2015 International Conference on Big Data and Smart Computing (BigComp) (2015)
Jeju, South Korea
Feb. 9, 2015 to Feb. 11, 2015
Chae-Gyun Lim , Dept. of Computer Engineering, Kyung Hee University 1732 Deogyeong-daero, Giheung-gu, Yongin-si, Korea
Byeong-Soo Jeong , Dept. of Computer Engineering, Kyung Hee University 1732 Deogyeong-daero, Giheung-gu, Yongin-si, Korea
Ho-Jin Choi , Dept. of Computer Science, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Korea
Due to the huge number of research articles in the biomedical domain, it becomes more and more important to develop methods to find relevant articles of our specific research interests. Keyword extraction is a useful method to find important topics from documents and summarize their major information. Unfortunately, it is hard to select appropriate keywords extracted by traditional method of keyword extraction from specific research fields such as biomedical domain. Although human experts can support to understand details of the keywords, extra time should be required to read contents of the documents. In this paper, we propose a method for suggesting keyword-based topics for unseen biomedical research articles from PubMed. Our method uses MeSH descriptors to summarize each document by obtaining frequencies of them. The list of frequencies is used to make keyword suggestions for given documents based on the MeSH. In the experiments, we evaluate the performance of the method by measuring the accuracy of keyword suggestions for a given set of unseen documents.
Accuracy, Ontologies, Libraries, Semantics, Labeling, Data mining, Information retrieval
C. Lim, B. Jeong and H. Choi, "Suggesting biomedical topics for unseen research articles based on MeSH descriptors," 2015 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Jeju, South Korea, 2015, pp. 51-54.