Band 1)}, comment = {alpha}, editor = {Dittrich, K. and König, W. and Oberweis, A. and Rannenberg, K. and Wahlster, W.}, interhash = {54f275f02db30eafdab2f178e50fd7dc}, intrahash = {8f2983e0f20c26ff98577059343f2cd4}, pages = {116-131}, publisher = {Gesellschaft für Informatik}, series = {LNI}, title = {Semantic Methods and Tools for Information Portals}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/agarwal2003semantic.pdf}, volume = 34, year = 2003 } @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 } @inproceedings{studer03building, address = {Osaka, Japan}, author = {Studer, Rudi and Stumme, Gerd and Handschuh, Siegfried and Hotho, Andreas and Motik, B.}, booktitle = {New Trends in Knowledge Processing -- Data Mining, Semantic Web and Computational}, comment = {alpha}, interhash = {67d164f4a531ddf0f84df0b5de52e80a}, intrahash = {a0e7b52680f1876cdd9cd21f7cb2f95c}, month = {March 10-11,}, pages = {31-34}, title = {Building and Using the Semantic Web}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/Sanken03.pdf}, year = 2003 } @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 }