[1]高文馨,李贯峰*,王云丽,等.融入逻辑规则的知识图谱推荐模型研究[J].计算机技术与发展,2024,34(09):109-115.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0180]
　GAO Wen-xin,LI Guan-feng*,WANG Yun-li,et al.Research on Knowledge Graph Recommendation Models Integrated with Logical Rules[J].,2024,34(09):109-115.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0180]
点击复制

融入逻辑规则的知识图谱推荐模型研究()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年09期
页码:
109-115
栏目:
人工智能
出版日期:
2024-09-10

文章信息/Info

Title:
Research on Knowledge Graph Recommendation Models Integrated with Logical Rules
文章编号:
1673-629X(2024)09-0109-07
作者:
高文馨1; 2; 李贯峰1; 2*; 王云丽1; 胡德洲1; 李瑞1
1. 宁夏大学 信息工程学院,宁夏 银川 750021;2. 宁夏“东数西算”人工智能与信息安全重点实验室,宁夏 银川 750021
Author(s):
GAO Wen-xin1; 2; LI Guan-feng1; 2*; WANG Yun-li1; HU De-zhou1; LI Rui1
1. School of Information Engineering,Ningxia University,Yinchuan 750021,China;2. Ningxia “East Calculation West Algorithm” Artificial Intelligence and Information Security Key Laboratory,Yinchuan 750021,China
关键词:
知识图谱; 知识图谱嵌入; 逻辑规则; 推荐算法; 联合学习
Keywords:
knowledge graph; knowledge graph embedding; logical rules; recommendation algorithm; joint learning
分类号:
TP391
DOI:
10.20165/j.cnki.ISSN1673-629X.2024.0180
摘要:
知识图谱嵌入技术已在推荐系统领域引起广泛关注,将结构化知识图谱中的信息融入到推荐模型中,可以提高推荐的个性化程度。 然而,因为初始数据的不准确性会导致推荐结果不正确,现存的知识图谱推荐模型中仍存在误差传播问题。 针对这个问题,该文提出了 RR-KGE 模型,由知识图谱嵌入模块和推荐算法模块组成;其中聚焦于知识图谱嵌入框架,将规则嵌入和知识图谱嵌入进行联合学习,通过规则给予模型更多的约束条件,以减少误差传播;并结合此框架将推荐算法 ALS(Alternating Least Squares)和 RNN(Recurrent Neural Network)相融合来获得更加精确的推荐结果;最后将 RR-KGE 与不同基准模型进行比较,在两个数据集上多项指标均优于对比模型,证明了推荐方法的有效性。
Abstract:
Knowledge graph embedding technology has attracted widespread attention in the field of recommendation systems. Integrating information from structured knowledge graphs into recommendation models can enhance the personalization of recommendations. However,existing knowledge graph recommendation models still face the issue of error propagation due to the inaccuracy of initial data,which leads to incorrect recommendation results. To address this problem,we propose the RR-KGE model,consisting of a knowledge graph embedding module and a recommendation algorithm module. The focus is on the knowledge graph embedding framework,where rule embedding and knowledge graph embedding are jointly learned. Rules provide the model with additional constraints to reduce error propagation. This framework is combined with the recommendation algorithms ALS (Alternating Least Squares) and RNN (Recurrent Neural Network) to obtain more accurate recommendation results. Finally,RR-KGE is compared with different baseline models,and multiple metrics on two datasets demonstrate its superiority over the comparison models, confirming the effectiveness of the recommendation approach.

