Research on Concept Lattice Based Personalized Information Retrieval

Tang, Jun; Du, Yajun; Qi, Liang

doi:10.1109/icmlc.2007.4370851

Cited by 5 publications

(3 citation statements)

References 12 publications

(10 reference statements)

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“…FCA [6] is an effective data analysis technique, which automatically generates hierarchies called concept lattices from contexts. Recently, concept lattices have already been successfully applied to a wide range of scientific disciplines such as knowledge discovery [1, 3, 4, 5, 8-11, 15, 18], information retrieval [2,13,16,19], software engineering [12,20], rough set theory [17,21,23,25], and knowledge ontology [7,14].…”

Section: Introductionmentioning

confidence: 99%

The Maximal Relation Based on A Given Relation Schema and Its Concept Lattice

Lei¹,

Sui²,

Tian³

2013

JSW

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Formal concept analysis (FCA) is a valid tool for data mining and knowledge discovery,
which identifies concept lattices from binary relations. Given a nonempty finite set $A$ of binary attributes, one obtains a maximal binary relation R^max based on a relation schema S(A). Firstly, we analyze concepts in R^max and the concept lattice L(R^max, and there are some important results as follows: for any two concepts in R^max, the union of their intents is an intent of some concept in R^max, and further the intent of their supremum is the union of their intents;
for any two concepts in R^max, if one of them is not a sub-concept or super-concept of the other one, then the union of their extents is not an extent of any concept in R^max; L(R^max) is a complemented distributive lattice. Secondly, we provide the structural connection between L(R) and L(R^max: for any relation R based on S(A), there is a supremum-preserving order-embedding map from L(R) to L(R^max), and conversely, there is an infimum-preserving order-preserving map from L(R^max) to L(R), which is generally not a surjective homomorphism.
Thirdly, we propose two algorithms to extract concepts in R from L(R^max), which are respectively based on intents and extents of concepts, and prove their soundness. These results have already been used to analyze the data in architectual engineering and medical science

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Section: Introductionmentioning

confidence: 99%

The Maximal Relation Based on A Given Relation Schema and Its Concept Lattice

Lei¹,

Sui²,

Tian³

2013

JSW

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“…FCA focuses on the lattice structure induced by a binary relation between a pair of sets (called objects and attributes, respectively) [1] . Recently, as an effective tool for data analysis and knowledge processing, the theory of FCA has widely been applied various fields such as data mining, and knowledge discovery [2−7] , and information retrieval [8,9] .…”

Section: Introductionmentioning

confidence: 99%

An Approach to Text Knowledge Analysis Based on Many-Valued Context

Zhong-he

Nie

Wang

2009

2009 International Conference on Management and Service Science

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Text knowledge analysis plays a very important role in knowledge processing. In order to find interesting patters, we introduce text analysis into the Formal Concept Analysis(FCA) field. FCA is a valid tool for data ming and knowledge processing. In the paper, we present a method for exacting the conceptual patterns and concept lattice from multiple knowledge sources. The method consists of two steps: the first is to insert some related many-valued contexts(MVCs)(which represent text knowledge) into some corresponding column or cell of other MVC in accordance with certain rules, and then extract formal concepts and concept lattice. Our insertion method not only is semanticpreserving, but adds the dimension of attributes for describing objects. Hence, the discovered concepts have the more richer intents and are more denser in the concept lattice. The paper mainly applied the method to column insertion operation and cell insertion operation, and obtained some propositions, which can be used by knowledge engineers to represent and analyze text knowledge (especially,text objects) and to extract new semantics information or associate rules from concept lattices.

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“…Ever since the introduction of FCA, concept lattices have been proven to be valid tools for data analysis and applied widespread applications such as grid knowledge management [2][3][4][5], knowledge discovery [6][7][8][9][10][11], information retrieval [12], and ontology design and merging [13,14].…”

Section: Introductionmentioning

confidence: 99%

The Logical Operations on Relations, Scales and Concept Lattices

Lei¹,

Sui²,

Cao³

2009

2009 Fifth International Conference on Semantics, Knowledge and Grid

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Object-attribute-value-relationships, which are a frequently used data structure to code real-world problems, are formalized via many-valued contexts in Formal Concept Analysis (FCA). The aim of FCA is to explore (formal) concepts from empirical data contexts. A concept of a context consists of two parts: the extent (objects the concept covers) and the intent (attributes describing the concept). From the logical point of view, the intent of each concept is a conjunction of some attributes. Similar to conjunction, negation and disjunction are also important logical operations of attributes or attributevalue pairs, which are common in human language. However, the classical FCA as a mathematical theory of concepts lacks a theory of negation and disjunction. In this paper, we take negation and disjunction into consideration in the process of constructing concepts, and hence obtain the following extended concepts: negative concepts of a context (i.e., a binary relation), negative concepts of a relation with some scales, ∨-concepts of a relation, ∨-concepts of a relation with some scales, logical concepts of a relation, and logical concepts of a relation with some scales. Compared with concepts in the classical FCA, the extended concepts mentioned above are more pertinent and more meaningful.

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Research on Concept Lattice Based Personalized Information Retrieval

Cited by 5 publications

References 12 publications

The Maximal Relation Based on A Given Relation Schema and Its Concept Lattice

The Maximal Relation Based on A Given Relation Schema and Its Concept Lattice

An Approach to Text Knowledge Analysis Based on Many-Valued Context

The Logical Operations on Relations, Scales and Concept Lattices

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