ATOM: AdapTive and OptiMized dynamic temporal knowledge graph
  construction using LLMs
Authors: name: Yassir Lairgi, name: Ludovic Moncla, name: Khalid Benabdeslem, name: Rémy Cazabet, name: Pierre Cléau

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 while employing a dual-time modeling
that distinguishes when information is observed from when it is valid. The
resulting atomic TKGs are subsequently merged in parallel. Empirical
evaluations demonstrate that ATOM achieves ~18% higher exhaustivity, ~17%
better stability, and over 90% latency reduction compared to baseline methods,
demonstrating a strong scalability potential for dynamic TKG construction.