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Towards Accurate Search for E-Commerce in Steel Industry: A Knowledge-Graph-Based Approach

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2020)

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.

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Acknowledgment

This work was supported in part by the National Key R&D Program of China under Grant 2016YFC0600510, in part by the Beijing Natural Science Foundation under Grant 19L2029, in part by the Beijing Intelligent Logistics System Collaborative Innovation Center under Grant BILSCIC-2019KF-08, in part by the Scientific and Technological Innovation Foundation of Shunde Graduate School, USTB, under Grant BK19BF006, and in part by the Fundamental Research Funds for the University of Science and Technology Beijing under Grant FRF-BD-19-012A.

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Authors and Affiliations

  1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Maojian Chen, Xiong Luo & Junluo Yin

  2. Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, 100083, China

    Maojian Chen, Xiong Luo & Junluo Yin

  3. Shunde Graduate School, University of Science and Technology Beijing, Foshan, Guangdong, 528399, China

    Maojian Chen, Xiong Luo & Junluo Yin

  4. Ouyeel Co., Ltd., Shanghai, 201999, China

    Hailun Shen & Ziyang Huang

Authors
  1. Maojian Chen
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  2. Hailun Shen
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  3. Ziyang Huang
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  4. Xiong Luo
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  5. Junluo Yin
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Corresponding author

Correspondence to Xiong Luo .

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Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Xinheng Wang

  3. London South Bank University, London, UK

    Muddesar Iqbal

  4. Hangzhou Dianzi University, Hangzhou, China

    Yuyu Yin

  5. Zhejiang University, Hangzhou, China

    Jianwei Yin

  6. Fudan University, Shanghai, China

    Ning Gu

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Chen, M., Shen, H., Huang, Z., Luo, X., Yin, J. (2021). Towards Accurate Search for E-Commerce in Steel Industry: A Knowledge-Graph-Based Approach. In: Gao, H., Wang, X., Iqbal, M., Yin, Y., Yin, J., Gu, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 349. Springer, Cham. https://doi.org/10.1007/978-3-030-67537-0_1

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  • DOI: https://doi.org/10.1007/978-3-030-67537-0_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67536-3

  • Online ISBN: 978-3-030-67537-0

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