Titlebook: Knowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence; 8th China Conference Haofen Wang,Xianpei

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https://doi.org/10.1007/978-981-99-7224-1artificial intelligence; computational linguistics; computer networks; data mining; databases; graph theo

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Knowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence978-981-99-7224-1Series ISSN 1865-0929 Series E-ISSN 1865-0937

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Dynamic Weighted Neural Bellman-Ford Network for?Knowledge Graph Reasoninggraphs to compute only the most relevant relations and entities. This way, we can integrate multiple reasoning paths more flexibly to achieve better interpretable reasoning, while scaling more easily to more complex and larger KGs. DyNBF consists of two key modules: 1) a transformer-based relation w

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Exploring the?Logical Expressiveness of?Graph Neural Networks by?Establishing a?Connection with? for the handling of both unary and binary predicates in . formulas. We prove that the proposed models possess the same expressiveness as .. Through experiments conducted on synthetic and real datasets, we validate that our proposed models outperform both ACR-GNN and a widely-used model, GIN, in the

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Relation Repository Based Adaptive Clustering for?Open Relation Extractionon boundary, which lead to generate cluster-friendly relation representations to improve the effect of open relation extraction. Experiments on two public datasets show that our method can effectively improve the performance of open relation extraction.

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Multi-Perspective Frame Element Representation for?Machine Reading Comprehensiondemonstrate that our proposed model outperforms existing state-of-the-art methods. The superiority of our approach highlights its potential for advancing the field of MRC and showcasing the importance of properly modeling FEs for better semantic understanding.