Joint Semantics and Data-Driven Path Representation for Knowledge Graph Inference
Beihang University（北京航空航天大学）；Pengcheng Laboratory（鹏城实验室）；Chongqing University（重庆大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Hikvision Research Institute（海康威视研究院）
Guanglin Niu, Bo Li, Yongfei Zhang, Yongpan Sheng, Chuan Shi, Jingyang Li, Shiliang Pu
Inference on a large-scale knowledge graph (KG) is of great importance for KG applications like question answering. The path-based reasoning models can leverage much information over paths other than pure triples in the KG, which face several challenges: all the existing path-based methods are data-driven, lacking explainability for path representation. Besides, some methods either consider only relational paths or ignore the heterogeneity between entities and relations both contained in paths, which cannot capture the rich semantics of paths well. To address the above challenges, in this work, we propose a novel joint semantics and data-driven path representation that balances explainability and generalization in the framework of KG embedding. More specifically, we inject horn rules to obtain the condensed paths by the transparent and explainable path composition procedure. The entity converter is designed to transform the entities along paths into the representations in the semantic level similar to relations for reducing the heterogeneity between entities and relations, in which the KGs both with and without type information are considered. Our proposed model is evaluated on two classes of tasks: link prediction and path query answering task. The experimental results show that it has a significant performance gain over several different state-of-the-art baselines.
Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Unisound Information Technology Co., Ltd（北京云知声信息技术有限公司）
SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China（南京大学新型软件技术国家重点实验室）；Amap, Alibaba Group, China（阿里巴巴集团阿地图）；University of Alberta, Edmonton, Canada（阿尔伯塔大学）；The University of Tokyo, Tokyo, Japan（东京大学）
University of Macau（澳门大学）；Xiamen University（厦门大学）；Peking University（北京大学）；Hanyang University（翰阳大学）；University of Liverpool（利物浦大学）；Zhejiang University（浙江大学）
RNG: Reducing Multi-level Noise and Multi-grained Semantic Gap for Joint Multimodal Aspect-Sentiment Analysis
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, UCAS（中国科学院大学网络空间安全学院）；Academy of Cyber（网络空间研究院）
Joint Semantic Transfer Network for IoT Intrusion Detection
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Institute of Technology（北京理工大学）；University of Macau（澳门大学）
Semantic Channel Equalization Strategies for Deep Joint Source-Channel Coding
DIAG Department, Sapienza University of Rome（罗马大学DIAG系）；Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT)（国家跨大学电信合作机构）；CEA Leti, University Grenoble Alpes（CEA Leti与格勒诺布尔阿尔卑斯大学）；DIET Department, Sapienza University of Rome（罗马大学DIET系）
