Constructing marine expert management knowledge graph based on Trellisnet-CRF

Jiajing Wu; Zhiqiang Wei; Dongning Jia; Xin Dou; Huo Tang; Nannan Li

doi:10.7717/peerj-cs.1083

Constructing marine expert management knowledge graph based on Trellisnet-CRF

PeerJ Comput Sci. 2022 Sep 5:8:e1083. doi: 10.7717/peerj-cs.1083. eCollection 2022.

Authors

Jiajing Wu^{1

2}, Zhiqiang Wei¹, Dongning Jia¹, Xin Dou¹, Huo Tang³, Nannan Li³

Affiliations

¹ School of Information Science and Engineering, Ocean University of China, Qingdao, China.
² China Research Institute of Radiowave Propagation, Qingdao, China.
³ Chifeng University, Chifeng, China.

Abstract

Creating and maintaining a domain-specific database of research institutions, academic experts and scholarly literature is essential to expanding national marine science and technology. Knowledge graphs (KGs) have now been widely used in both industry and academia to address real-world problems. Despite the abundance of generic KGs, there is a vital need to build domain-specific knowledge graphs in the marine sciences domain. In addition, there is still not an effective method for named entity recognition when constructing a knowledge graph, especially when including data from both scientific and social media sources. This article presents a novel marine science domain-based knowledge graph framework. This framework involves capturing marine domain data into KG representations. The proposed approach utilizes various entity information based on marine domain experts to enrich the semantic content of the knowledge graph. To enhance named entity recognition accuracy, we propose a novel TrellisNet-CRF model. Our experiment results demonstrate that the TrellisNet-CRF model reached a 96.99% accuracy rate for marine domain named entity recognition, which outperforms the current state-of-the-art baseline. The effectiveness of the TrellisNet-CRF module was then further demonstrated and confirmed on entity recognition and visualization tasks.

Keywords: Entity recognition; Knowledge graph; Marine expert; Trellisnet-CRF.

Grants and funding

This work was financially supported by the Key R&D Projects of Shandong Province under Grant 2019JMRH0109; the Key R&D Projects of Shandong Province under Grant 2020JMRH0201; and the Research on Mining Method of Cube Association Rules Based on Cloud Computing Inner Mongolia Department of Education under Grant NJZY16256. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.