Knowledge Extraction From National Standards for Natural Resources

Abstract

National standards for natural resources (NSNR) plays an important role in promoting efficient use of China's natural resources, which sets standards for many domains such as marine and land resources. Its revision is difficult since standards in different domains may overlap or conflict. To facilitate the revision of NSNR, this paper extracts structural knowledge from the NSNR files to assist its revision. NSNR files are in multi-domain texts, where the traditional knowledge extraction methods could fall short in recalling multi-domain entities. To address this issue, this paper proposes a knowledge extraction method for multi-domain texts, including sub-domain relation discovery (SRD) and domain semantic features fusion (DSFF) module. SRD splits NSNR into sub-domains to facilitate the relation discovery. DSFF integrates relation features in the conditional random field (CRF) model to improve the capability of multi-domain entity recognition. Experimental results demonstrate that the proposed method could effectively extract structural knowledge from NSNR.