A Hybrid Method to Detect Missing Hierarchical Relations in NCI Thesaurus Biomedical terminologies such as National Cancer Institute thesaurus (NCIt) have been widely used in supporting various biomedical research and applications. Therefore, the quality of biomedical terminologies directly impacts their downstream applications. In this paper, we introduce a hybrid method to identify missing hierarchical ISA relations in NCIt, by leveraging both role definitions and lexical features of concepts in nonlattice subgraphs. We first extract nonlattice subgraphs in NCIt, problematic areas with quality issue. We model each concept using its role definitions and words in its concept name as well as words in the names of its ancestors. Then we perform a twostep subsumption testing for candidate pairs of concepts in the nonlattice subgraphs to automatically suggest potentially missing ISA relations. We applied our method to the 19.01d version of NCIt. A total of 9,512 nonlattice subgraphs were extracted, among which 654 of them revealed 268 potentially missing ISA relations. After the removal of duplication and redundancy, 121 potentially missing ISA relations were obtained. To evaluate our method, we adopted a retrospective ground truth (RGT)based idea to use version difference as the reference standard. We constructed a reference standard based on the ISA changes between the 19.01d and 19.07e versions of NCIt. Among 121 potentially missing ISA relations suggested by our method, 46 out of them are valid according to the reference standard. The RGTbased evaluation indicates that our hybrid method is promising in detecting missing ISA relations which motivates us to perform a thorough evaluation by domain experts in future work.

A neural networkbased joint learning approach for biomedical entity and relation extraction from biomedical literature Abstract   Recently joint modeling methods of entity and relation exhibit more promising results than traditional pipelined methods in general domain. However, they are inappropriate for the biomedical domain due to numerous overlapping relations in biomedical text. To alleviate the problem, we propose a neural networkbased joint learning approach for biomedical entity and relation extraction. In this approach, a novel tagging scheme that takes into account overlapping relations is proposed. Then the AttBiLSTMCRF model is built to jointly extract the entities and their relations with our extraction rules. Moreover, the contextualized ELMo representations pretrained on biomedical text are used to further improve the performance. Experimental results on biomedical corpora show that our method can significantly improve the performance of overlapping relation extraction and achieves the stateoftheart performance.