Building a large-scale testing dataset for conceptual semantic annotation of text

Xiao Wei, Daniel Dajun Zeng, Xiangfeng Luo, Wei Wu
2018 International Journal of Computational Science and Engineering (IJCSE)  
One major obstacle facing the research on semantic annotation is lack of large-scale testing datasets. In this paper, we develop a systematic approach to constructing such datasets. This approach is based on guided ontology auto-construction and annotation methods which use little priori domain knowledge and little user knowledge in documents. We demonstrate the efficacy of the proposed approach by developing a large-scale testing dataset using information available from MeSH and PubMed. The
more » ... and PubMed. The developed testing dataset consists of a large-scale ontology, a large-scale set of annotated documents, and the baselines to evaluate the target algorithm, which can be employed to evaluate both the ontology construction algorithms and semantic annotation algorithms. (2018) 'Building a large-scale testing dataset for conceptual semantic annotation of text', Int.
doi:10.1504/ijcse.2018.089582 fatcat:zh2cegjlirbn5dzbcmldujbbte