dc.contributor.authorKim, Jin-Dong
dc.contributor.authorKim, Jung-jae
dc.contributor.authorHan, Xu
dc.contributor.authorRebholz-Schuhmann, Dietrich
dc.date.accessioned2016-06-28T09:20:37Z
dc.date.available2016-06-28T09:20:37Z
dc.date.issued2015
dc.identifier.citationKim, J.-D., Kim, J.-J., Han, X., & Rebholz-Schuhmann, D. (2015). Extending the evaluation of Genia Event task toward knowledge base construction and comparison to Gene Regulation Ontology task. BMC Bioinformatics, 16(Suppl 10), S3-.en_US
dc.identifier.issn1471-2105en_US
dc.identifier.urihttp://hdl.handle.net/10220/40825
dc.description.abstractBackground: The 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. Results: We 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.en_US
dc.format.extent13 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesBMC Bioinformaticsen_US
dc.rights© 2015 Kim et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en_US
dc.subjectbionlpen_US
dc.subjectshared tasken_US
dc.subjectevaluationen_US
dc.subjectinformation extractionen_US
dc.subjecttext miningen_US
dc.subjectknowledge baseen_US
dc.subjectsemantic weben_US
dc.subjectresource description frameworken_US
dc.titleExtending the evaluation of Genia Event task toward knowledge base construction and comparison to Gene Regulation Ontology tasken_US
dc.typeJournal Article
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1186/1471-2105-16-S10-S3
dc.description.versionPublished versionen_US


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