A Fuzzy Ontological Knowledge Document Clustering Methodology

Trappey, Amy J.C.; Trappey, Charles V.; Hsu, Fu-Chiun; Hsiao, David W.

doi:10.1109/tsmcb.2008.2009463

Cited by 54 publications

(26 citation statements)

References 14 publications

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“…Cheng and C.P. Wei [2] [16] suggested a novel hierarchal clustering approach for knowledge document self organizing using fuzzy ontology. This method has been used for automatic interpretation and knowledge documents clustering using ontology schema.…”

Section: Related Workmentioning

confidence: 99%

ONTOLOGY BASED CLUSTERING IN RESEARCH PROJECT SELECTION & ASSIGN PROPOSALS TO EXPERTS BY ONTOLOGY MATCHING

Lavanya¹

2014

IJRET

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Research Project Selection is a remarkable bustle in all Research funding and Government agencies. Selection and Assignment processes are unproblematic when number of proposal submission is less. But with mounting in numbers, both processes will become complicated. Through traditional methodologies, Proposal Grouping, Experts grouping and Proposal assignment to experts are done manually or via keywords which yield inefficient results. In recent methodologies, Ontology based Text Mining Method (OTMM) has been used to cluster Proposals based on their likeness in research area by means of Research Ontology. Whereas, Reviewers grouping and proposal assignment to experts is still not precise since it is done manually. This paper proposes Ontology based Text Mining Method to cluster reviewers based on their specialism and overcome the Ontology Mapping Problem to allocate proposals to appropriate reviewers analytically. This methodology affords an effective way for research project selection with escalating figure of proposals and reviewers.

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Section: Related Workmentioning

confidence: 99%

ONTOLOGY BASED CLUSTERING IN RESEARCH PROJECT SELECTION & ASSIGN PROPOSALS TO EXPERTS BY ONTOLOGY MATCHING

Lavanya¹

2014

IJRET

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“…Although there are several text-mining approaches that can be used to cluster and classify documents [20]- [27], they are developed with a focus on English text. TMMs which deal with English are not effective in processing Chinese text [28].…”

Section: Ontology-based Text Mining To Cluster Research Proposalsmentioning

confidence: 99%

An Ontology-Based Text-Mining Method to Cluster Proposals for Research Project Selection

Sun

et al. 2012

IEEE Trans. Syst., Man, Cybern. A

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Abstract-Research project selection is an important task for government and private research funding agencies. When a large number of research proposals are received, it is common to group them according to their similarities in research disciplines. The grouped proposals are then assigned to the appropriate experts for peer review. Current methods for grouping proposals are based on manual matching of similar research discipline areas and/or keywords. However, the exact research discipline areas of the proposals cannot often be accurately designated by the applicants due to their subjective views and possible misinterpretations. Therefore, rich information in the proposals' full text can be used effectively. Text-mining methods have been proposed to solve the problem by automatically classifying text documents, mainly in English. However, these methods have limitations when dealing with non-English language texts, e.g., Chinese research proposals. This paper presents a novel ontology-based text-mining approach to cluster research proposals based on their similarities in research areas. The method is efficient and effective for clustering research proposals with both English and Chinese texts. The method also includes an optimization model that considers applicants' characteristics for balancing proposals by geographical regions. The proposed method is tested and validated based on the selection process at the National Natural Science Foundation of China. The results can also be used to improve the efficiency and effectiveness of research project selection processes in other government and private research funding agencies.

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“…Patent analysis provides a means to predict product maturity and market trends (Trappey, Trappey, Hsu & Hsiao 2009). Patent analysis is employed across organizations and is a research approach frequently used by R&D engineers, academics, patent attorneys, and technology policy makers.…”

Section: Patent Analysismentioning

confidence: 99%

Clustering patents using non-exhaustive overlaps

Trappey

2010

J. Syst. Sci. Syst. Eng.

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Patent documents are unique external sources of information that reveal the core technology underlying new inventions. Patents also serve as a strategic data source that can be mined to discover state-of-the-art technical development and subsequently help guide R&D investments. This research incorporates an ontology schema to extract and represent patent concepts. A clustering algorithm with non-exhaustive overlaps is proposed to overcome deficiencies with exhaustive clustering methods used in patent mining and technology discovery. The non-exhaustive clustering approach allows for the clustering of patent documents with overlapping technical findings and claims, a feature that enables the grouping of patents that define related key innovations. Legal advisors can use this approach to study potential cases of patent infringement or devise strategies to avoid litigation. The case study demonstrates the use of non-exhaustive overlaps algorithm by clustering US and Japan radio frequency identification (RFID) patents and by analyzing the legal implications of automated discovery of patent infringement.

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A Fuzzy Ontological Knowledge Document Clustering Methodology

Cited by 54 publications

References 14 publications

ONTOLOGY BASED CLUSTERING IN RESEARCH PROJECT SELECTION & ASSIGN PROPOSALS TO EXPERTS BY ONTOLOGY MATCHING

ONTOLOGY BASED CLUSTERING IN RESEARCH PROJECT SELECTION & ASSIGN PROPOSALS TO EXPERTS BY ONTOLOGY MATCHING

An Ontology-Based Text-Mining Method to Cluster Proposals for Research Project Selection

Clustering patents using non-exhaustive overlaps

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