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

流浪者

Dynamic Weighted Neural Bellman-Ford Network for Knowledge Graph ReasoningGR). However, prior studies tend to focus solely on enhancing entity representations using related relations, with little attention paid to the impact of different relations on different entities and their importance in various reasoning paths. Meanwhile, conventional Graph Neural Networks (GNNs) ut

诙谐

CausE: Towards Causal Knowledge Graph Embeddingcan be employed to predict the missing triples to achieve knowledge graph completion (KGC). However, KGE models often only briefly learn structural correlations of triple data and embeddings would be misled by the trivial patterns and noisy links in real-world KGs. To address this issue, we build th

Harness

Exploring the Logical Expressiveness of Graph Neural Networks by Establishing a Connection with ed data. However, the computational power and logical expressiveness of GNNs are still not fully understood. This work explores the logical expressiveness of GNNs from a theoretical view and establishes a connection between them and the fragment of first-order logic, known as ., which servers as a l

商议

Research on Joint Representation Learning Methods for Entity Neighborhood Information and Descriptioombines entity neighborhood information and description information is proposed. Firstly, a graph attention network is employed to obtain the features of entity neighboring nodes, incorporating relationship features to enrich the structural information. Next, the BERT-WWM model is utilized in conjun

sigmoid-colon

Aromatic

NTDA: Noise-Tolerant Data Augmentation for Document-Level Event Argument Extractionaugmentation can leverage annotated data to augment training data, but always encounters the issue of noise. The noise mainly consists of two aspects: boundary annotation differences and domain knowledge discrepancy, which may significantly impact the effectiveness of data augmentation. In this pape

N斯巴达人

Event-Centric Opinion Mining via In-Context Learning with ChatGPTof events themselves, encompassing their underlying causes, effects, and consequences. This helps us to understand and explain social phenomena in a more comprehensive way. In this regard, we introduce ChatGPT-opinion mining as a framework that transforms event-centric opinion mining tasks into ques

潜伏期

Relation Repository Based Adaptive Clustering for Open Relation Extractionmantically overlapping regions often remain indistinguishable. In this work, we propose an adaptive clustering method based on a relation repository to explicitly model the semantic differences between clusters to mitigate the relational semantic overlap in unlabeled data. Specifically, we construct

PSA-velocity

LNFGP: Local Node Fusion-Based Graph Partition by Greedy Clusteringn graph partitioning can be generally categorized into two types: vertex partitioning and edge partitioning. Due to the independent nature of vertex partitioning, which facilitates easier management and maintenance, vertex partitioning methods have become more practical and popular. However, most ex

Insufficient

Multi-Perspective Frame Element Representation for Machine Reading Comprehension knowledge provided by FrameNet to enhance the performance of MRC systems. While significant efforts have been dedicated to Frame representation, there is a noticeable lack of research on Frame Element (FE) representation, which is equally crucial for MRC. We propose a groundbreaking approach called