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

Ma, Jian; Xu, Wei; Sun, Yong-Hong; Turban, Efraim; Wang, Shouyang; Liu, Ou

doi:10.1109/tsmca.2011.2172205

Cited by 78 publications

(43 citation statements)

References 41 publications

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“…In this paper two ontology has been used, so Ontology Matching has to be done to assign Proposals to Experts according to their specialization. This Paper has been extended the work of Jian Ma, Wei Xu and et al [5] and Pavel Shvaiko and Je´ roˆme Euzenat [18]. Because of the extention grouped proposals were assigned to grouped reviewers systematically.…”

Section: Fig 1 Research Project Selection Processmentioning

confidence: 93%

“…Pre requisites have been done as per the paper -An OntologyBased Text-Mining Method to Cluster Proposals for Research Project Selection‖ [5] given by Jian Ma, Wei Xu, Yong-hong Sun, Efraim Turban, Shouyang Wang, and Ou Liu.…”

Section: Research Methodolohymentioning

confidence: 99%

“…Jian Ma, Wei Xu and et al [5] proposed OTMM to cluster and to balance research project proposals. Then these grouped proposals have been assigned to reviewers by hand not systematically.…”

Section: Fig 1 Research Project Selection Processmentioning

confidence: 99%

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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: Fig 1 Research Project Selection Processmentioning

confidence: 93%

Section: Research Methodolohymentioning

confidence: 99%

See 1 more Smart Citation

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

Lavanya¹

2014

IJRET

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“…The technique was found to reduce the gap of distribution between domain source and domain targeted. Ma et al [21] have also used ontology with text mining in order to perform selection of research-based projects. The technique was implemented on both English as well as Chinese text using supervised learning algorithm as well as evolutionary learning algorithm.…”

Section: A Backgroundmentioning

confidence: 99%

RKE-CP: Response-based Knowledge Extraction from Collaborative Platform of Text-based Communication

Jalaja¹,

Kavitha²

2017

ijacsa

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Abstract-With the generation of massive amount of productcentric responses from existing applications on collaborative platform, it is necessary to perform a discrete analytical operation on it. As majority of such responses are textual in nature, it increases the applicability of using text mining approaches on it. We review the existing research contribution in text mining to find that there are significant research gap. Therefore, the proposed study presents a technique called as RKE-CP i.e. Response-based Knowledge Extraction from Collaborative Platform where the term Collaborative points towards cloud environment. The proposed technique is designed using mathematical modelling where the maximum focus of design and implementation lies on accomplishing a good balance between faster response time in mining operation and higher precision/recall rate. The study outcome possess' better precision score, recall, and lowered processing time as compared with the most relevant work text mining.

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“…We will make a brief introduction to ontology. Ontology [15]- [17] is the structural frameworks for organizing information and is widely used in information analysis field, such as artificial intelligence (AI), Semantic analysis. In computer and information science, ontology structure formally represents a set of concepts and knowledge within a domain, where we could obtain additional information by the relationships between those concepts.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Document Clustering Approach with Frequent Itemsets

Lee¹,

Hsu²,

Chen³

2017

IJET

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Abstract-In order to effectively retrieve required information from the large amount of information collected from the Internet, document clustering in text mining becomes a popular research topic. Clustering is the unsupervised classification of data items into groups without the need of training data. Many conventional document clustering methods perform inefficiently for large document of collected information and require special handling for high dimensionality and high volume. We propose the OCFI (Ontology and Closed Frequent Itemset-based Hierarchical Clustering) method, which is a hierarchical clustering method developed for document clustering. OCFI uses common words to cluster documents and builds hierarchical topic tree. In addition, OCFI utilizes ontology to solve the semantic problem and mine the meaning behind the words in documents. Furthermore, we use the closed frequent itemsets instead of only use frequent itemsets, which increases efficiency and scalability. The experimental results reveal that our method is more effective than the well-known document clustering algorithms. The clustering results can be used in the personalized search service to assist users to obtain the information they need.Index Terms-OCFI, documents clustering, ontology, closed frequent itemsets. I. INTRODUCTIONDue to the popularity of the Internet, a large number of documents, reports, e-mails, and web pages cause the information overload problem. Many enterprises spend lots of manpower on organizing these unstructured data into a logical structure for later use. In order to save the manpower and find interesting knowledge effectively, text mining becomes more and more important. information from text, text mining usually involves the process of parsing the input text, mining valuable information through the analysis models, and finally evaluating and interpreting the output. However, most of the text mining algorithms do not have enough capability to solve the problems from text mining, because many methods are modifications of traditional data mining algorithms that were originally designed for relational database. Therefore, traditional clustering algorithms become impractical in real-world document clustering which requires special handling for high dimensionality, high volume, and ease of browsing.Fung proposed Frequent Itemset-based Hierarchical Clustering (FIHC) [11] method to solve the problems from traditional algorithms. FIHC is a hierarchical clustering method developed for document clustering. Clustering or cluster analysis is the task of assigning a set of objects into groups, called clusters, so that the objects in the same cluster are more similar to each other than to those in other clusters. Clustering is a main topic of data mining algorithm, and a common technique for statistical data analysis used in many fields, including machine learning, image analysis, information retrieval, and bioinformatics.A major breakthrough of FIHC is that the clustering algorithm utilizes an important notion, frequent items...

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

Cited by 78 publications

References 41 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

RKE-CP: Response-based Knowledge Extraction from Collaborative Platform of Text-based Communication

A Hierarchical Document Clustering Approach with Frequent Itemsets

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