Matching knowledge elements in concept maps using a similarity flooding algorithm

Marshall, Byron; Chen, Hsinchun; Madhusudan, Therani

doi:10.1016/j.dss.2005.10.009

Cited by 37 publications

(26 citation statements)

References 15 publications

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“…However, in other cases, the automated transformation of a PPI description into a structured definition is associated with considerable challenges. These challenges mainly relate to the flexible and inherently ambiguous nature of natural language, as preferred by human users, but much less suitable for automated interpretation [182,250]. We identify three main challenges in this regard.…”

Section: Is Ppi Formalizationmentioning

confidence: 99%

Comparing and Aligning Process Representations

2018

Lecture Notes in Business Information Processing

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Section: Is Ppi Formalizationmentioning

confidence: 99%

Comparing and Aligning Process Representations

2018

Lecture Notes in Business Information Processing

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“…2) Select the candidate whose knowledge is most similar with the knowledge of existing staffs in the team [3,4]. Higher knowledge similarity [5,6] among staffs means a A new construction method of the scientific and technological innovation team and its multi-agent simulation better knowledge communication among members, and can improve the overall cooperation capacity of the team.…”

Section: The Process Of Team Construction Of Our Proposed Methodsmentioning

confidence: 99%

A new construction method of the scientific and technological innovation team and its multi-agent simulation

Sun

Hua-bo

et al. 2013

2013 25th Chinese Control and Decision Conference (CCDC)

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For overcoming the shortcomings of the existing method of the construction of scientific and technological innovation team, this paper proposes a new method. In the new method, the selected candidate has the higher similarity not only with the knowledge that team needs, but also with the selected persons previously. Therefore, this method may increase the knowledge communication greatly. By the simulation based on the Repast platform to compare the old method and our proposed method of the construction of scientific and technological innovation team, we validate the novel approach of scientific and technology innovative has the advantage..

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“…Marshall et al [20] use a lexical similarity between elements occurs after matching the substructures. Pan et al [24] use key terms and abbreviations in place of string matching to improve the recall.…”

Section: Related Workmentioning

confidence: 99%

“…Similarity flooding has been used to match structured data such as concepts [20,24] to identify similar structures and concepts. Marshall et al [20] use a lexical similarity between elements occurs after matching the substructures.…”

Section: Related Workmentioning

confidence: 99%

Enforcing strictness in integration of dimensions

Riazati

Thom

Zhang

2011

Proceedings of the ACM 14th International Workshop on Data Warehousing and OLAP

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Maintaining strictness in dimensions is important in integration of data warehouses. A dimension that satisfies all of its roll-up constraints is said to be strict, a property that is required for correct aggregation. Existing work on instance matching does not address the problem of enforcing the strictness of roll-up constraints. In this paper, we use a graph matching-based approach to dimension instance matching and propose an algorithm that enforces strictness and reduces false positives. Making use of similarity flooding, the graph matching algorithm can be greedy in identifying matching members, we propose heuristics to further reduce false positive matches and reduce false strictness. Experiments on real-world data demonstrates the effectiveness of our proposed approach.

show abstract

Matching knowledge elements in concept maps using a similarity flooding algorithm

Cited by 37 publications

References 15 publications

Comparing and Aligning Process Representations

Comparing and Aligning Process Representations

A new construction method of the scientific and technological innovation team and its multi-agent simulation

Enforcing strictness in integration of dimensions

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