To construct an intelligent biomedical knowledge management system, researchers had proposed many relation extraction methods in past. Before applying these methods, the system has to recognize the name entities in the literature and map the entities to the relative EntrezIDs. The purpose of this study is to automatically and exactly identify the relative EntrezIDs which are mentioned in literatures. We employ the similarity-based inference network to calculate the similarity score with the entities, and this EntrezID is a solution to the term variation problem. The proposed de-ambiguity strategy increases the confidence of EntrezID in literature. The strategy provides researchers a good utilization of information for mapping the entity to the EntrezID. As a result, the precision of system increase about 75.1%, and it makes the identified entity even more meaningful. The system using the proposed strategies outperforms the previous methods in biomedical entity normalization.
['Chih-Hsuan Wei', 'I-Chin Huang', 'Yi-Yu Hsu', 'Hung-Yu Kao']
Normalizing Biomedical Name Entities by Similarity-Based Inference Network and De-ambiguity Mining