@inproceedings{jaschke2013attribute, abstract = {We propose an approach for supporting attribute exploration by web information retrieval, in particular by posing appropriate queries to search engines, crowd sourcing systems, and the linked open data cloud. We discuss underlying general assumptions for this to work and the degree to which these can be taken for granted.}, author = {Jäschke, Robert and Rudolph, Sebastian}, booktitle = {Contributions to the 11th International Conference on Formal Concept Analysis}, editor = {Cellier, Peggy and Distel, Felix and Ganter, Bernhard}, interhash = {000ab7b0ae3ecd1d7d6ceb39de5c11d4}, intrahash = {45e900e280661d775d8da949baee3747}, month = may, organization = {Technische Universität Dresden}, pages = {19--34}, title = {Attribute Exploration on the Web}, url = {http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-113133}, urn = {urn:nbn:de:bsz:14-qucosa-113133}, year = 2013 } @inproceedings{wille94plaedoyer, address = {Mannheim}, author = {Wille, Rudolf}, booktitle = {Begriffliche Wissensverarbeitung -- Grundfragen und Aufgaben}, editor = {Wille, R. and Zickwolff, M.}, interhash = {3d247c54b3ed4d35266336fc305665e1}, intrahash = {4423251a98a4bba43a7ffa89c108a3fd}, journal = {B. I. -Wissenschaftsverlag}, pages = {11-25}, publisher = {B. I. -Wissenschaftsverlag}, title = {{Plädoyer für eine philosophische Grundlegung der Begrifflichen Wissensverarbeitung}}, year = 1994 } @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 } @article{hereth03conceptual, abstract = {In this paper we discuss Conceptual Knowledge Discovery in Databases (CKDD) as it is developing in the field of Conceptual Knowledge Processing. Conceptual Knowledge Processing is based on the mathematical theory of Formal Concept Analysis which has become a successful theory for data analysis during the last two decades. CKDD aims to support a human-centered process of discovering knowledge from data by visualizing and analyzing the conceptual structure of the data. We dicuss how the management system TOSCANA for conceptual information systems supports CKDD, and illustrate it by two applications in database marketing and flight movement analysis. Finally, we present a new tool for conceptual deviation discovery, Chianti.}, author = {Hereth, Joachim and Stumme, Gerd and Wille, Rudolf and Wille, Uta}, comment = {alpha}, interhash = {a9c05101aeb799232425d7651a581684}, intrahash = {edffeb9bd2aaac559f2a6233dd49ae3b}, journal = {Journal of Applied Artificial Intelligence (AAI)}, number = 3, pages = {281-301}, title = {Conceptual Knowledge Discovery - a Human-Centered Approach}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hereth2003conceptual.pdf}, volume = 17, 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 } @proceedings{stumme00begriffliche, address = {Heidelberg}, comment = {alpha}, editor = {Stumme, Gerd and Wille, Rudolf}, interhash = {fd1a01ab39d86aa72487bc68bfd4d887}, intrahash = {da312496324083ee022682d70fdb22a1}, publisher = {Springer}, title = {Begriffliche Wissensverarbeitung -- Methoden und Anwendungen}, url = {http://www.springer.com/dal/home/generic/search/results?SGWID=1-40109-22-2058937-0}, year = 2000 } @techreport{stumme99conceptualknowledge, author = {Stumme, G.}, comment = {alpha}, institution = {TU Darmstadt}, interhash = {c33970150f97bad7972281e38b42738f}, intrahash = {6d562dc043ba698acee8a83ce35bde6e}, title = {Conceptual Knowledge Discovery with Frequent Concept Lattices}, type = {{FB}4-{P}reprint 2043}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/1999/P2043.pdf}, year = 1999 } @inproceedings{stumme97tool, address = {Berlin}, author = {Stumme, Gerd}, booktitle = {Conceptual Structures: Fulfilling Peirce's Dream. Proc. ICCS'97}, editor = {Lukose, D. and Delugach, H. and Keeler, M. and Searle, L. and Sowa, J. F.}, interhash = {fce31f95ec4537a11fd78e5081d63fe6}, intrahash = {81bd13dbc2767c77b18bc3bd919fcc29}, page = {318-331}, publisher = {Springer}, series = {LNAI}, title = {Concept Exploration - A Tool for Creating and Exploring Conceptual Hierarchies}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/1997/P1905-ICCS97.pdf}, volume = 1257, year = 1997 } @inproceedings{stumme98knowledgeacquisition, address = {Heidelberg}, author = {Stumme, Gerd}, booktitle = {KI-98: Advances in Artificial Intelligence. Proc. 22. Jahrestagung}, comment = {alpha}, editor = {Herzog, O. and Günter, A.}, interhash = {733cdb16d12f945d904f46c3243bcabb}, intrahash = {df6b2348edd768d19918387671315251}, pages = {117-128}, publisher = {Springer}, series = {LNAI}, title = {Distributive Concept Exploration - A Knowledge Acquisition Tool in Formal Concept Analysis}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/1998/KI98.pdf}, volume = 1504, year = 1998 } @inproceedings{stumme1995knowledge, address = {Berlin--Heidelberg--New~York}, author = {Stumme, Gerd}, booktitle = {Conceptual structures: applications, implementation and theory}, editor = {Ellis, G. and Levinson, R. and Rich, W. and Sowa, J. F.