Journal of Data and Information Science ›› 2016, Vol. 1 ›› Issue (1): 69-85.doi: 10.20309/jdis.201606

• Research Paper • Previous Articles     Next Articles

A Bootstrapping-based Method to Automatically Identify Data-usage Statements in Publications

Qiuzi Zhang, Qikai Cheng, Yong Huang & Wei Lu   

  1. School of Information Management, Wuhan University, Wuhan 430072, China
  • Received:2016-01-21 Revised:2016-02-19 Online:2016-03-15 Published:2016-03-15
  • Contact: Wei Lu
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (Grant No.: 71473183).

Abstract: Purpose: Our study proposes a bootstrapping-based method to automatically extract datausage statements from academic texts.

Design/methodology/approach: The method for data-usage statements extraction starts with seed entities and iteratively learns patterns and data-usage statements from unlabeled text. In each iteration, new patterns are constructed and added to the pattern list based on their calculated score. Three seed-selection strategies are also proposed in this paper.

Findings: The performance of the method is verified by means of experiments on real data collected from computer science journals. The results show that the method can achieve satisfactory performance regarding precision of extraction and extensibility of obtained patterns.

Research limitations: While the triple representation of sentences is effective and efficient for extracting data-usage statements, it is unable to handle complex sentences. Additional features that can address complex sentences should thus be explored in the future.

Practical implications: Data-usage statements extraction is beneficial for data-repository construction and facilitates research on data-usage tracking, dataset-based scholar search, and dataset evaluation.

Originality/value: To the best of our knowledge, this paper is among the first to address the important task of automatically extracting data-usage statements from real data.

Key words: Data-usage statements extraction, Information extraction, Bootstrapping, Unsupervised learning, Academic text-mining