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Formal Concept Analysis in Information Science
 ANNUAL REVIEW OF INFORMATION SCIENCE AND TECHNOLOGY
, 1996
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Attribute Exploration With Background Knowledge
 Theoretical Computer Science
, 1996
"... this article is to describe a generalized version of a well known knowledge acquistion method, called attribute exploration. To get a rough idea of what these explorations are about, imagine you want to classify some collection G of items according to selected properties. For example, G could be a c ..."
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Cited by 33 (1 self)
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this article is to describe a generalized version of a well known knowledge acquistion method, called attribute exploration. To get a rough idea of what these explorations are about, imagine you want to classify some collection G of items according to selected properties. For example, G could be a class of mathematical structures, e.g. groups, to be classified by structural properties like "commutative", "nilpotent", etc. Or G could consist of technical devices, car engines for example, and the attributes may reflect properties such as reliability, weight, price, and so on. But G might also be a set of persons, perhaps the students of your university, and the classifying attributes may be field of study, age, degree, etcetera. Attribute exploration then would help you to explore the implicational logic of these attributes.
A settheoretical approach for the induction of inheritance hierarchies
 Electronic Notes in Theoretical Computer Science
, 2001
"... An approach for the automatic construction of inheritance hierarchies is presented. It is based on the strict settheoretical point of view in the mathematical theory of Formal Concept Analysis. The resulting hierarchies are concept lattices. An extension of the approach to the induction of nonmonot ..."
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Cited by 15 (4 self)
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An approach for the automatic construction of inheritance hierarchies is presented. It is based on the strict settheoretical point of view in the mathematical theory of Formal Concept Analysis. The resulting hierarchies are concept lattices. An extension of the approach to the induction of nonmonotonic inheritance networks is also discussed. It turns out that the main ideas of Formal Concept Analysis, i. e. the formal context, the concept lattice and the set of implications, provide three different ways of looking at the data to be represented, each of which provides a different way to solve problems of knowledge representation. 1
Exploration Tools in Formal Concept Analysis
 Opitz (Eds.): Ordinal and Symbolic Data Analysis. Proc. OSDA’95. Studies in Classification, Data Analysis, and Knowledge Organization 8
, 1995
"... The development of conceptual knowledge systems specifically requests knowledge acquistion tools within the framework of Formal Concept Analysis. In this paper, the existing tools are presented, and further developments are discussed. ..."
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Cited by 13 (7 self)
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The development of conceptual knowledge systems specifically requests knowledge acquistion tools within the framework of Formal Concept Analysis. In this paper, the existing tools are presented, and further developments are discussed.
Concept Exploration  A Tool for Creating and Exploring Conceptual Hierarchies
 IN PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON CONCEPTUAL STRUCTURES
, 1997
"... Concept exploration is a knowledge acquisition tool for interactively exploring the hierarchical structure of finitely generated lattices. Applications comprise the support of knowledge engineers by constructing a type lattice for conceptual graphs, and the exploration of large formal contexts in fo ..."
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Cited by 7 (3 self)
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Concept exploration is a knowledge acquisition tool for interactively exploring the hierarchical structure of finitely generated lattices. Applications comprise the support of knowledge engineers by constructing a type lattice for conceptual graphs, and the exploration of large formal contexts in formal concept analysis.
The Concept Classification of a Terminology Extended by Conjunction and Disjunction
 PRICAI'96: TOPICS IN ARTIFICIAL INTELLIGENCE. LNAI 1114
, 1996
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Manyvalued context analysis using descriptions
 Proceedings of the 9th International Conference on Conceptual Structures: Broadening the Base (Harry S Delugach and Gerd
, 2001
"... Abstract. We propose an approach to manyvalued contexts using formal descriptions instead of scaling. The underlying idea is the philosphical definition of a concept as a set of objects together with the most precise description. We introduce a formal description as a mapping from the set of attrib ..."
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Cited by 3 (0 self)
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Abstract. We propose an approach to manyvalued contexts using formal descriptions instead of scaling. The underlying idea is the philosphical definition of a concept as a set of objects together with the most precise description. We introduce a formal description as a mapping from the set of attributes to the power set of the values (which is extended appropriately to empty cells), assigning to each attribute the set of allowed values. Descriptions are naturally ordered by preciseness. Using this, we can introduce extent and intent according to the philosophical idea, and thus we define concepts. We present a way to restrict the amount of concepts for a manyvalued context by preselecting some descriptions of interest. Furthermore, we introduce implications on descriptions, allowing to investigate relationships between attributes. Within this approach, we reformulate the known theory under a different point of view. It certainly does not provide a better analysis than scaling, but it allows to avoid the generation of a huge onevalued context. 1 Manyvalued Contexts Recall the definition of manyvalued contexts: Definition 1. A manyvalued context IK = (G, M, W, I) is a set of objects G, a set of attributes M, a set of possible values W, and a ternary relation I ⊆ G × M × W, with (g, m, w) ∈ I, (g, m, v) ∈ I = ⇒ w = v. (g, m, w) ∈ I indicates, that object g has the attribute m with value w. In this case, we also write m(g) = w, regarding the attribute m as a partial function from G to W. We can consider in particular each data base as a manyvalued context, thus formal concept analysis appears as a tool of knowledge discovery and datamining. Within this paper, we will consider the following small example (taken from [7]), representing some facts from algebra:
Computing minimal generators from implications: a logicguided approach
"... Abstract. Sets of attribute implications may have a certain degree of redundancy and the notion of basis appears as a way to characterize the implication set with less redundancy. The most widely accepted is the DuquenneGuigues basis, strongly based on the notion of pseudointents. In this work we ..."
