Research Article
Towards Accurate Search for E-Commerce in Steel Industry: A Knowledge-Graph-Based Approach
@INPROCEEDINGS{10.1007/978-3-030-67537-0_1, author={Maojian Chen and Hailun Shen and Ziyang Huang and Xiong Luo and Junluo Yin}, title={Towards Accurate Search for E-Commerce in Steel Industry: A Knowledge-Graph-Based Approach}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part I}, proceedings_a={COLLABORATECOM}, year={2021}, month={1}, keywords={Steel E-commerce Knowledge graph (KG) Entity extraction Bidirectional encoder representation from transformers (BERT)}, doi={10.1007/978-3-030-67537-0_1} }- Maojian Chen
Hailun Shen
Ziyang Huang
Xiong Luo
Junluo Yin
Year: 2021
Towards Accurate Search for E-Commerce in Steel Industry: A Knowledge-Graph-Based Approach
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-67537-0_1
Abstract
Mature artificial intelligence (AI) makes human life more and more convenient. However, in some application fields, it is impossible to achieve the satisfactory results only depending on the traditional AI algorithm. Specifically, in order to avoid the limitations of traditional searching strategies in e-commerce field related to steel, such as the inability to analyzing long technical sentences, we propose a collaborative decision making method in this field, through the combination of deep learning algorithms and expert systems. Firstly, we construct a knowledge graph (KG) on the basis of steel commodity data and expert database, and then train a model to accurately extract steel entities from long technical sentences, while using an advanced bidirectional encoder representation from transformers (BERT), a bidirectional long short-term memory (Bi-LSTM), and a conditional random field (CRF) approach. Finally, we develop an intelligent searching system for e-commence in steel industry, with the help of the designed KG and entity extraction model, while improving the searching performance and user experience in such system.
