ATOM: AdapTive and OptiMized dynamic temporal knowledge graph construction using LLMs
This addresses the need for real-time analytics and dynamic memory frameworks by improving adaptability and scalability in temporal knowledge graph construction, though it appears incremental as it builds on existing few-shot approaches.
The paper tackled the problem of building dynamic temporal knowledge graphs from unstructured text by introducing ATOM, which splits documents into atomic facts and uses dual-time modeling, achieving ~18% higher exhaustivity, ~17% better stability, and over 90% latency reduction compared to baselines.
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.