Building a large-scale testing dataset for conceptual semantic annotation of text. (2018)

• Record Type: Journal Article
• Authors: Wei, Xiao; Zeng, Daniel Dajun; Luo, Xiangfeng; Wu, Wei
• Is Part Of: International journal of computational science and engineering. Volume 16:Number 1(2018)
• Page Start: 63
• Page End: 72
• Publication Date: 2018
• Subjects: semantic annotation -- ontology concept learning -- testing dataset -- evaluation baseline -- ontology auto-construction -- priori knowledge -- evaluation parameters -- guided annotation method -- MeSH -- PubMed; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

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 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.