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Vogt, F.; Wille, R.: TOSCANA - a graphical tool for analyzing and exploring data (1995)
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- Abstract
- TOSCANA is a computer program which allows an online interaction with larger data bases to analyse and explore data conceptually. It uses labelled line diagrams of concept lattices to communicate knowledge coded in given data. The basic problem to create online presentations of concept lattices is solved by composing prepared diagrams to nested line diagrams. A larger number of applications in different areas have already shown that TOSCANA is a useful tool for many purposes
- Source
- Knowledge organization. 22(1995) no.2, S.78-81
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Priss, U.: Formal concept analysis in information science (2006)
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- Date
- 13. 7.2008 19:29:59
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Kollewe, W.: Instrumente der Literaturverwaltung : Inhaltliche analyse von Datenbeständen durch 'Begriffliche Wissensverarbeitung' (1996)
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- Abstract
- Ein grundsätzliches Problem der Literaturverwaltung besteht darin, daß viele Nutzer der Retrievalsysteme gar nicht genau sagen können, was sie suchen. Erst im Prozeß des erkundenden Suchens lernen sie genauer zu präzisieren, was sie finden wollen. Dieser Lernprozeß wird durch einzelne Suchwörter (Suchwortketten) nur unzureichend unterstützt, weshalb der benutzer häufig unzufrieden mit dem Ergebnis eines solchen Suchprozesses ist. Notwendig sind reichhaltigere Begriffsnetze, die thematisch geordnete Zusammenhänge darstellen und sich flexibel verfeinern, vergröbern oder verändern lassen, um in geeignetem Umfang die wünschenswerte Orientierung liefern zu können. Das Computerprogramm TOSCANA könnte hier weiterhelfen
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Carpineto, C.; Romano, G.: Order-theoretical ranking (2000)
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- Abstract
- Current best-match ranking (BMR) systems perform well but cannot handle word mismatch between a query and a document. The best known alternative ranking method, hierarchical clustering-based ranking (HCR), seems to be more robust than BMR with respect to this problem, but it is hampered by theoretical and practical limitations. We present an approach to document ranking that explicitly addresses the word mismatch problem by exploiting interdocument similarity information in a novel way. Document ranking is seen as a query-document transformation driven by a conceptual representation of the whole document collection, into which the query is merged. Our approach is nased on the theory of concept (or Galois) lattices, which, er argue, provides a powerful, well-founded, and conputationally-tractable framework to model the space in which documents and query are represented and to compute such a transformation. We compared information retrieval using concept lattice-based ranking (CLR) to BMR and HCR. The results showed that HCR was outperformed by CLR as well as BMR, and suggested that, of the two best methods, BMR achieved better performance than CLR on the whole document set, whereas CLR compared more favorably when only the first retrieved documents were used for evaluation. We also evaluated the three methods' specific ability to rank documents that did not match the query, in which case the speriority of CLR over BMR and HCR was apparent
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Prediger, S.: Kontextuelle Urteilslogik mit Begriffsgraphen : Ein Beitrag zur Restrukturierung der mathematischen Logik (1998)
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- Date
- 26. 2.2008 15:58:22
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De Maio, C.; Fenza, G.; Loia, V.; Senatore, S.: Hierarchical web resources retrieval by exploiting Fuzzy Formal Concept Analysis (2012)
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- Abstract
- In recent years, knowledge structuring is assuming important roles in several real world applications such as decision support, cooperative problem solving, e-commerce, Semantic Web and, even in planning systems. Ontologies play an important role in supporting automated processes to access information and are at the core of new strategies for the development of knowledge-based systems. Yet, developing an ontology is a time-consuming task which often needs an accurate domain expertise to tackle structural and logical difficulties in the definition of concepts as well as conceivable relationships. This work presents an ontology-based retrieval approach, that supports data organization and visualization and provides a friendly navigation model. It exploits the fuzzy extension of the Formal Concept Analysis theory to elicit conceptualizations from datasets and generate a hierarchy-based representation of extracted knowledge. An intuitive graphical interface provides a multi-facets view of the built ontology. Through a transparent query-based retrieval, final users navigate across concepts, relations and population.
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Kaytoue, M.; Kuznetsov, S.O.; Assaghir, Z.; Napoli, A.: Embedding tolerance relations in concept lattices : an application in information fusion (2010)
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- Abstract
- Formal Concept Analysis (FCA) is a well founded mathematical framework used for conceptual classication and knowledge management. Given a binary table describing a relation between objects and attributes, FCA consists in building a set of concepts organized by a subsumption relation within a concept lattice. Accordingly, FCA requires to transform complex data, e.g. numbers, intervals, graphs, into binary data leading to loss of information and poor interpretability of object classes. In this paper, we propose a pre-processing method producing binary data from complex data taking advantage of similarity between objects. As a result, the concept lattice is composed of classes being maximal sets of pairwise similar objects. This method is based on FCA and on a formalization of similarity as a tolerance relation (reexive and symmetric). It applies to complex object descriptions and especially here to interval data. Moreover, it can be applied to any kind of structured data for which a similarity can be dened (sequences, graphs, etc.). Finally, an application highlights that the resulting concept lattice plays an important role in information fusion problem, as illustrated with a real-world example in agronomy.
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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
