Knowledge Graphs

IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion
Temporal Knowledge Graphs (TKGs) incorporate a temporal dimension, allowing for a precise capture of the evolution of knowledge and …
Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning
Large language models (LLMs) have demonstrated impressive reasoning abilities in complex tasks. However, they lack up-to-date knowledge …
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Unifying Large Language Models and Knowledge Graphs: A Roadmap
Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the field of natural language processing and artificial …
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Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approach
Few-shot inductive link prediction on knowledge graphs (KGs) aims to predict missing links for unseen entities with few-shot links …
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Shrinking Embeddings for Hyper-Relational Knowledge Graphs
Link prediction on knowledge graphs (KGs) has been extensively studied on binary relational KGs, wherein each fact is represented by a …
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A Dynamic Variational Framework for Open-World Node Classification in Structured Sequences
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Multi-Relational Graph Neural Architecture Search with Fine-grained Message Passing
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Unifying Graph Contrastive Learning with Flexible Contextual Scopes
Graph contrastive learning (GCL) has recently emerged as an effective learning paradigm to alleviate the reliance on labelling …
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Exploring Relational Semantics for Inductive Knowledge Graph Completion
Knowledge graph completion (KGC) aims to infer missing information in incomplete knowledge graphs (KGs). Most previous works only …
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Reasoning Like Human: Hierarchical Reinforcement Learning for Knowledge Graph Reasoning
Knowledge Graphs typically suffer from incompleteness. A popular approach to knowledge graph completion is to infer missing knowledge …
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