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 and experience hallucinations …
complex tasks. However, they lack up-to-date knowledge and experience hallucinations …
Reasoning beyond Triples: Recent Advances in Knowledge Graph Embeddings
Knowledge Graphs (KGs) are a collection of facts describing entities connected by
relationships. KG embeddings map entities and relations into a vector space while …
relationships. KG embeddings map entities and relations into a vector space while …
Chatrule: Mining logical rules with large language models for knowledge graph reasoning
Logical rules are essential for uncovering the logical connections between relations, which
could improve the reasoning performance and provide interpretable results on knowledge …
could improve the reasoning performance and provide interpretable results on knowledge …
Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning
Graph learning plays a pivotal role and has gained significant attention in various
application scenarios, from social network analysis to recommendation systems, for its …
application scenarios, from social network analysis to recommendation systems, for its …
MADE: Multicurvature Adaptive Embedding for Temporal Knowledge Graph Completion
Temporal knowledge graphs (TKGs) are receiving increased attention due to their time-
dependent properties and the evolving nature of knowledge over time. TKGs typically …
dependent properties and the evolving nature of knowledge over time. TKGs typically …
NestE: modeling nested relational structures for knowledge graph reasoning
Abstract Reasoning with knowledge graphs (KGs) has primarily focused on triple-shaped
facts. Recent advancements have been explored to enhance the semantics of these facts by …
facts. Recent advancements have been explored to enhance the semantics of these facts by …
Transformer-based reasoning for learning evolutionary chain of events on temporal knowledge graph
Temporal Knowledge Graph (TKG) reasoning often involves completing missing factual
elements along the timeline. Although existing methods can learn good embeddings for …
elements along the timeline. Although existing methods can learn good embeddings for …
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 reflecting the dynamic nature of the real …
precise capture of the evolution of knowledge and reflecting the dynamic nature of the real …
Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning
Temporal Knowledge Graph Reasoning (TKGR) is the process of utilizing temporal
information to capture complex relations within a Temporal Knowledge Graph (TKG) to infer …
information to capture complex relations within a Temporal Knowledge Graph (TKG) to infer …
Incorporating Multi-Level Sampling with Adaptive Aggregation for Inductive Knowledge Graph Completion
In recent years, Graph Neural Networks (GNNs) have achieved unprecedented success in
handling graph-structured data, thereby driving the development of numerous GNN-oriented …
handling graph-structured data, thereby driving the development of numerous GNN-oriented …