An incremental algorithm to construct a lattice of set intersections

Kourie, Derrick G.; Obiedkov, Sergei; Watson, Bruce W.; Merwe, Dean van der

doi:10.1016/j.scico.2008.09.015

Cited by 43 publications

(25 citation statements)

References 12 publications

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“…Then, by using the incremental updating algorithm of the concept lattice [11,12,26,27], we drew a general weighted concept lattice (WCL) for representing the merged ontologies, which was induced from the formal context in Table 3.…”

Section: Case Study and Discussionmentioning

confidence: 99%

An Entropy-Based Weighted Concept Lattice for Merging Multi-Source Geo-Ontologies

Zhu

2013

Entropy

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Abstract:To deal with the complexities associated with the rapid growth in a merged concept lattice, a formal method based on an entropy-based weighted concept lattice (EWCL) is proposed as a mechanism for merging multi-source geographic ontologies (geo-ontologies). First, formal concept analysis (FCA) is used to formalize different term-based representations in relation to the geographic domain, and to construct a merged formal context. Second, a weighted concept lattice (WCL) is applied to reduce the merged concept lattice, based on information entropy and a deviance analysis. The entropy of the attribute set is exploited to acquire the intent weight value, and the standard deviation contributes to computing the intent importance deviance value, according to the user preferences and interests. Some nodes of the merged concept lattice are then removed if their intent weights are lower than the intent importance thresholds specified by the user. Finally, experiments were conducted by combining fundamental geographic information data and spatial data in the hydraulic engineering domain from China. The results indicate that the proposed method is feasible and valid for reducing the complexities associated with the merging of geo-ontologies. Although there are still some problems in the application, the manuscript offers a new approach for the merging of geo-ontologies.

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Section: Case Study and Discussionmentioning

confidence: 99%

An Entropy-Based Weighted Concept Lattice for Merging Multi-Source Geo-Ontologies

Zhu

2013

Entropy

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“…In general, we can divide these algorithms into two categories including batch algorithms (Ganter, 2010;Kuznetsov, 1993;Outrata & Vychodil, 2012) and incremental algorithms (Godin et al, 1995;Kourie et al, 2009;Valtchev & Missaoui, 2001;Van Der Merwe et al, 2004). Batch algorithms usually construct the lattice in a bottom-up (or top-down) approach, while incremental algorithms compute the lattice by adding objects (or attributes) of a given context one by one.…”

Section: Incremental Algorithmsmentioning

confidence: 99%

“…Therefore, the correctness of the proposed algorithm follows the correctness of the AddIntent algorithm and the above descriptions of our proposed improvements. The reader is kindly referred to Kourie et al (2009) and Van der Merwe (2006) for the correctness of the AddIntent algorithm.…”

Section: Proposed Improvementsmentioning

confidence: 99%

“…The algorithm we propose is a refinement of AddIntent (Kourie, Obiedkov, Watson, & van der Merwe, 2009;Van Der Merwe et al, 2004) in which we improve two fundamental procedures including fixing the lattice order relation and searching for canonical generators. The improvements make the algorithm perform considerably better than AddIntent when applied to relatively large or (and) dense datasets.…”

Section: Introductionmentioning

confidence: 99%

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A fast incremental algorithm for constructing concept lattices

Zou

Zhang

Long

2015

Expert Systems with Applications

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“…However, this occurs rarely in practical applications, as witnessed by a number of theoretical and experimental findings. Several algorithms have been developed for constructing a concept lattice [24], [29]. These algorithms aim to solve the problem of generating the set of all concepts and the concept lattice for a formal context.…”

Section: Lattice Constructionmentioning

confidence: 99%

Mining and Representing User Interests: The Case of Tagging Practices

Kim

Breslin

Decker

et al. 2011

IEEE Trans. Syst., Man, Cybern. A

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Abstract-Social tagging in online communities has become an important method for reflecting classified thoughts of individual users. A number of social Web sites provide tagging functionalities and also offer folksonomies within or across the sites. However, it is practically not easy to find users' interests based on such folksonomies. In this paper, we provide a novel approach for clustering user-centric interests by analyzing tagging practices of individual users. To do this, we collect Really Simple Syndication data from blogosphere, find conceptual clusters using formal concept analysis, and then evaluate the significance of these clusters. The results of the empirical evaluation show that we can effectively recommend different collections of tags to an individual or a set of users.

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An incremental algorithm to construct a lattice of set intersections

Cited by 43 publications

References 12 publications

An Entropy-Based Weighted Concept Lattice for Merging Multi-Source Geo-Ontologies

An Entropy-Based Weighted Concept Lattice for Merging Multi-Source Geo-Ontologies

A fast incremental algorithm for constructing concept lattices

Mining and Representing User Interests: The Case of Tagging Practices

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