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导读 ：备受关注的人工智能顶级会议AAAI 2020持续火爆，在此期间共接收了8843篇有效论文投稿。经严格评审后共有7737篇论文顺利进入了评审环节，在此期间最终成功录用了1591篇高质量的学术论文。相较于去年显著提升了录用比例的同时投稿数量相比去年增长了约一千一百百篇。本系列文章主要会对今年被录用的优秀论文按照研究主题进行划分整理以便于相关领域的爱好者们查阅方便省去自行查找大量文献的时间之苦。本届会议中被录用的优秀论文将在<一起读论文>栏目中进行深入解析请各位读者继续关注！

文章目录

• 知识图谱篇章（Knowledge Graph）
• 图补全（Graph Completion）
• 实体与关系的学习与提取（Entity and Relation Learning and Extraction）
• 图对齐（Graph Alignment）
• 图嵌入（Graph Embedding）
• KBQA
• 通用/其他类别（General/Other）

知识图谱篇（Knowledge Graph）

Graph Completion

This paper proposes an enhanced approach to improve entity linking by effectively modeling latent entity type information. The methodology leverages advanced techniques to capture semantic dependencies, ensuring more accurate linking mechanisms. Through extensive experiments, we demonstrate the superiority of our method compared to existing approaches. Contributions include the development of a novel framework that integrates semantic analysis with latent feature extraction. Future work will explore applications in real-time systems and large-scale datasets.

3019: Constructing Hierarchical-Aware Knowledge Graph Embeddings for Effective Link Prediction
Zhanqiu Zhang, Jianyu Cai, Yongdong Zhang, and Jie Wang.
University of Science and Technology of China;

8971: Contextual Variable Creation for Knowledge Graph Link Prediction
George Stoica, Otilia Stretcu, Anthony Platanios, Tom Mitchell, and Barnabas Poczos are affiliated with Carnegie Mellon University.

9004: Breaking the Filter Bubble: Exploring Fairness in Link Prediction
Farzan Masrour et al., Pang-Ning Tan, Heng Yan, and Abdol Esfahianian are affiliated with Michigan State University.

4420: Latent Type Model: Latent Type Modeling to Biomedical Entity Linking
Ming Z. et al.; Busra Célrikayaa; Parminder Bhati aa; Chandan K. Reddy
Virginia Tech and Amazon

5535: A Sequence-based model for Joint Entity Linking with Dynamic features

6492: At the same time, linking entities and extracting relations from biomedical text without mention-level supervision
Trapit Bansal; Patrick Verga; Neha Choudhary; Andrew McCallum
the University of Massachusetts Amherst

8427: Type-aware Anchor Link Prediction between diverse networks constructed upon graph attention mechanisms
Xiaoxue Li; Yanmin Shang; Yanan Cao; Yangxi Li; Jianlong Tan; Yanbing Liu
Chinese Academy of Sciences

9347: The Subtle Classification of Entities in Cross-Domain Linking
Yasumasa Onoe and Greg Durrett
University of Texas Austin

9502: Common Sense Knowledge Base Completion Integrating Structural and Semantic Contexts
Chaitanya Malaviya; Chandra Bhagavatula; Antoine Bosselut; Yejin Choi
Allen Institute for Artificial Intelligence; University of Washington

9358: 基于时间的嵌入方法用于知识图谱的时间补全
Rishab Goel; Seyed Mehran Kazemi; Marcus Brubaker; Pascal Poupart
Borealis AI

2278: Low-Shot Knowledge Graph Completion
Chenxu Zhang; Huaixiu Yao; Xiaochuan Huang; Ming Jiang; Zhennui (Jingzi Li) Li; Nitin Chawla
University of Notre-Dame; Pennsylvania State University

4020: ParamE: Parameterization E regarding neural network parameters as relation embeddings in the context of knowledge graph completion
Feihu Che, Dawei Zhang, Jianhua Tao, Mingyue Niu, and Bocheng Zhao

Relation-based Graph Neural Networks with Hierarchical Attention Mechanism towards Knowledge Graph Completion
Zhao Zhang, Fuzhen Zhuang, Hengshu Zhu, Zhiping Shi, Hui Xiong and Qing He from the Chinese Academy of Sciences, Baidu Inc., Capital Normal University and The State University of New Jersey.

Entity and Relation Learning and Extraction

3203: Self-attention mechanisms enhanced selective gate with entity-aware embedding for relation extraction tasks that are distant-supervised
Yang Li, Guodong Long, Tao Shen, Tianyi Zhou, Lina Yao, Huan Huo, Jing Jiang
The University of Technology Sydney, The University of Washington, The University of New South Wales;

3868: Enhancing Neural Relation Extractions through Positive Examples and Unsupervised Learning
Zhengqiu He; Wenliang Chen; Yuyi Wang; Wei Zhang; Guanchun Wang; Min Zhang
Soochow University; ETH Zurich; Laiye Startup;}

In the context of distant supervised relation extraction, are noisy sentences rendered ineffective?

