[1]李军怀,武允文,王怀军,等.融合实体描述与路径信息的知识图谱表示学习模型[J].智能系统学报,2023,18(1):153-161.[doi:10.11992/tis.202112010]
LI Junhuai,WU Yunwen,WANG Huaijun,et al.Knowledge graph representation learning model combining entity description and path information[J].CAAI Transactions on Intelligent Systems,2023,18(1):153-161.[doi:10.11992/tis.202112010]
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LI Junhuai,WU Yunwen,WANG Huaijun,et al.Knowledge graph representation learning model combining entity description and path information[J].CAAI Transactions on Intelligent Systems,2023,18(1):153-161.[doi:10.11992/tis.202112010]
融合实体描述与路径信息的知识图谱表示学习模型
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
18
期数:
2023年第1期
页码:
153-161
栏目:
人工智能院长论坛
出版日期:
2023-01-05
- Title:
- Knowledge graph representation learning model combining entity description and path information
- Keywords:
- knowledge graph; expression learning; multidimensional vector; multi-hop reasoning ability; entity description; path information; energy function; vector fusion
- 分类号:
- TP301.6
- 摘要:
- 知识图谱表示学习方法是将知识图谱中的实体和关系通过特定规则表示成一个多维向量的过程。现有表示学习方法多用于解决单跳知识图谱问答任务，其多跳推理能力无法满足实际需求，为提升多跳推理能力，提出一种融合实体描述与路径信息的知识图谱表示学习模型。首先通过预训练语言模型RoBERTa得到融合实体描述的实体、关系表示学习向量；其次利用OPTransE将知识图谱转化成融入有序关系路径信息的向量。最后构建总能量函数，将针对实体描述和路径信息的向量进行融合。通过实验分析与对比该模型在链路预测任务上与主流知识图谱表示学习模型的性能，验证了该模型的可行性与有效性。
- Abstract:
- Knowledge graph representation learning is a process of representing knowledge graph entities and relations in a multidimensional vector through specific rules. Existing representation learning methods are mostly used to solve the single-hop knowledge graph question-and-answer task, but their multi-hop reasoning ability cannot meet the actual demand. To improve the multi-hop reasoning ability, a knowledge graph representation learning model combining entity description and path information is proposed. First, the learning vector of entity and relation representation is obtained using the pre-training language model RoBERTa. Second, OPTransE is used to transform the knowledge graph into a vector integrating the path information of an ordered relation. Finally, the total energy function is constructed to fuse the vectors of entity description and path information. The feasibility and validity of the model are verified by comparing its performance in a link prediction task with that of the mainstream knowledge graph representation learning model.
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备注/Memo
收稿日期:2021-12-03。
基金项目:国家重点研发计划项目(2018YFB1703000);陕西省现代装备绿色制造协同中心自主研发或开放基金项目(112-256092104).
作者简介:李军怀,教授,博士生导师,西安理工大学计算机科学与工程学院院长,CCF会员,主要研究方向为网络通信、物联网和云计算。以课题组组长、副组长负责开展国家863项目4项、省(部)市项目10余项。发表学术论文40余篇;武允文,硕士研究生,主要研究方向为物联网技术;王怀军,副教授,博士,CCF会员,主要研究方向为工业互联网和智能感知
通讯作者:王怀军.E-mail:wanghuaijun@xaut.edu.cn
更新日期/Last Update:
1900-01-01