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 data-base, 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.
Authors: Maojian Chen (University of Science and Technology Beijing), Hailun Shen (Ouyeel Co., Ltd), Ziyang Huang (Ouyeel Co., Ltd), Xiong Luo (University of Science and Technology Beijing), Junluo Yin (University of Science and Technology Beijing),
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