research-article

Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules

Authors:

Huajun ChenAuthors Info & Claims

IJCKG '21: Proceedings of the 10th International Joint Conference on Knowledge Graphs

Pages 71 - 79

https://doi.org/10.1145/3502223.3502232

Published: 24 January 2022 Publication History

Abstract

Knowledge Graphs (KGs), representing facts as triples, have been widely adopted in many applications. Reasoning tasks such as link prediction and rule induction are important for the development of KGs. Knowledge Graph Embeddings (KGEs) embedding entities and relations of a KG into continuous vector spaces, have been proposed for these reasoning tasks and proven to be efficient and robust. But the plausibility and feasibility of applying and deploying KGEs in real-work applications has not been well-explored. In this paper, we discuss and report our experiences of deploying KGEs in a real domain application: e-commerce. We first identity three important desiderata for e-commerce KG systems: 1) attentive reasoning, reasoning over a few target relations of more concerns instead of all; 2) explanation, providing explanations for a prediction to help both users and business operators understand why the prediction is made; 3) transferable rules, generating reusable rules to accelerate the deployment of a KG to new systems. While non existing KGE could meet all these desiderata, we propose a novel one, an explainable knowledge graph attention network that make prediction through modeling correlations between triples rather than purely relying on its head entity, relation and tail entity embeddings. It could automatically selects attentive triples for prediction and records the contribution of them at the same time, from which explanations could be easily provided and transferable rules could be efficiently produced. We empirically show that our method is capable of meeting all three desiderata in our e-commerce application and outperform typical baselines on datasets from real domain applications.

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Aldughayfiq BAshfaq FJhanjhi NHumayun M(2023)Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDBHealthcare10.3390/healthcare1112176211:12(1762)Online publication date: 15-Jun-2023
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Chen ZZhang WHuang YChen MGeng YYu HBi ZZhang YYao ZSong WWu XYang YChen MLian ZLi YCheng LChen H(2023)Tele-Knowledge Pre-training for Fault Analysis2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00265(3453-3466)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00265
Deng SWang CLi ZZhang NDai ZChen HXiong FYan MChen QChen MChen JPan JHooi BChen H(2023)Construction and Applications of Billion-Scale Pre-Trained Multimodal Business Knowledge Graph2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00229(2988-3002)Online publication date: Apr-2023
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Index Terms

Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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IJCKG '21: Proceedings of the 10th International Joint Conference on Knowledge Graphs

December 2021

204 pages

ISBN:9781450395656

DOI:10.1145/3502223

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 24 January 2022

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IJCKG'21: The 10th International Joint Conference on Knowledge Graphs

December 6 - 8, 2021

Virtual Event, Thailand

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Cited By

Aldughayfiq BAshfaq FJhanjhi NHumayun M(2023)Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDBHealthcare10.3390/healthcare1112176211:12(1762)Online publication date: 15-Jun-2023
https://doi.org/10.3390/healthcare11121762
Chen ZZhang WHuang YChen MGeng YYu HBi ZZhang YYao ZSong WWu XYang YChen MLian ZLi YCheng LChen H(2023)Tele-Knowledge Pre-training for Fault Analysis2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00265(3453-3466)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00265
Deng SWang CLi ZZhang NDai ZChen HXiong FYan MChen QChen MChen JPan JHooi BChen H(2023)Construction and Applications of Billion-Scale Pre-Trained Multimodal Business Knowledge Graph2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00229(2988-3002)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00229
Choi J(2022)Graph Embedding-Based Domain-Specific Knowledge Graph Expansion Using Research Literature SummarySustainability10.3390/su14191229914:19(12299)Online publication date: 27-Sep-2022
https://doi.org/10.3390/su141912299
Sun LHu WXu KChen YSun QWang J(2022)ASRC:A Knowledge Graph Relation Construction Model based on Active Learning and Semantic Recognition2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020502(6025-6029)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020502

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