Beyond Single Pass, Looping Through Time: KG-IRAG with Iterative Knowledge Retrieval

Retrieval-augmented generation (RAG) has improved large language models (LLMs) on knowledge-intensive tasks, yet most systems assume static facts and struggle when answers depend on serialized and dynamic data, like time--e.g., ordering events, aligning facts to valid intervals, or planning actions under evolving conditions. This paper presents Knowledge-Graph Iterative Retrieval-Augmented Generation (KG-iRAG), a framework specialized for temporal reasoning. KG-iRAG couples a time-aware planner with a knowledge graph (KG) to iteratively fetch and compose evidence along a temporal axis. Concretely, it (i) represents events and facts with explicit timestamps and validity intervals; (ii) propagates temporal constraints through iterative retrieval using operators; and (iii) verifies temporal consistency while refining intermediate hypotheses, enabling step-by-step deduction for queries that mix knowledge retrieval with inference. Across public temporal QA benchmarks, KG-iRAG consistently improves accuracy and calibration over strong RAG baselines while reducing unnecessary retrieval through targeted, constraint-guided steps. To stress-test real-time decision queries, three application-oriented datasets (weatherQA-Irish, ~ weatherQA-Sydney, and~ trafficQA-TFNSW) are additionally constructed and tested alongside existing temporal benchmarks. The results demonstrate that injecting temporal structure into KG-driven RAG yields robust gains on multi-step, time-dependent queries, advancing the state of temporal reasoning with LLMs.

Citation

R. Yang, H. Xue, I. Razzak, F.D. Salim, "Beyond Single Pass, Looping Through Time: KG-IRAG with Iterative Knowledge Retrieval," 2026, pp. 4460-4471.

Source

WWW '26: Proceedings of the ACM Web Conference 2026

Conference

ACM Web Conference 2026

Keywords

46 Information and Computing Sciences, 4602 Artificial Intelligence, 4605 Data Management and Data Science

Source

ACM Web Conference 2026

Publisher

Association for Computing Machinery

DOI

10.1145/3774904.3792745

Additional links

https://dl.acm.org/doi/10.1145/3774904.3792745