Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
The availability of large scale event data with time stamps has given rise to dynamically evolving knowledge graphs that contain temporal information for each edge. Reasoning over time in such dynamic knowledge graphs is not yet well understood. To this end, we present Know-Evolve, a novel deep evolutionary knowledge network that learns non-linearly evolving entity representations over time. The occurrence of a fact (edge) is modeled as a multivariate point process whose intensity function is modulated by the score for that fact computed based on the learned entity embeddings. We demonstrate significantly improved performance over various relational learning approaches on two large scale real-world datasets. Further, our method effectively predicts occurrence or recurrence time of a fact which is novel compared to prior reasoning approaches in multi-relational setting.
Code (2)
Tasks
Entity EmbeddingsKnowledge GraphsRelational ReasoningSimilar Papers 제목 키워드 기반
Temporal Reasoning with Large Language Models Augmented by Evolving Knowledge Graphs
Large language models (LLMs) excel at many language understanding tasks but struggle to reason over knowledge that evolves. To address this, recent work has explored augmenting LLMs with knowledge graphs (KGs) to provide…
Question AnsweringKnowledge GraphsWhen Facts Change: Probing LLMs on Evolving Knowledge with evolveQA
LLMs often fail to handle temporal knowledge conflicts--contradictions arising when facts evolve over time within their training data. Existing studies evaluate this phenomenon through benchmarks built on structured know…
TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs
Conventional static knowledge graphs model entities in relational data as nodes, connected by edges of specific relation types. However, information and knowledge evolve continuously, and temporal dynamics emerge, which …
Knowledge GraphsLink PredictionPerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning
Personalized healthcare decisions require reasoning about how physiological and behavioral variables influence an individual patient over time. Existing temporal causal discovery methods are poorly matched to this settin…
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 new knowledge. Conventional methods in TKGR typical…