A survey of knowledge graph reasoning on graph types: Static, dynamic, and multi-modal
Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …
Llava-o1: Let vision language models reason step-by-step
Large language models have demonstrated substantial advancements in reasoning
capabilities, particularly through inference-time scaling, as illustrated by models such as …
capabilities, particularly through inference-time scaling, as illustrated by models such as …
DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature
Recent advancements in large language models (LLMs) have achieved promising
performances across various applications. Nonetheless, the ongoing challenge of …
performances across various applications. Nonetheless, the ongoing challenge of …
Large language models can learn temporal reasoning
Large language models (LLMs) learn temporal concepts from the co-occurrence of related
tokens in a sequence. Compared with conventional text generation, temporal reasoning …
tokens in a sequence. Compared with conventional text generation, temporal reasoning …
FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference
Personalized Federated Graph Learning (pFGL) facilitates the decentralized training of
Graph Neural Networks (GNNs) without compromising privacy while accommodating …
Graph Neural Networks (GNNs) without compromising privacy while accommodating …
RealTCD: temporal causal discovery from interventional data with large language model
In the field of Artificial Intelligence for Information Technology Operations, causal discovery
is pivotal for operation and maintenance of systems, facilitating downstream industrial tasks …
is pivotal for operation and maintenance of systems, facilitating downstream industrial tasks …
Transtarec: Time-adaptive translating embedding model for next poi recommendation
Y Sun - arxiv preprint arxiv:2404.07096, 2024 - arxiv.org
The rapid growth of location acquisition technologies makes Point-of-Interest (POI)
recommendation possible due to redundant user check-in records. In this paper, we focus …
recommendation possible due to redundant user check-in records. In this paper, we focus …
Deliberate reasoning for llms as structure-aware planning with accurate world model
Enhancing the reasoning capabilities of large language models (LLMs) remains a key
challenge, especially for tasks that require complex, multi-step decision-making. Humans …
challenge, especially for tasks that require complex, multi-step decision-making. Humans …
A rule-and query-guided reinforcement learning for extrapolation reasoning in temporal knowledge graphs
T Chen, L Yang, Z Wang, J Long - Neural Networks, 2025 - Elsevier
Extrapolation reasoning in temporal knowledge graphs (TKGs) aims at predicting future facts
based on historical data, and finds extensive application in diverse real-world scenarios …
based on historical data, and finds extensive application in diverse real-world scenarios …
Temporal knowledge graph reasoning based on discriminative neighboring semantic learning
J Zhang, B Hui, X Zhu, L Tian, F Hua - Pattern Recognition, 2025 - Elsevier
Abstract Temporal Knowledge Graphs (TKGs) reflect the dynamic temporal evolution of real-
world facts. The extrapolation of TKG reasoning, which predicts future facts based on …
world facts. The extrapolation of TKG reasoning, which predicts future facts based on …