中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
Link Prediction on N-ary Relational Data Based on Relatedness Evaluation

文献类型：期刊论文

作者Guan, Saiping1,2; Jin, Xiaolong1,2; Guo, Jiafeng1,2; Wang, Yuanzhuo None1,2; Cheng, Xueqi1,2
刊名IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
出版日期2023
卷号35期号:1页码:672-685
关键词Link prediction n-ary relational facts knowledge graph relatedness
ISSN号1041-4347
DOI10.1109/TKDE.2021.3073483
英文摘要With the overwhelming popularity of Knowledge Graphs (KGs), researchers have poured attention to link prediction to fill in missing facts for a long time. However, they mainly focus on link prediction on binary relational data, where facts are usually represented as triples in the form of (head entity, relation, tail entity). In practice, n-ary relational facts are also ubiquitous. When encountering such facts, existing studies usually decompose them into triples by introducing a multitude of auxiliary virtual entities and additional triples. These conversions result in the complexity of carrying out link prediction on n-ary relational data. It has even proven that they may cause loss of structure information. To overcome these problems, in this paper, we represent each n-ary relational fact as a set of its role and role-value pairs. We then propose a method called NaLP to conduct link prediction on n-ary relational data, which explicitly models the relatedness of all the role and role-value pairs in an n-ary relational fact. We further extend NaLP by introducing type constraints of roles and role-values without any external type-specific supervision, and proposing a more reasonable negative sampling mechanism. Experimental results validate the effectiveness and merits of the proposed methods.
资助项目National KeyResearch and Development Program of China[2016YFB1000902] ; Beijing Academy of ArtificialIntelligence (BAAI)[BAAI2019ZD0306] ; Lenovo-CAS Joint Lab Youth Scientist Project ; National Natural Science Foundation of China[62002341] ; National Natural Science Foundation of China[U1911401] ; National Natural Science Foundation of China[61772501] ; National Natural Science Foundation of China[U1836206] ; National Natural Science Foundation of China[91646120] ; GFKJ Innovation Program
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000895445500049
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/20187]  
专题中国科学院计算技术研究所期刊论文
通讯作者Guan, Saiping
作者单位1.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing 100864, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Guan, Saiping,Jin, Xiaolong,Guo, Jiafeng,et al. Link Prediction on N-ary Relational Data Based on Relatedness Evaluation[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2023,35(1):672-685.
APA Guan, Saiping,Jin, Xiaolong,Guo, Jiafeng,Wang, Yuanzhuo None,&Cheng, Xueqi.(2023).Link Prediction on N-ary Relational Data Based on Relatedness Evaluation.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,35(1),672-685.
MLA Guan, Saiping,et al."Link Prediction on N-ary Relational Data Based on Relatedness Evaluation".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 35.1(2023):672-685.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。