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）
Yao Zhang, Hongru Liang, Adam Jatowt, Wenqiang Lei, Xin Wei, Ning Jiang, Zhenglu Yang
备注：
Accepted to EMNLP 2021; 10 pages, 5 figures, 4 tables
Knowledge graphs are essential for numerous downstream natural language processing applications, but are typically incomplete with many facts missing. This results in research efforts on multi-hop reasoning task, which can be formulated as a search process and current models typically perform short distance reasoning. However, the long-distance reasoning is also vital with the ability to connect the superficially unrelated entities. To the best of our knowledge, there lacks a general framework that approaches multi-hop reasoning in mixed long-short distance reasoning scenarios. We argue that there are two key issues for a general multi-hop reasoning model: i) where to go, and ii) when to stop. Therefore, we propose a general model which resolves the issues with three modules: 1) the local-global knowledge module to estimate the possible paths, 2) the differentiated action dropout module to explore a diverse set of paths, and 3) the adaptive stopping search module to avoid over searching. The comprehensive results on three datasets demonstrate the superiority of our model with significant improvements against baselines in both short and long distance reasoning scenarios.
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）
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）
Neural-Symbolic Models for Logical Queries on Knowledge Graphs
Mila - Québec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；McGill University（麦吉尔大学）；HEC Montréal（蒙特利尔高等商学院）；CIFAR AI Chair（加拿大高等研究院人工智能讲席）
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）
SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphs
Institute of Big Data and Artificial Intelligence, China Telecom Research Institute（大数据与人工智能研究院，中国电信研究院）；Wangxuan Institute of Computer Technology, Peking University（王宣计算机技术研究所，北京大学）；School of Artificial Intelligence, China University of Geosciences (Beijing)（人工智能学院，中国地质大学（北京））；Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University（认知与神经工效学中心，认知神经科学与学习国家重点实验室，北京师范大学）；Institute of Artificial Intelligence and Future Networks, Beijing Normal University（人工智能与未来网络研究院，北京师范大学）