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Cited by 2 (2 self)
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Abstract. Sets of attribute implications may have a certain degree of redundancy and the notion of basis appears as a way to characterize the implication set with less redundancy. The most widely accepted is the DuquenneGuigues basis, strongly based on the notion of pseudointents. In this work we propose the minimal generators as an element to remove redundancy in the basis. The main problem is to enumerate all the minimal generators from a set of implications. We introduce a method to compute all the minimal generators which is based on the Simplification Rule for implications. The simplification paradigm allows us to remove redundancy in the implications by deleting attributes inside the implication without removing the whole implication itself. In this work, the application of the Simplification Rule to the set of implications guides the search of the minimal generators in a logicbased style, providing a deterministic approach. 1
PROOF Inheritancebased models of the lexicon
"... A rapid and remarkable development took place within computational linguistics in the years immediately following the introduction of unificationbased models of language, in particular Lexical Functional Grammar (LFG) and Generalized Phrase Structure Grammar (GPSG), which employ feature structures ..."
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Cited by 1 (1 self)
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A rapid and remarkable development took place within computational linguistics in the years immediately following the introduction of unificationbased models of language, in particular Lexical Functional Grammar (LFG) and Generalized Phrase Structure Grammar (GPSG), which employ feature structures to represent linguistic information. By the end of the 1980s a consensus had emerged, according to which the lexicon, which pairs word forms with feature structures, constitutes the main repository of information in a language. Furthermore, hierarchical structuring had come to be viewed as an essential aspect or perhaps even the most salient characteristic of the lexicon (cf. Briscoe et al. 1993). GPSG, as conceived in Gazdar & Pullum (1982) and even in Gazdar et al. (1985), still largely represents the older, dichotomous view of the lexicon. Here major aspects of linguistic structure were encoded in syntactic rules, many of which later came to be regarded as stating the possible complement structures of verbs, i.e. lexical information. Later developments in the treatment of subcategorization are only alluded to in a footnote (cf. Gazdar et al. 1985: 107). While the question “How is a classification imposed on the content of the lexicon by the system of features ” is raised (p. 13), the answer of GPSG does not explicitly model the hierarchical inheritance relations inherent in lexical classifications. Rather, these relations are captured in logical feature cooccurrence restrictions (FCRs) and feature specification defaults (FSDs), the latter of which, prophetically, are nonmonotonic. From the start the lexicalist orientation was prominent in LFG (cf. Bresnan 1982, therein Kaplan & Bresnan 1982) and reached a peak in the radical lexicalism of Karttunen (1986), which uses the framework of categorial grammar to shift the entirety of linguistic description to the lexicon. The move toward the lexicalist view was independent of hierarchical modelling, which emerged in other work. In particular, Flickinger (1987) pioneered the explicit description of relations between English verb classes in terms of inheritance hierarchies. On a separate front, 1
Conceptual reasoning  Belief, multiple agents and preference
, 1998
"... xi Acknowledgements xiii 1 Introduction 1 1.1 Motivation and goals . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Overview and structure . . . . . . . . . . . . . . . . . . . . . . . 1 2 Background 5 2.1 Lattices and order . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Formal Conce ..."
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xi Acknowledgements xiii 1 Introduction 1 1.1 Motivation and goals . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Overview and structure . . . . . . . . . . . . . . . . . . . . . . . 1 2 Background 5 2.1 Lattices and order . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Formal Concept Analysis . . . . . . . . . . . . . . . . . . . . . . 8 2.3 Bilattices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3 Ontology and belief 15 3.1 Language consensus . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2 Misrepresentation . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.3 Believed worlds, or paradigm shift . . . . . . . . . . . . . . . . . 21 3.4 Formulae and regions . . . . . . . . . . . . . . . . . . . . . . . . 27 3.5 Language of descriptions . . . . . . . . . . . . . . . . . . . . . . 31 3.6 Information ordering on abstract objects . . . . . . . . . . . . . 34 3.7 Descriptions and contexts . . . . . . . . . . . . . . . . . . . . . 38 4 Model t...