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- W948832009 abstract "The exponential growth of scientific resources on the web has raised scalability issues like quick retrieval of relevant resource. The major obstacle is attributed to unstructured nature of information. It is estimated that the scientific knowledge is doubling in every five years. This information is being indexed by search engines, citation indexes, and digital libraries etc. Most of the time, the indexed information is unstructured or semi-structured. Therefore, the users are unable to find the relevant research papers. Users are given thousands and sometimes millions of irrelevant hits. This is mainly due to the unstructured nature of information being stored. For a query, the information retrieval may be restricted to the relevant resources if the resources are structured in a classification. Some classifications are being used in different domains such as in computer science, the ACM classification scheme is being used to structure the research papers. However, the ACM classification was majorly updated in 1998 and does not include evolving concepts and domains since then. Furthermore, only ACM papers are classified using ACM classification scheme while other publishers normally do not use this classification. Therefore, the automatic classification becomes a major challenge for scientific community. Several approaches are being used for creating automatic classification on the basis of co-occurrence analysis, keywords analysis, and using domain models ,etc. However, the current approaches have their limitations. In this paper, we proposed and implemented a new approach of finding the evolving classification, termed as concept classifier for the scientific community by aggregating the correlation between title terms and keywords of the paper. Initially the experiment has been conducted over the dataset of journal i.e. Journal of Universal Computer Science (J.UCS) .The resultant taxonomy has been verified and compared with the Growbag dataset (DBLP dataset of co-occurrence terms). The results are promising. The developed concept classifier is useful for classification of scientific documents." @default.
- W948832009 created "2016-06-24" @default.
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- W948832009 date "2012-01-01" @default.
- W948832009 modified "2023-09-25" @default.
- W948832009 title "Exploiting Title-Keywords Relation to Develop Concept Classifier for Scientific Community" @default.
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- W948832009 doi "https://doi.org/10.1007/978-3-642-30567-2_37" @default.
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