-
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
-
Ganter, B.; Wille, R.: Conceptual scaling (1989)
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- Pages
- S.139-167
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- Applications of combinatorics and graph theory to the biological and social sciences. Ed.: F. Roberts
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Vogt, F.; Wachter, C.; Wille, R.: Data analysis based on a conceptual file (1991)
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- Pages
- S.131-142
-
Vogt, F.; Wille, R.: TOSCANA - a graphical tool for analyzing and exploring data (1995)
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- Pages
- S.226-233
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Scheich, P.; Skorsky, M.; Vogt, F.; Wachter, C.; Wille, R.: Conceptual data systems (1993)
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- Pages
- S.72-84
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Priss, U.: Faceted information representation (2000)
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- Date
- 22. 1.2016 17:47:06
- Pages
- S.84-94
-
Priss, U.: Faceted knowledge representation (1999)
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- Abstract
- Faceted Knowledge Representation provides a formalism for implementing knowledge systems. The basic notions of faceted knowledge representation are "unit", "relation", "facet" and "interpretation". Units are atomic elements and can be abstract elements or refer to external objects in an application. Relations are sequences or matrices of 0 and 1's (binary matrices). Facets are relational structures that combine units and relations. Each facet represents an aspect or viewpoint of a knowledge system. Interpretations are mappings that can be used to translate between different representations. This paper introduces the basic notions of faceted knowledge representation. The formalism is applied here to an abstract modeling of a faceted thesaurus as used in information retrieval.
- Date
- 22. 1.2016 17:30:31
-
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.
- Source
- Information processing and management. 53(2017) no.1, S.203-222
-
Wille, R.: Line diagrams of hierarchical concept systems (1984)
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- Source
- International classification. 11(1984), S.77-86
-
Wille, R.: Geometric representations of concept lattices (1989)
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- Pages
- S.239-255
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Wille, R.: Lattices in data analysis : how to draw them with a computer (1989)
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- Pages
- S.33-58
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Wille, R.: Concept lattices and conceptual knowledge systems (1992)
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- Source
- Computers and mathematics with applications. 23(1992), S.493-515
-
Groh, B.; Strahringer, S.; Wille, R.: TOSCANA-systems based on thesauri (1998)
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- Pages
- S.127-138
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Ganter, B.; Stahl, J.; Wille, R.: Conceptual measurement and many-valued contexts (1986)
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- Pages
- S.169-176
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Wille, R.: Knowledge acquisition by methods of formal concept analysis (1989)
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- Pages
- S.365-380
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Rusch, A.; Wille, R.: Knowledge spaces and formal concept analysis (1996)
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- S. -
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Reinartz, T.P.; Zickwolff, M.: ¬Two conceptual approaches to acquire human expert knowledge in a complex real world domain (1996)
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- S. -
-
Priss, U.: Lattice-based information retrieval (2000)
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- Abstract
- A lattice-based model for information retrieval was suggested in the 1960's but has been seen as a theoretical possibility hard to practically apply ever since. This paper attempts to revive the lattice model and demonstrate its applicability in an information retrieval system, FalR, that incorporates a graphical representation of a faceted thesaurus. It shows how Boolean queries can be lattice-theoretically related to the concepts of the thesaurus and visualized within the thesaurus display. An advantage of FaIR is that it allows for a high level of transparency of the system, which can be controlled by the user
- Source
- Knowledge organization. 27(2000) no.3, S.132-142
-
De Maio, C.; Fenza, G.; Loia, V.; Senatore, S.: Hierarchical web resources retrieval by exploiting Fuzzy Formal Concept Analysis (2012)
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- Source
- Information processing and management. 48(2012) no.3, S.399-418
-
Sedelow, S.Y.; Sedelow, W.A.: Thesauri and concept-lattice semantic nets (1994)
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- Pages
- S.350-357
