ATOM is finally here! A scalable and fast approach that can build and continuously update temporal knowledge graphs, inspired by atomic bonds.
Alhamdulillah, ATOM is finally here! A scalable and fast approach that can build and continuously update temporal knowledge graphs, inspired by atomic bonds.
Just as matter is formed from atoms, and galaxies are formed from stars, knowledge is likely to be formed from atomic knowledge graphs.
Atomic knowledge graphs were born from our intention to solve a common problem in LLM-based KG construction methods: exhaustivity and stability. Often, these methods produce unstable KGs that change when rerunning the construction process, even without changing anything. Moreover, they fail to capture all facts in the input documents and usually overlook the temporal and dynamic aspects of real-world data.
What is the solution? Atomic facts that are temporally aware.
Instead of constructing knowledge graphs from raw documents, we split them into atomic facts, which are self-contained and concise propositions. Temporal atomic KGs are constructed from each atomic fact. Then, we defined how the temporal atomic KGs would be merged at the atomic level and how the temporal aspects would be handled. We designed a binary merge algorithm that combines two TKGs and a parallel merge process that merges all TKGs simultaneously. The entire architecture operates in parallel.
ATOM employs dual-time modeling that distinguishes observation time from validity time and has 3 main modules:
- Module 1 (Atomic Fact Decomposition) splits input documents observed at time t into atomic facts using LLM-based prompting, where each temporal atomic fact is a short, self-contained snippet that conveys exactly one piece of information.
- Module 2 (Atomic TKGs Construction) extracts 5-tuples in parallel from each atomic fact to construct atomic temporal KGs, while embedding nodes and relations and addressing temporal resolution during extraction.
- Module 3 (Parallel Atomic Merge) employs a binary merge algorithm to merge pairs of atomic TKGs through iterative pairwise merging in parallel until convergence, with three resolution phases: (1) entity resolution, (2) relation name resolution, and (3) temporal resolution that merges observation and validity time sets for relations with similar (e_s, r_p, e_o). The resulting TKG snapshot is then merged with the previous DTKG to yield the updated DTKG.
Results: Empirical evaluations demonstrate that ATOM achieves ~18% higher exhaustivity, ~17% better stability, and over 90% latency reduction compared to baseline methods (including iText2KG), demonstrating strong scalability potential for dynamic TKG construction.
Check our ATOM's architecture and code:
Preprint Paper: https://lnkd.in/dsJzDaQc
Code: https://lnkd.in/drZUyisV
Website: (coming soon)
Example use cases: (coming soon)
Special thanks to the dream team: Ludovic Moncla, Khalid Benabdeslem, Rรฉmy Cazabet, Pierre Clรฉau ๐๐ก | 14 comments on LinkedIn
ATOM is finally here! A scalable and fast approach that can build and continuously update temporal knowledge graphs, inspired by atomic bonds.
