%0 Conference Paper %1 taheriyan2011subject %A Taheriyan, Mohsen %B Proceedings of the 2011 Workshop on Knowledge Discovery, Modeling and Simulation %C New York, NY, USA %D 2011 %I ACM %K citation classification paper research subject topic %P 39--44 %R 10.1145/2023568.2023579 %T Subject Classification of Research Papers Based on Interrelationships Analysis %U http://doi.acm.org/10.1145/2023568.2023579 %X Finding scientific papers and journals relevant to a particular area of research is a concern for many people including students, professors, and researchers. A subject classification of papers facilitates the search process. That is, having a list of subjects in a research field, we try to find out to which subject(s) a given paper is more related. This task can be done manually by, for example, asking authors to assign one or more categories at submit time. However, categorizing a large collection of resources manually is a time consuming process. In automatic methods, a naive strategy is to do a keyword-based search for the subject term in paper's title, keywords, and even its fulltext. Nonetheless, this approach fails for resources employing semantically equivalent terms but not exactly the same subject words. Besides, processing the whole text of a paper takes a long time. In this paper, we introduce a novel supervised approach for subject classification of scientific articles based on analysis of their interrelationships. We exploit links such as citations, common authors, and common references to assign subject to papers. Our experimental results show that our approach works well especially when the graph of relationships is dense enough, i.e., there are significant number of links among papers. %@ 978-1-4503-0836-6

@inproceedings{taheriyan2011subject, abstract = {Finding scientific papers and journals relevant to a particular area of research is a concern for many people including students, professors, and researchers. A subject classification of papers facilitates the search process. That is, having a list of subjects in a research field, we try to find out to which subject(s) a given paper is more related. This task can be done manually by, for example, asking authors to assign one or more categories at submit time. However, categorizing a large collection of resources manually is a time consuming process. In automatic methods, a naive strategy is to do a keyword-based search for the subject term in paper's title, keywords, and even its fulltext. Nonetheless, this approach fails for resources employing semantically equivalent terms but not exactly the same subject words. Besides, processing the whole text of a paper takes a long time. In this paper, we introduce a novel supervised approach for subject classification of scientific articles based on analysis of their interrelationships. We exploit links such as citations, common authors, and common references to assign subject to papers. Our experimental results show that our approach works well especially when the graph of relationships is dense enough, i.e., there are significant number of links among papers.}, acmid = {2023579}, added-at = {2014-04-29T08:13:15.000+0200}, address = {New York, NY, USA}, author = {Taheriyan, Mohsen}, biburl = {https://www.bibsonomy.org/bibtex/2c56efe6e2a7a21d69bc2e81dad8c4462/jaeschke}, booktitle = {Proceedings of the 2011 Workshop on Knowledge Discovery, Modeling and Simulation}, description = {Subject classification of research papers based on interrelationships analysis}, doi = {10.1145/2023568.2023579}, interhash = {a5078335cb6e985ad014bf6749b8c77c}, intrahash = {c56efe6e2a7a21d69bc2e81dad8c4462}, isbn = {978-1-4503-0836-6}, keywords = {citation classification paper research subject topic}, location = {San Diego, California, USA}, numpages = {6}, pages = {39--44}, publisher = {ACM}, series = {KDMS '11}, timestamp = {2014-07-28T15:57:31.000+0200}, title = {Subject Classification of Research Papers Based on Interrelationships Analysis}, url = {http://doi.acm.org/10.1145/2023568.2023579}, year = 2011 }