Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph
Department of Computer Science and Technology, BNRist（计算机科学与技术系，BNRist）；KIRC, Institute for Artificial Intelligence, Tsinghua University（清华大学人工智能研究院，知识图谱与智能推理中心（KIRC））；Alibaba Group（阿里巴巴集团）
Multi-hop reasoning has been widely studied in recent years to seek an effective and interpretable method for knowledge graph (KG) completion. Most previous reasoning methods are designed for dense KGs with enough paths between entities, but cannot work well on those sparse KGs that only contain sparse paths for reasoning. On the one hand, sparse KGs contain less information, which makes it difficult for the model to choose correct paths. On the other hand, the lack of evidential paths to target entities also makes the reasoning process difficult. To solve these problems, we propose a multi-hop reasoning model named DacKGR over sparse KGs, by applying novel dynamic anticipation and completion strategies: (1) The anticipation strategy utilizes the latent prediction of embedding-based models to make our model perform more potential path search over sparse KGs. (2) Based on the anticipation information, the completion strategy dynamically adds edges as additional actions during the path search, which further alleviates the sparseness problem of KGs. The experimental results on five datasets sampled from Freebase, NELL and Wikidata show that our method outperforms state-of-the-art baselines. Our codes and datasets can be obtained from https://github.com/THU-KEG/DacKGR
Modeling relation paths for knowledge base completion via joint adversarial training
Beihang University（北京航空航天大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；National Computer Network Emergency Response Technical Team/Coordination Center of China（国家计算机网络应急技术处理协调中心）
GMH: A General Multi-hop Reasoning Model for KG Completion
Nankai University（南开大学）；Sichuan University（四川大学）；University of Innsbruck（因斯布鲁克大学）；National University of Singapore（新加坡国立大学）；Mashang Consumer Finance Co., Ltd.（马上消费金融股份有限公司）
Coarse-grained decomposition and fine-grained interaction for multi-hop question answering
School of Electronic and Information, Beijing Jiaotong University（北京交通大学电子信息学院）；Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education（北京市教育委员会通信与信息系统重点实验室）
To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion
Dalian University of Technology（大连理工大学）；Renmin University of China（中国人民大学）；Microsoft Research Asia（微软亚洲研究院）；Université de Montréal（蒙特利尔大学）；Microsoft（微软）
