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Scheich, P.; Skorsky, M.; Vogt, F.; Wachter, C.; Wille, R.: Conceptual data systems (1993)
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- Imprint
- Berlin : Springer
-
Ganter, B.; Wille, R.: Conceptual scaling (1989)
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- Imprint
- Berlin : Springer
- Source
- Applications of combinatorics and graph theory to the biological and social sciences. Ed.: F. Roberts
-
Vogt, F.; Wachter, C.; Wille, R.: Data analysis based on a conceptual file (1991)
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- Imprint
- Berlin : Springer
-
Reinartz, T.P.; Zickwolff, M.: ¬Two conceptual approaches to acquire human expert knowledge in a complex real world domain (1996)
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- Berlin : Springer
-
Groh, B.; Strahringer, S.; Wille, R.: TOSCANA-systems based on thesauri (1998)
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- Imprint
- Berlin : Springer
- Source
- Conceptual structures: theory, tools and applications. 6th International Conference on conceptual Structures, ICCS'98, Montpellier, France, August, 10-12, 1998, Proceedings. Ed.: M.L. Mugnier u. M. Chein
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Vogt, F.; Wille, R.: TOSCANA - a graphical tool for analyzing and exploring data (1995)
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- Source
- Knowledge organization. 22(1995) no.2, S.78-81
-
Wille, R.: Geometric representations of concept lattices (1989)
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- Imprint
- Berlin : Springer
-
Rusch, A.; Wille, R.: Knowledge spaces and formal concept analysis (1996)
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- Imprint
- Berlin : Springer
-
Vogt, F.; Wille, R.: TOSCANA - a graphical tool for analyzing and exploring data (1995)
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-
Luksch, P.; Wille, R.: ¬A mathematical model for conceptual knowledge systems (1991)
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- Imprint
- Berlin : Springer
-
Eklund, P.; Groh, B.; Stumme, G.; Wille, R.: ¬A conceptual-logic extension of TOSCANA (2000)
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- Berlin : Springer
-
Eklund. P.W.: Logic-based networks : concept graphs and conceptual structures (2000)
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- Berlin : Springer
-
Burmeister, P.; Holzer, R.: On the treatment of incomplete knowledge in formal concept analysis (2000)
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- Berlin : Springer
-
Ganter, B.: Computing with conceptual structures (2000)
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- Berlin : Springer
-
Priss, U.: Faceted information representation (2000)
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- Date
- 22. 1.2016 17:47:06
-
Priss, U.: Faceted knowledge representation (1999)
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- Date
- 22. 1.2016 17:30:31
-
Hereth, J.; Stumme, G.; Wille, R.; Wille, U.: Conceptual knowledge discovery and data analysis (2000)
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- Imprint
- Berlin : Springer
-
Ganter, B.; Stahl, J.; Wille, R.: Conceptual measurement and many-valued contexts (1986)
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- Source
- Classification as a tool of research. Ed.: W. Gaul u. M. Schader
-
Negm, E.; AbdelRahman, S.; Bahgat, R.: PREFCA: a portal retrieval engine based on formal concept analysis (2017)
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- Abstract
- The web is a network of linked sites whereby each site either forms a physical portal or a standalone page. In the former case, the portal presents an access point to its embedded web pages that coherently present a specific topic. In the latter case, there are millions of standalone web pages, that are scattered throughout the web, having the same topic and could be conceptually linked together to form virtual portals. Search engines have been developed to help users in reaching the adequate pages in an efficient and effective manner. All the known current search engine techniques rely on the web page as the basic atomic search unit. They ignore the conceptual links, that reveal the implicit web related meanings, among the retrieved pages. However, building a semantic model for the whole portal may contain more semantic information than a model of scattered individual pages. In addition, user queries can be poor and contain imprecise terms that do not reflect the real user intention. Consequently, retrieving the standalone individual pages that are directly related to the query may not satisfy the user's need. In this paper, we propose PREFCA, a Portal Retrieval Engine based on Formal Concept Analysis that relies on the portal as the main search unit. PREFCA consists of three phases: First, the information extraction phase that is concerned with extracting portal's semantic data. Second, the formal concept analysis phase that utilizes formal concept analysis to discover the conceptual links among portal and attributes. Finally, the information retrieval phase where we propose a portal ranking method to retrieve ranked pairs of portals and embedded pages. Additionally, we apply the network analysis rules to output some portal characteristics. We evaluated PREFCA using two data sets, namely the Forum for Information Retrieval Evaluation 2010 and ClueWeb09 (category B) test data, for physical and virtual portals respectively. PREFCA proves higher F-measure accuracy, better Mean Average Precision ranking and comparable network analysis and efficiency results than other search engine approaches, namely Term Frequency Inverse Document Frequency (TF-IDF), Latent Semantic Analysis (LSA), and BM25 techniques. As well, it gains high Mean Average Precision in comparison with learning to rank techniques. Moreover, PREFCA also gains better reach time than Carrot as a well-known topic-based search engine.
-
Kumar, C.A.; Radvansky, M.; Annapurna, J.: Analysis of Vector Space Model, Latent Semantic Indexing and Formal Concept Analysis for information retrieval (2012)
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