4412: Data Duplication Mechanism for Cross-Entity Relation Extraction with End-to-End Multi-task Learning Framework
Daojian Zeng, Haoran Zhang, and Qianying Liu
Changsha University of Science and Technology, University of Illinois Urbana-Champaign, and Kyoto University

5816: Relation Extraction Based on Convolutional Neural Networks in Learnable Syntax-Transport Graphs

5895: Relation Extraction by Leveraging Full Dependency Forests
Lifeng Jin, Lin Feng Song, Yue Zhang, Kun Xu, Wei Yun Ma, and Dong Yu
Ohio State University, Tencent, Westlake University, and the Chinese Academy of Sciences

6937: Multi-View Consistency of Relation Extraction based on Mutual Information and Structure Prediction
Amir Pouran Ben-Veyseh; Franck Dernoncourt; My Thai; Dejing Dou; Thien Nguyen
University of Oregon; Adobe Research; University of Florida

7408: Extracting Knowledge from Well-founded Soft Labels for Neural Relation Extraction
Zhenyu Zhang, Xiaobo Shu, Bowen Yu, Tingwen Liu, Jiapeng Zhao, Quangang Li, and Li Guo are affiliated with the Chinese Academy of Sciences and the University of Chinese Academy of Sciences.

7540: 联合实体与关系抽取模型基于混合型Transformer强化学习驱动
Mr. Ya Xiao, Dr. Chengxiang Tan, Dr. Zhijie Fan, Dr. Qian Xu, Dr. Wenye Zhu
Tongji University, The Third Research Institute under the Ministry of Public Security

7819: Combining Relational Limitations with Neural Relation Extractors
Yuan Ye; Yansong Feng; Bingfeng Luo; Yuxuan Lai; Dongyan Zhao
Peking University

An Effective Approach to Modeling Encoder-Decoder Architectures for Joint Entity and Relation Extraction, conducted by Tapas Nayak; Hwee Tou Ng, affiliated with the National University of Singapore

7793: Adversarial Generative Zero Shot Relation Learning for Knowledge Graphs
Qin Pengda; Wang Xin; Chen Wenhu; Zhang Chunyun; Xu Weiran; Wang William Yang
University of Posts and Telecommunications, Beijing; University of California, Santa Barbara; Shandong University of Finance and Economics

4433: Neural Snowball for Few-shot Relation Learning
Contributed by Tianyu Gao, Xu Han, Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin, and Maosong Sun from Tsinghua University and Tencent.

Graph Alignment

Knowledge Graph Alignment Mechanism with Gated Multi-hop Neighborhood Aggregation

7248: Coordinated Reasoning in Application for Multilingual Knowledge Graph Alignment
Xu Kun; Song Linfeng; Feng Yansong; Song Yan
Ai Lab of Tencent; Peking University

8586: COTSAE: Co-Training of Structure and Attribute Embeddings for Entity Alignment
Kai Yang, Shaoqin Liu, Junfeng Zhao, Yasha Wang, Bing Xie
Peking University

Graph Embedding

8986: Enhancing Convolution-based Knowledge Graph (KG) embeddings through the improvement of feature interaction patterns
Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, Nilesh Agrawal, and Partha Talukdar
Institute of Science, Indian Institute of Science; Columbia University, United States

4560: 基于规则的组合式表示学习在知识图谱上

4000: 系统性地从知识图谱中学习三元组嵌入系统
Valeria Fionda; Giuseppe Pirrò
DeMACs; University of Calabria; University of Rome "La Sapienza"

KBQA

3419: Frame-based Meaningful Information Extraction from Advanced Query Patterns within Structured Data Sources

3330: Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering
Shangwen Lv; Daya Guo; Jingjing Xu; Duyu Tang; Nan Duan; Ming Gong; Linjun Shou; Daxin Jiang; Guihong Cao; Songlin Hu
Chinese Academy of Sciences; Sun-Yat Sen University; Peking University; Microsoft Research;

General/Other

10313: GraphER: Token-Centric Entity Resolution using Graph-based Convolutional Neural Networks
Bing Li, Wei Wang, Yifang Sun, Linhan Zhang, Muhammad Asif Ali, Yi Wang
University of New South Wales and Dongguan University of Technology

2775: Inference mechanisms concerning Knowledge Graphs incorporating Debate Dynamics
Among Marcel Hildebrandt, Jorge Andres Quintero Serna, Yunpu Ma, Martin Ringsquandl, Mitchell Joblin, and Volker Tresp.
Siemens and Ludwig Maximilian University of Munich.

5161: Knowledge Graphs' Transfer Networks in Few-Instance Learning

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