RECIPE-TKG: From Sparse History to Structured Reasoning for LLM-based Temporal Knowledge Graph Completion

• URL: http://arxiv.org/abs/2505.17794v1
• Date: Fri, 23 May 2025 12:11:40 GMT
• Title: RECIPE-TKG: From Sparse History to Structured Reasoning for LLM-based Temporal Knowledge Graph Completion
• Authors: Ömer Faruk Akgül, Feiyu Zhu, Yuxin Yang, Rajgopal Kannan, Viktor Prasanna,
• Abstract summary: Temporal Knowledge Graphs (TKGs) represent dynamic facts as timestamped relations between entities.<n>We introduce RECIPE-TKG, a lightweight and data-efficient framework designed to improve accuracy and generalization in settings with sparse historical context.
• Score: 3.680772033409751
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Temporal Knowledge Graphs (TKGs) represent dynamic facts as timestamped relations between entities. TKG completion involves forecasting missing or future links, requiring models to reason over time-evolving structure. While LLMs show promise for this task, existing approaches often overemphasize supervised fine-tuning and struggle particularly when historical evidence is limited or missing. We introduce RECIPE-TKG, a lightweight and data-efficient framework designed to improve accuracy and generalization in settings with sparse historical context. It combines (1) rule-based multi-hop retrieval for structurally diverse history, (2) contrastive fine-tuning of lightweight adapters to encode relational semantics, and (3) test-time semantic filtering to iteratively refine generations based on embedding similarity. Experiments on four TKG benchmarks show that RECIPE-TKG outperforms previous LLM-based approaches, achieving up to 30.6\% relative improvement in Hits@10. Moreover, our proposed framework produces more semantically coherent predictions, even for the samples with limited historical context.

Related papers

• Towards Improving Long-Tail Entity Predictions in Temporal Knowledge Graphs through Global Similarity and Weighted Sampling [53.11315884128402]
  Temporal Knowledge Graph (TKG) completion models traditionally assume access to the entire graph during training.<n>We present an incremental training framework specifically designed for TKGs, aiming to address entities that are either not observed during training or have sparse connections.<n>Our approach combines a model-agnostic enhancement layer with a weighted sampling strategy, that can be augmented to and improve any existing TKG completion method.
  arXiv  Detail & Related papers  (2025-07-25T06:02:48Z)
• Respecting Temporal-Causal Consistency: Entity-Event Knowledge Graphs for Retrieval-Augmented Generation [69.45495166424642]
  We develop a robust and discriminative QA benchmark to measure temporal, causal, and character consistency understanding in narrative documents.<n>We then introduce Entity-Event RAG (E2RAG), a dual-graph framework that keeps separate entity and event subgraphs linked by a bipartite mapping.<n>Across ChronoQA, our approach outperforms state-of-the-art unstructured and KG-based RAG baselines, with notable gains on causal and character consistency queries.
  arXiv  Detail & Related papers  (2025-06-06T10:07:21Z)
• Compile Scene Graphs with Reinforcement Learning [69.36723767339001]
  Next-token prediction is the fundamental principle for training large language models (LLMs)<n>We introduce R1-SGG, a multimodal LLM (M-LLM) initially trained via supervised fine-tuning (SFT) on the scene graph dataset.<n>We design a set of graph-centric rewards, including three recall-based variants -- Hard Recall, Hard Recall+Relax, and Soft Recall.
  arXiv  Detail & Related papers  (2025-04-18T10:46:22Z)
• Ignite Forecasting with SPARK: An Efficient Generative Framework for Refining LLMs in Temporal Knowledge Graph Forecasting [13.402856325579236]
  We introduce SPARK, a Sequence-level Proxy framework for refining Large Language Models in TKG forecasting.<n>Inspired by inference-time algorithms, SPARK offers a cost-effective, plug-and-play solution through two key innovations.<n> Experiments across diverse datasets validate SPARK's forecasting performance, robust generalization capabilities, and high efficiency.
  arXiv  Detail & Related papers  (2025-03-27T03:02:02Z)
• KG-CF: Knowledge Graph Completion with Context Filtering under the Guidance of Large Language Models [55.39134076436266]
  KG-CF is a framework tailored for ranking-based knowledge graph completion tasks.<n> KG-CF leverages LLMs' reasoning abilities to filter out irrelevant contexts, achieving superior results on real-world datasets.
  arXiv  Detail & Related papers  (2025-01-06T01:52:15Z)
• Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation [9.844598565914055]
  Large Language Models (LLMs) demonstrate strong reasoning abilities but face limitations such as hallucinations and outdated knowledge.<n>We introduce SubgraphRAG, extending the Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) framework that retrieves subgraphs.<n>Our approach innovatively integrates a lightweight multilayer perceptron with a parallel triple-scoring mechanism for efficient and flexible subgraph retrieval.
  arXiv  Detail & Related papers  (2024-10-28T04:39:32Z)
• Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning [87.10396098919013]
  Large Language Models (LLMs) have demonstrated extensive knowledge and remarkable proficiency in temporal reasoning.<n>We propose a Large Language Models-guided Dynamic Adaptation (LLM-DA) method for reasoning on Temporal Knowledge Graphs.<n>LLM-DA harnesses the capabilities of LLMs to analyze historical data and extract temporal logical rules.
  arXiv  Detail & Related papers  (2024-05-23T04:54:37Z)
• Learning Multi-graph Structure for Temporal Knowledge Graph Reasoning [3.3571415078869955]
  This paper proposes an innovative reasoning approach that focuses on Learning Multi-graph Structure (LMS) LMS incorporates an adaptive gate for merging entity representations both along and across timestamps effectively. It also integrates timestamp semantics into graph attention calculations and time-aware decoders.
  arXiv  Detail & Related papers  (2023-12-04T08:23:09Z)
• GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models [35.594662986581746]
  Large language models (LLMs) have ignited interest in the temporal knowledge graph (tKG) domain, where conventional embedding-based and rule-based methods dominate. We propose a novel retrieval-augmented generation framework named GenTKG combining a temporal logical rule-based retrieval strategy and few-shot parameter-efficient instruction tuning. Experiments have shown that GenTKG outperforms conventional methods of temporal relational forecasting with low computation resources.
  arXiv  Detail & Related papers  (2023-10-11T18:27:12Z)
• Exploring the Limits of Historical Information for Temporal Knowledge Graph Extrapolation [59.417443739208146]
  We propose a new event forecasting model based on a novel training framework of historical contrastive learning. CENET learns both the historical and non-historical dependency to distinguish the most potential entities. We evaluate our proposed model on five benchmark graphs.
  arXiv  Detail & Related papers  (2023-08-29T03:26:38Z)
• Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning [23.971206470486468]
  We present a framework that converts relevant historical facts into prompts and generates ranked predictions using token probabilities. Surprisingly, we observe that LLMs, out-of-the-box, perform on par with state-of-the-art TKG models. We also discover that using numerical indices instead of entity/relation names, does not significantly affect the performance.
  arXiv  Detail & Related papers  (2023-05-17T23:50:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.