-
Priss, U.: Faceted information representation (2000)
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- Abstract
- This paper presents an abstract formalization of the notion of "facets". Facets are relational structures of units, relations and other facets selected for a certain purpose. Facets can be used to structure large knowledge representation systems into a hierarchical arrangement of consistent and independent subsystems (facets) that facilitate flexibility and combinations of different viewpoints or aspects. This paper describes the basic notions, facet characteristics and construction mechanisms. It then explicates the theory in an example of a faceted information retrieval system (FaIR)
- Date
- 22. 1.2016 17:47:06
-
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
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Wille, R.: Line diagrams of hierarchical concept systems (1984)
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-
Wille, R.: Concept lattices and conceptual knowledge systems (1992)
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-
Priss, U.; Old, L.J.: Concept neighbourhoods in knowledge organisation systems (2010)
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- Abstract
- This paper discusses the application of concept neighbourhoods (in the sense of formal concept analysis) to knowledge organisation systems. Examples are provided using Roget's Thesaurus, WordNet and Wikipedia categories.
- Source
- Paradigms and conceptual systems in knowledge organization: Proceedings of the Eleventh International ISKO Conference, 23-26 February 2010 Rome, Italy. Edited by Claudio Gnoli and Fulvio Mazzocchi
-
Scheich, P.; Skorsky, M.; Vogt, F.; Wachter, C.; Wille, R.: Conceptual data systems (1993)
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-
Rusch, A.; Wille, R.: Knowledge spaces and formal concept analysis (1996)
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- Source
- Data analysis and information systems, statistical and conceptual approaches: Proceedings of the 19th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Basel, March 8-10, 1995. Ed.: H.-H. Bock u. W. Polasek
-
Eschenfelder, D.; Kollewe, W.; Skorsky, M.; Wille, R.: ¬Ein Erkundungssystem zum Baurecht : Methoden der Entwicklung eines TOSCANA-Systems (2000)
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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
-
Reinartz, T.P.; Zickwolff, M.: ¬Two conceptual approaches to acquire human expert knowledge in a complex real world domain (1996)
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- Source
- Data analysis and information systems, statistical and conceptual approaches: Proceedings of the 19th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Basel, March 8-10, 1995. Ed.: H.-H. Bock u. W. Polasek
-
Groh, B.; Strahringer, S.; Wille, R.: TOSCANA-systems based on thesauri (1998)
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-
Luksch, P.; Wille, R.: ¬A mathematical model for conceptual knowledge systems (1991)
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- Abstract
- Objects, attributes, and concepts are basic notations of conceptual knowledge; they are linked by the following four basic relations: an object has an attribute, an object belongs to a concept, an attribute abstracts from a concept, and a concept is a subconcept of another concept. These structural elements are well mathematized in formal concept analysis. Therefore, conceptual knowledge systems can be mathematically modelled in the frame of formal concept analysis. How such modelling may be performed is indicated by an example of a conceptual knowledge system. The formal definition of the model finally clarifies in which ways representation, inference, acquisition, and communication of conceptual knowledge can be mathematically treated
-
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.
-
Neuss, C.; Kent, R.E.: Conceptual analysis of resource meta-information (1995)
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- Source
- Computer networks and ISDN systems. 27(1995) no.6, S.973-984
-
Priss, U.: Comparing classification systems using facets (2000)
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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
-
Hereth, J.; Stumme, G.; Wille, R.; Wille, U.: Conceptual knowledge discovery and data analysis (2000)
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- Abstract
- In this paper, we discuss Conceptual Knowledge Discovery in Databases (CKDD) in its connection with Data Analysis. Our approach is based on Formal Concept Analysis, a mathematical theory which has been developed and proven useful during the last 20 years. Formal Concept Analysis has led to a theory of conceptual information systems which has been applied by using the management system TOSCANA in a wide range of domains. In this paper, we use such an application in database marketing to demonstrate how methods and procedures of CKDD can be applied in Data Analysis. In particular, we show the interplay and integration of data mining and data analysis techniques based on Formal Concept Analysis. The main concern of this paper is to explain how the transition from data to knowledge can be supported by a TOSCANA system. To clarify the transition steps we discuss their correspondence to the five levels of knowledge representation established by R. Brachman and to the steps of empirically grounded theory building proposed by A. Strauss and J. Corbin
-
Priss, U.; Jacob, E.: Utilizing faceted structures for information systems design (1999)
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