Extending the evaluation of Genia Event task toward knowledge base construction and comparison to Gene Regulation Ontology task

BMC Bioinformatics

Research

Jin-Dong Kim¹  Xu Han²  Jung-jae Kim²  Dietrich Rebholz-Schuhmann³ 

[1] Database Center for Life Science, Research Organization of Information and Systems, 178-4-4 Wakashiba, Kashiwa, Japan;Nanyang Technological University, School of Computer Engineering, Block N4 #02a-32, Nanyang Avenue, 639798, Singapore;University of Zurich, Institute of Computational Linguistics, Binzmühlestrasse 14, 8050, Zurich, Switzerland;

关键词: bionlp;    shared task;    evaluation;    information extraction;    text mining;    knowledge base;    semantic web;    resource description framework;   

DOI  :  10.1186/1471-2105-16-S10-S3

来源: Springer

【 摘 要 】

BackgroundThe third edition of the BioNLP Shared Task was held with the grand theme "knowledge base construction (KB)". The Genia Event (GE) task was re-designed and implemented in light of this theme. For its final report, the participating systems were evaluated from a perspective of annotation. To further explore the grand theme, we extended the evaluation from a perspective of KB construction. Also, the Gene Regulation Ontology (GRO) task was newly introduced in the third edition. The final evaluation of the participating systems resulted in relatively low performance. The reason was attributed to the large size and complex semantic representation of the ontology. To investigate potential benefits of resource exchange between the presumably similar tasks, we measured the overlap between the datasets of the two tasks, and tested whether the dataset for one task can be used to enhance performance on the other.ResultsWe report an extended evaluation on all the participating systems in the GE task, incoporating a KB perspective. For the evaluation, the final submission of each participant was converted to RDF statements, and evaluated using 8 queries that were formulated in SPARQL. The results suggest that the evaluation may be concluded differently between the two different perspectives, annotation vs. KB. We also provide a comparison of the GE and GRO tasks by converting their datasets into each other's format. More than 90% of the GE data could be converted into the GRO task format, while only half of the GRO data could be mapped to the GE task format. The imbalance in conversion indicates that the GRO is a comprehensive extension of the GE task ontology. We further used the converted GRO data as additional training data for the GE task, which helped improve GE task participant system performance. However, the converted GE data did not help GRO task participants, due to overfitting and the ontology gap.

【 授权许可】

CC BY   
© Kim et al.; licensee BioMed Central Ltd. 2015