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

• DOI: 10.1504/ijcse.2018.089582
• Publication Date: 2018-02-01T12:30:09Z
• Authors: Xiao Wei; Daniel Dajun Zeng; Xiangfeng Luo; Wei Wu
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

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.