BioProber: Software System for Biomedical Relation Discovery from PubMed

@inproceedings{citeulike:1673458, abstract = {The numbers of articles and journals that are published are increasing at a considerable rate, and the published information is growing continuously and fast. Because of this, researches to acquire knowledge automatically have been carried out in the areas of information retrieval, information extraction and text mining. Information retrieval approaches are good for specific topics that the number of related articles is small. But, if the number is bigger, searching skill and knowledge acquisition ability are useless. Though many efforts have been made to extract information from literature, many approaches have concentrated on specific entities, such as proteins, genes and their interactions, and much information is still remained in unstructured text. So, we have developed a system that discovers relations between various categories of biomedical entities. Our system collects abstracts from PubMed by queries representing a topic and visualizes relationship from the collection by automatic information extraction}, author = {Jang, Hyunchul and Lim, Jaesoo and Lim, Joon-Ho and Park, Soo-Jun and Lee, Kyu-Chul }, booktitle = {Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE}, citeulike-article-id = {1673458}, journal = {Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE}, keywords = {nlp}, pages = {5779--5782}, posted-at = {2007-09-19 06:03:08}, priority = {2}, title = {BioProber: Software System for Biomedical Relation Discovery from PubMed}, url = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=4029736}, year = {2006} }

TY - CONF ID - citeulike:1673458 L3 - citeulike-article-id:1673458 TI - BioProber: Software System for Biomedical Relation Discovery from PubMed T2 - Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE JF - Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE SP - 5779 EP - 5782 N2 - The numbers of articles and journals that are published are increasing at a considerable rate, and the published information is growing continuously and fast. Because of this, researches to acquire knowledge automatically have been carried out in the areas of information retrieval, information extraction and text mining. Information retrieval approaches are good for specific topics that the number of related articles is small. But, if the number is bigger, searching skill and knowledge acquisition ability are useless. Though many efforts have been made to extract information from literature, many approaches have concentrated on specific entities, such as proteins, genes and their interactions, and much information is still remained in unstructured text. So, we have developed a system that discovers relations between various categories of biomedical entities. Our system collects abstracts from PubMed by queries representing a topic and visualizes relationship from the collection by automatic information extraction KW - nlp AU - Jang, Hyunchul AU - Lim, Jaesoo AU - Lim, Joon-Ho AU - Park, Soo-Jun AU - Lee, Kyu-Chul PY - 2006/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4029736 ER -