相似文献/References:

[1]孙艳,田丽梅. 基于多维尺度分析的舆情研究主题词知识图谱[J].计算机技术与发展,2016,26(04):187.
　SUN Yan,TIAN Li-mei. Mapping Knowledge Domain on Subject Headings of Public Sentiment Research Based on Multi-dimensional Scaling[J].,2016,26(09):187.
[2]刘申凯,周霁婷,朱永华,等.融合知识图谱和 ESA 方法的网络新词识别[J].计算机技术与发展,2019,29(03):12.[doi:10.3969/ j. issn.1673-629X.2019.03.003]
　LIU Shen-kai,ZHOU Ji-ting,ZHU Yong-hua,et al.Network New Word Recognition Based on Fusion of Knowledge Graph and ESA[J].,2019,29(09):12.[doi:10.3969/ j. issn.1673-629X.2019.03.003]
[3]戈其平,钟艳如.基于数学教学的知识图谱构建[J].计算机技术与发展,2019,29(03):187.[doi:10.3969/ j. issn.1673-629X.2019.03.039]
　GE Qi-ping,ZHONG Yan-ru.Construction of Knowledge Atlas Based on Mathematics Teaching[J].,2019,29(09):187.[doi:10.3969/ j. issn.1673-629X.2019.03.039]
[4]魏 瑾,李伟华,潘 炜.基于知识图谱的智能决策支持技术及应用研究[J].计算机技术与发展,2020,30(01):1.[doi:10. 3969 / j. issn. 1673-629X. 2020. 01. 001]
　WEI Jin,LI Wei-hua,PAN Wei.Research on Intelligent Decision Support Technology and Application Based on Knowledge Graph[J].,2020,30(09):1.[doi:10. 3969 / j. issn. 1673-629X. 2020. 01. 001]
[5]项 威,王 邦.中文事件抽取研究综述[J].计算机技术与发展,2020,30(02):1.[doi:10. 3969 / j. issn. 1673-629X. 2020. 02. 001]
　XIANG Wei,WANG Bang.Survey of Chinese Event Extraction Research[J].,2020,30(09):1.[doi:10. 3969 / j. issn. 1673-629X. 2020. 02. 001]
[6]刘家祝,郭 强,吴碧伟,等.基于子图相交的社交账号与知识图谱实体对齐[J].计算机技术与发展,2020,30(05):10.[doi:10. 3969 / j. issn. 1673-629X. 2020. 05. 003]
　LIU Jia-zhu,GUO Qiang,WU Bi-wei,et al.Subgraph Intersection Based Alignment between Social Media Account and Knowledge Graph Entity[J].,2020,30(09):10.[doi:10. 3969 / j. issn. 1673-629X. 2020. 05. 003]
[7]陆菁宇,张绍阳,黄文旎.学科发展状态的知识图谱构建[J].计算机技术与发展,2020,30(06):145.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 028]
　LU Jing-yu,ZHANG Shao-yang,HUANG Wen-ni.Analysis of Development Status of Discipline Based on Knowledge Graph[J].,2020,30(09):145.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 028]
[8]黄东晋,秦 汉,郭 昊.基于 BERT-CNN 的电影原声智能问答系统[J].计算机技术与发展,2020,30(11):158.[doi:10. 3969 / j. issn. 1673-629X. 2020. 11. 029]
　HUANG Dong-jin,QIN Han,GUO Hao.Movie Soundtrack Intelligent Question and Answer System Based on BERT-CNN[J].,2020,30(09):158.[doi:10. 3969 / j. issn. 1673-629X. 2020. 11. 029]
[9]任佳妮,杨 阳.全球医疗机器人技术领域创新态势分析[J].计算机技术与发展,2021,31(04):158.[doi:10. 3969 / j. issn. 1673-629X. 2021. 04. 027]
　REN Jia-ni,YANG Yang.Analysis of Innovation Situation in Field of Global MedicalRobot Technology[J].,2021,31(09):158.[doi:10. 3969 / j. issn. 1673-629X. 2021. 04. 027]
[10]卢 琪,谢艺菲,谢 钧,等.知识图谱在智能问答中的应用研究[J].计算机技术与发展,2021,31(07):13.[doi:10. 3969 / j. issn. 1673-629X. 2021. 07. 003]
　LU Qi,XIE Yi-fei,XIE Jun,et al.Research on Application of Knowledge Graphs in Intelligent Question Answering[J].,2021,31(09):13.[doi:10. 3969 / j. issn. 1673-629X. 2021. 07. 003]

更新日期/Last Update: 2024-09-10