Large scale pre-trained knowledge graph model and e-commerce application

Huajun CHEN; Wen ZHANG; Chi-Man WONG; Ganqiang YE; Bo WEN; Wei ZHANG

doi:10.11959/j.issn.2096-0271.2021028

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Large scale pre-trained knowledge graph model and e-commerce application

TOPIC：BIG DATA BASED KNOWLEDGE GRAPH AND ITS APPLICATIONS | 更新时间：2024-06-03

- Large scale pre-trained knowledge graph model and e-commerce application
- Big data research Vol. 7, Issue 3, Pages: 2021028(2021)
- 作者机构：
  
  1. 浙江大学计算机科学与技术学院，浙江杭州 310007
  2. 阿里巴巴-浙江大学前沿技术联合研究中心，浙江杭州 311121
  3. 浙江大学软件学院，浙江杭州 310007
  4. 阿里巴巴集团，浙江杭州 311121
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2021028
  CLC： TP183
- Online First：2021-05，
  
  Published：15 May 2021
- 稿件说明：
移动端阅览
Huajun CHEN, Wen ZHANG, Chi-Man WONG, et al. Large scale pre-trained knowledge graph model and e-commerce application[J]. Big data research, 2021, 7(3): 2021028.
DOI：

Huajun CHEN, Wen ZHANG, Chi-Man WONG, et al. Large scale pre-trained knowledge graph model and e-commerce application[J]. Big data research, 2021, 7(3): 2021028. DOI： 10.11959/j.issn.2096-0271.2021028.

摘要

近年来，知识图谱因具有以统一的方式组织数据等优势，被广泛应用于许多需要知识的任务，并且在电子商务领域大放光彩。然而知识服务通常需要烦琐的数据选择和知识注入模型的设计，这会给业务带来不良影响。为了更好地解决这一问题，提出了“预训练+知识向量服务”的模式，并设计了知识图谱预训练模型（PKGM），在不直接访问商品知识图谱中三元组数据的情况下，以知识向量的方式为下游任务提供知识图谱服务。在商品分类、同款商品识别和商品推荐等知识图谱下游任务中进行测试，实验结果表明，知识图谱预训练模型能够有效地提高每个任务的性能。

Abstract

In recent years

knowledge graph has been widely applied to organize data in a uniform way and enhance many tasks that require knowledge.For example

it has been widely used in the field of e-commerce.However

such knowledge services usually include tedious data selection and model design for knowledge infusion

which might bring inappropriate results.Thus

to solve this problem

the method of first pre-training then providing knowledge vector service was put forward

and a pre-trained knowledge graph model (PKGM) was proposed for our billionscale e-commerce product knowledge graph

providing item knowledge services in a uniform way for embeddingbased models without accessing triple data in the knowledge graph.PKGM was tested in three knowledge-related tasks including item classification

same item identification

and recommendation.Experimental results show PKGM successfully improves the performance of each task.

关键词

Keywords

references

PECHSIRI C , PIRIYAKUL R . Explanation knowledge graph construction through causality extraction from texts [J ] . Journal of Computer Science and Technology , 2010 , 25 ( 5 ): 1055 - 1070 .

RADFORD A , NARASIMHAN K , SALIMANS T , et al . Improving language understanding by generative pre-training [Z ] . 2018 .

DEVLIN J , CHANG M W , LEE K , et al . BERT:pre-training of deep bidirectional transformers for language understanding [J ] . arXiv preprint , 2018 ,arXiv:1810.04805.

YANG Z L , DAI Z H , YANG Y M , et al . XLNet:generalized autoregressive pretraining for language understanding [J ] . arXiv preprint , 2019 ,arXiv:1906.08237.

MIKOLOV T , CHEN K , CORRADO G , et al . Efficient estimation of word representations in vector space [J ] . arXiv preprint , 2013 ,arXiv:1301.3781.

BORDES A , USUNIER N , GARCIADURÁN A , et al . Translating embeddings for modeling multi-relational data [C ] // Proceedings of the 26th International Conference on Neural Information Processing Systems (NIPS) .[S.l. ] : Curran Associates Inc. , 2013 : 2787 - 2795 .

PARK M Y , HASTIE T . L1-regularization path algorithm for generalized linear models [J ] . Journal of the Royal Statistical Society:Series B (Statistical Methodology) , 2007 , 69 ( 4 ): 659 - 677 .

JI S X , PAN S R , CAMBRIA E , et al . A survey on knowledge graphs:Representation,acquisition and applications [J ] . arXiv preprint , 2020 ,arXiv:2002.00388.

MELO A , PAULHEIM H . Detection of relation assertion errors in knowledge graphs [C ] // Proceedings of the 2017 Knowledge Capture Conference . New York:ACM Press , 2017 : 1 - 8 .

VASWANI A , SHAZEER N , PARMAR N , et al . Attention is all you need [J ] . arXiv preprint , 2017 ,arXiv:1706.03762.

ZHU Y K , KIROS R , ZEMEL R , et al . Aligning books and movies:towards story-like visual explanations by watching movies and reading books [C ] // Proceedings of the 2015 IEEE International Conference on Computer Vision . Piscataway:IEEE Press , 2015 : 19 - 27 .

VÖLKEL M , KRÖTZSCH M , VRANDECIC D , et al . Semantic Wikipedia [C ] // Proceedings of the 15th International Conference on World Wide Web . New York:ACM Press , 2006 : 585 - 594 .

SUCHANEK F M , KASNECI G , WEIKUM G . YAGO:a core of semantic knowledge [C ] // Proceedings of the 16th International Conference on World Wide Web . New York:ACM Press , 2007 : 697 - 706 .

BOLLACKER K , EVANS C , PARITOSH P , et al . Freebase:a collaboratively created graph database for structuring human knowledge [C ] // Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data . New York:ACM Press , 2008 : 1247 - 1250 .

MILLER G A , BECKWITH R , FELLBAUM C , et al . Introduction to WordNet:an on-line lexical database [J ] . International Journal of Lexicography , 1990 , 3 ( 4 ): 235 - 244 .

SINOARA R A , CAMACHO-COLLADOS J , ROSSI R G , et al . Knowledgeenhanced document embeddings for text classification [J ] . Knowledge-Based Systems , 2019 , 163 : 955 - 971 .

HE X N , LIAO L Z , ZHANG H W , et al . Neural collaborative filtering [C ] // Proceedings of the 26th International Conference on World Wide Web .[S.l.:s.n. ] , 2017 : 173 - 182 .

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