Discovering Semantic Relationships between Concepts from MEDLINE

被引：1
|
作者：
Yan, Peng [1 ]
Jin, Wei [1 ]
Jha, Kishlay [1 ]
机构：
[1] North Dakota State Univ, Dept Comp Sci, Fargo, ND 58108 USA
来源：
2016 IEEE TENTH INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC) | 2016年
关键词：
hypothesis generation; text mining; cross-document knowledge discovery; GENERATING HYPOTHESES; FISH-OIL;
D O I：
10.1109/ICSC.2016.9
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents an approach for detecting unapparent links between two concepts from MEDLINE. Given two topics A and C, for example, two biomedical concepts "fish oil" and "raynauds", we attempt to find the best concept chain and evidence trail that connect them across documents. The technique begins with developing concept profiles for each input topic, and then grouping relevant concepts by semantic types, and finally generating ranked concept chains and evidence trails connecting topics. Biomedical corpus from MEDLINE is used to evaluate the performance of the system and demonstrates the effectiveness of the algorithm. The system provides a handy user interface for initializing the environment, customizing semantic types of interest, and viewing "bridge" concepts connecting topics.
引用
收藏
页码：369 / 372
页数：4
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