VN Network: Embedding Newly Emerging Entities with Virtual Neighbors
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Shandong University（山东大学）；Tencent AI Lab（腾讯AI实验室）
Embedding entities and relations into continuous vector spaces has attracted a surge of interest in recent years. Most embedding methods assume that all test entities are available during training, which makes it time-consuming to retrain embeddings for newly emerging entities. To address this issue, recent works apply the graph neural network on the existing neighbors of the unseen entities. In this paper, we propose a novel framework, namely Virtual Neighbor (VN) network, to address three key challenges. Firstly, to reduce the neighbor sparsity problem, we introduce the concept of the virtual neighbors inferred by rules. And we assign soft labels to these neighbors by solving a rule-constrained problem, rather than simply regarding them as unquestionably true. Secondly, many existing methods only use one-hop or two-hop neighbors for aggregation and ignore the distant information that may be helpful. Instead, we identify both logic and symmetric path rules to capture complex patterns. Finally, instead of one-time injection of rules, we employ an iterative learning scheme between the embedding method and virtual neighbor prediction to capture the interactions within. Experimental results on two knowledge graph completion tasks demonstrate that our VN network significantly outperforms state-of-the-art baselines. Furthermore, results on Subject/Object-R show that our proposed VN network is highly robust to the neighbor sparsity problem.
RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule Mining
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感信息工程学院）
Distributed Representations of Entities in Open-World Knowledge Graphs
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Zhejiang University - Ant Group Joint Laboratory of Knowledge Graph（浙江大学-蚂蚁集团知识图谱联合实验室）；Donghai Laboratory（东海实验室）；Department of Computer Science, The University of Manchester（曼彻斯特大学计算机科学系）；Department of Computer Science and Technology, Nanjing University（南京大学计算机科学与技术系）；Ant Group（蚂蚁集团）；Elmore Family School of Electrical and Computer Engineering, Purdue University（普渡大学埃尔莫尔家族电气与计算机工程学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）
SEG:Seeds-Enhanced Iterative Refinement Graph Neural Network for Entity Alignment
College of Computer；Mathematics Central South University of Forestry；4em College of Computer；Mathematics 4em Central South University of Forestry；3em Department of Computer Science 3em State University of New York 3em New Paltz, New York 12561, USA 4em
Interactive Contrastive Learning for Self-supervised Entity Alignment
Tsinghua University（清华大学）；Beijing University of Chemical Technology（北京化工大学）；Singapore Management University（新加坡管理大学）；Information Research Center of Military Science（军事科学院信息研究中心）；China Mobile Research Institute（中国移动研究院）
Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoning
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of Chinese Academy of Sciences（中国科学院大学）；University of Macau（澳门大学）
Department of Chemical Engineering, University of Michigan（美国密歇根大学化学工程系）；Michigan Institute for Data & AI in Society, University of Michigan（美国密歇根大学数据与社会人工智能研究所）；Department of Statistics, University of Michigan（美国密歇根大学统计系）