}, interhash = {8d345e4fd5d9422ef43d6bc88548b3f1}, intrahash = {81ad0b71e433513125eed3a478b28b60}, number = 954, organisation = {Simon Fraser University}, publisher = {Springer--Verlag}, series = {Lecture Notes in Artificial Intelligence}, title = {Knowledge acquisition by distributive concept exploration}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/1995/ICCS95_not_in_cv.pdf}, year = 1995 } @inproceedings{stumme98conceptual, address = {Heidelberg}, author = {Stumme, Gerd and Wille, Rudolf and Wille, Uta}, booktitle = {Principles of Data Mining and Knowledge Discovery Proc. 2nd European Symposium on PKDD'98}, editor = {Zytkow, J. M. and Quafofou, M.}, interhash = {5ef89b6f8fb22f9d24eda7da71b8bdb1}, intrahash = {b960d1d34dace39052a0530ab4026e18}, note = {{P}art of \cite{hereth03conceptual}}, page = {318-331}, pages = {450-458}, series = {LNAI}, title = {Conceptual Knowledge Discovery in Databases Using Formal Concept Analysis Methods}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/1998/P1993-PKDD98.pdf}, volume = 1510, year = 1998 } @inproceedings{mineau99conceptual, address = {Heidelberg}, author = {Mineau, Guy and Stumme, Gerd and Wille, Rudolf}, booktitle = {Conceptual Structures: Standards and Practices. Proc. ICCS '99}, editor = {Tepfenhart, W. and Cyre, W.}, interhash = {da964e12b882b59f6562a5e5ebb346ae}, intrahash = {162279b2ef7ec69ab0bf0dc3a7b79b18}, page = {318-331}, pages = {423-441}, publisher = {Springer}, series = {LNAI}, title = {Conceptual Structures Represented by Conceptual Graphs and Formal Concept Analysis}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/1999/ICCS99.pdf}, volume = 1640, year = 1999 } @article{stumme03off, abstract = {In the last years, the main orientation of Formal Concept Analysis (FCA) has turned from mathematics towards computer science. This article provides a review of this new orientation and analyzes why and how FCA and computer science attracted each other. It discusses FCA as a knowledge representation formalism using five knowledge representation principles provided by Davis, Shrobe, and Szolovits (1993). It then studies how and why mathematics-based researchers got attracted by computer science. We will argue for continuing this trend by integrating the two research areas FCA and Ontology Engineering. The second part of the article discusses three lines of research which witness the new orientation of Formal Concept Analysis: FCA as a conceptual clustering technique and its application for supporting the merging of ontologies; the efficient computation of association rules and the structuring of the results; and the visualization and management of conceptual hierarchies and ontologies including its application in an email management system.}, author = {Stumme, G.}, comment = {alpha}, interhash = {230f52a01e0807b91e2c36fb5610b1c6}, intrahash = {3ad5183ad5e15d93898a798bd5063194}, journal = {Intl. J. Human-Comuter Studies (IJHCS)}, month = {September}, number = 3, pages = {287-325}, title = {Off to New Shores -- Conceptual Knowledge Discovery and Processing}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/stumme2003off.pdf}, volume = 59, year = 2003 } @inbook{lakhal2005efficient, abstract = {Association rules are a popular knowledge discovery technique for warehouse basket analysis. They indicate which items of the warehouse are frequently bought together. The problem of association rule mining has first been stated in 1993. Five years later, several research groups discovered that this problem has a strong connection to Formal Concept Analysis (FCA). In this survey, we will first introduce some basic ideas of this connection along a specific algorithm, \titanic, and show how FCA helps in reducing the number of resulting rules without loss of information, before giving a general overview over the history and state of the art of applying FCA for association rule mining.}, address = {Heidelberg}, author = {Lakhal, Lotfi and Stumme, Gerd}, booktitle = {Formal Concept Analysis: Foundations and Applications}, editor = {Ganter, Bernhard and Stumme, Gerd and Wille, Rudolf}, ee = {http://dx.doi.org/10.1007/11528784_10}, interhash = {f5777a0f9dccfcf4f9968119d77297fc}, intrahash = {2b350f817428e4c6c7259cd279815091}, pages = {180-195}, publisher = {Springer}, series = {LNAI}, title = {Efficient Mining of Association Rules Based on Formal Concept Analysis}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2005/lakhal2005efficient.pdf}, volume = 3626, year = 2005 } @inproceedings{stumme01intelligent, address = {Heidelberg}, author = {Stumme, G. and Taouil, R. and Bastide, Y. and Pasquier, N. and Lakhal, L.}, booktitle = {KI 2001: Advances in Artificial Intelligence. KI 2001}, editor = {Baader, F. and Brewker, G. and Eiter, T.}, interhash = {15d7d015c8820a41323ab4e7639ff151}, intrahash = {d93292a7637bd2061b67f4934e7dde46}, pages = {335-350}, publisher = {Springer}, series = {LNAI}, title = {Intelligent Structuring and Reducing of Association Rules and with Formal Concept Analysis}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2001/KI01.pdf}, volume = 2174, year = 2001 }