ATOM: AdapTive and OptiMized dynamic temporal knowledge graph construction using LLMs
Yassir Lairgi, Ludovic Moncla, Khalid Benabdeslem, Rémy Cazabet, Pierre Cléau · 2026
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 1 (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.