ATOM: AdapTive and OptiMized dynamic temporal knowledge graph construction using LLMs

TL;DR

ATOM is a scalable, few-shot method for constructing and updating dynamic temporal knowledge graphs from unstructured text, improving coverage, stability, and efficiency over existing approaches.

Contribution

It introduces a novel atomic fact-based framework with dual-time modeling for continuous TKG updates, addressing instability and incompleteness in prior zero-shot methods.

Findings

18% higher exhaustivity

33% better stability

90% latency reduction

Abstract

In today's rapidly expanding data landscape, knowledge extraction from unstructured text is vital for real-time analytics, temporal inference, and dynamic memory frameworks. However, traditional static knowledge graph (KG) construction often overlooks the dynamic and time-sensitive nature of real-world data, limiting adaptability to continuous changes. Moreover, recent zero- or few-shot approaches that avoid domain-specific fine-tuning or reliance on prebuilt ontologies often suffer from instability across multiple runs, as well as incomplete coverage of key facts. To address these challenges, we introduce ATOM (AdapTive and OptiMized), a few-shot and scalable approach that builds and continuously updates Temporal Knowledge Graphs (TKGs) from unstructured texts. ATOM splits input documents into minimal, self-contained "atomic" facts, improving extraction exhaustivity and stability. Then, it constructs atomic TKGs from these facts, employing a dual-time modeling that distinguishes between when information is observed and when it is valid. The resulting atomic TKGs are subsequently merged in parallel. Empirical evaluations demonstrate that ATOM achieves ~18% higher exhaustivity, ~33% better stability, and over ~90% latency reduction compared to baseline methods, demonstrating a strong scalability potential for dynamic TKG construction.