Jäschke, R.:
Formal concept analysis and tag recommendations in collaborative tagging systems. [Amsterdam], 2011
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Jäschke, R.; Hotho, A.; Schmitz, C.; Ganter, B. & Stumme, G.: Discovering Shared Conceptualizations in Folksonomies. In:
Web Semantics: Science, Services and Agents on the World Wide Web 6 (2008), Nr. 1, S. 38-53
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[Kurzfassung]
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Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.
Jäschke, R.; Hotho, A.; Schmitz, C. & Stumme, G.: Analysis of the Publication Sharing Behaviour in BibSonomy.
Proceedings of the 15th International Conference on Conceptual Structures. Sheffield, England: 2007 (LNCS 4604)
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BibSonomy is a web-based social resource sharing system which allows users to organise and share bookmarks and publications in a collaborative manner. In this paper we present the system, followed by a description of the insights in the structure of its bibliographic data that we gained by applying techniques we developed in the area of Formal Concept Analysis.
Jäschke, R.; Hotho, A.; Schmitz, C.; Ganter, B. & Stumme, G.: TRIAS - An Algorithm for Mining Iceberg Tri-Lattices.
Proc. 6th ICDM conference. Hong Kong: 2006
[BibTeX]
Cimiano, P.; Hotho, A. & Staab, S.: Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis. In:
Journal on Artificial Intelligence Research 24 (2005), S. 305-339
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Stumme, G.: A Finite State Model for On-Line Analytical Processing in
Triadic Contexts. In: Ganter, B. & Godin, R. (Hrsg.):
Proc. 3rd Intl. Conf. on Formal Concept Analysis. Heidelberg: Springer, 2005 (Lecture Notes in Computer Science 3403), S. 315-328
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Cimiano, P.; Hotho, A.; Stumme, G. & Tane, J.: Conceptual Knowledge Processing with Formal Concept Analysis and Ontologies.
Proceedings of the The Second International Conference on Formal Concept Analysis (ICFCA 04). Springer, 2004 (LNCS 2961)
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Cimiano, P.; Hotho, A. & Staab, S.:
Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis. , 2004
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Hotho, A.; Staab, S. & Stumme, G.: Explaining Text Clustering Results using Semantic Structures.
Proc. of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD. 2003 (LNCS 2838), S. 217-228
[BibTeX]
Yoo, S. Y. & Hoffmann, A. G.: A New Approach for Concept-Based Web Search..
Australian Conference on Artificial Intelligence. 2003, S. 65-76
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[BibTeX]
Stumme, G.: Using Ontologies and Formal Concept Analysis for
Organizing Business Knowledge. In: Becker, J. & Knackstedt, R. (Hrsg.):
Wissensmanagement mit Referenzmodellen
- Konzepte für die Anwendungssystem- und Organisationsgestaltung. Heidelberg: Physica, 2002, S. 163-174
[BibTeX]
Stumme, G.; Taouil, R.; Bastide, Y.; Pasquier, N. & Lakhal, L.: Computing iceberg concept lattices with TITANIC. In:
Data Knowl. Eng. 42 (2002), Nr. 2, S. 189-222
[BibTeX]
Kim, M. & Compton, P.: Formal Concept Analysis for Domain-Specific Document Retrieval Systems. In: Stumptner, M.; Corbett, D. & Brooks, M. (Hrsg.):
AI 2001: Advances in Artificial Intelligence: 14th Australian Joint Conference on Artificial Intelligence. Adelaide Australia: Springer, 2001, S. 237-248
[BibTeX]
Stumme, G. & Maedche, A.: FCA-Merge: Bottom-Up Merging of Ontologies.
IJCAI-2001 - Proceedings of the 17th International Joint Conference on Artificial Intelligence, Seattle, USA, August, 1-6, 2001. San Francisco: Morgen Kaufmann, 2001, S. 225-234
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Lehmann, F. & Wille, R.: A triadic approach to formal concept analysis. In:
Conceptual Structures: Applications, Implementation and Theory (1995), S. 32-43
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Formal Concept Analysis, developed during the last fifteen years, has been based on the dyadic understanding of a concept constituted by its extension and its intension. The pragmatic philosophy of Charles S. Peirce with his three universal categories, and experiences in data analysis, have suggested a triadic approach to Formal Concept Analysis. This approach starts with the primitive notion of a triadic context defined as a quadruple (G, M, B, Y) where G, M, and B are sets and Y is a ternary relation between G, M, and B, i.e. Y G×M×B; the elements of G, M, and B are called objects, attributes, and conditions, respectively, and (g, m,b) Y is read: the object g has the attribute m under (or according to) the condition b. A triadic concept of a triadic context (G, M, B, Y) is defined as a triple (A
Lehmann, F. & Wille, R.: A triadic approach to formal concept analysis. In: Ellis, G.; Levinson, R.; Rich, W. & Sowa, J. F. (Hrsg.):
Conceptual structures: applications, implementation and theory. Berlin--Heidelberg--New~York: Springer--Verlag, 1995Lecture Notes in Artificial Intelligence , S. 32-43
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