OntoTKGE: Ontology-Enhanced Temporal Knowledge Graph Extrapolation

• URL: http://arxiv.org/abs/2604.05468v1
• Date: Tue, 07 Apr 2026 06:06:45 GMT
• Title: OntoTKGE: Ontology-Enhanced Temporal Knowledge Graph Extrapolation
• Abstract summary: A key challenge for most TKG extrapolation models is handling entities with sparse historical interaction.<n>We propose a novel encoder-decoder framework that leverages the ontological knowledge from the ontological-view KG.<n>OntoTKGE is flexible enough to adapt to many TKG extrapolation models.
• Score: 8.23535650340222
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Temporal knowledge graph (TKG) extrapolation is an important task that aims to predict future facts through historical interaction information within KG snapshots. A key challenge for most existing TKG extrapolation models is handling entities with sparse historical interaction. The ontological knowledge is beneficial for alleviating this sparsity issue by enabling these entities to inherit behavioral patterns from other entities with the same concept, which is ignored by previous studies. In this paper, we propose a novel encoder-decoder framework OntoTKGE that leverages the ontological knowledge from the ontology-view KG (i.e., a KG modeling hierarchical relations among abstract concepts as well as the connections between concepts and entities) to guide the TKG extrapolation model's learning process through the effective integration of the ontological and temporal knowledge, thereby enhancing entity embeddings. OntoTKGE is flexible enough to adapt to many TKG extrapolation models. Extensive experiments on four data sets demonstrate that OntoTKGE not only significantly improves the performance of many TKG extrapolation models but also surpasses many SOTA baseline methods.

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)
• Efficient Relational Context Perception for Knowledge Graph Completion [25.903926643251076]
  Knowledge Graphs (KGs) provide a structured representation of knowledge but often suffer from challenges of incompleteness.<n>Previous knowledge graph embedding models are limited in their ability to capture expressive features.<n>We propose Triple Receptance Perception architecture to model sequential information, enabling the learning of dynamic context.
  arXiv  Detail & Related papers  (2024-12-31T11:25:58Z)
• Distill-SynthKG: Distilling Knowledge Graph Synthesis Workflow for Improved Coverage and Efficiency [59.6772484292295]
  Knowledge graphs (KGs) generated by large language models (LLMs) are increasingly valuable for Retrieval-Augmented Generation (RAG) applications. Existing KG extraction methods rely on prompt-based approaches, which are inefficient for processing large-scale corpora. We propose SynthKG, a multi-step, document-level synthesis KG workflow based on LLMs. We also design a novel graph-based retrieval framework for RAG.
  arXiv  Detail & Related papers  (2024-10-22T00:47:54Z)
• Relational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective [60.64922606733441]
  We introduce a mathematical model that formalizes relational learning as hypergraph recovery to study pre-training of Foundation Models (FMs) In our framework, the world is represented as a hypergraph, with data abstracted as random samples from hyperedges. We theoretically examine the feasibility of a Pre-Trained Model (PTM) to recover this hypergraph and analyze the data efficiency in a minimax near-optimal style.
  arXiv  Detail & Related papers  (2024-06-17T06:20:39Z)
• Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion [69.55700751102376]
  Few-shot knowledge graph completion (FKGC) aims to predict missing facts for unseen relations with few-shot associated facts. Existing FKGC methods are based on metric learning or meta-learning, which often suffer from the out-of-distribution and overfitting problems. In this paper, we propose a normalizing flow-based neural process for few-shot knowledge graph completion (NP-FKGC)
  arXiv  Detail & Related papers  (2023-04-17T11:42:28Z)
• Meta-Learning Based Knowledge Extrapolation for Temporal Knowledge Graph [4.103806361930888]
  Temporal KGs (TKGs) extend traditional Knowledge Graphs by associating static triples with timestamps forming quadruples. We propose a Meta-Learning based Temporal Knowledge Graph Extrapolation (MTKGE) model, which is trained on link prediction tasks sampled from the existing TKGs. We show that MTKGE consistently outperforms both the existing state-of-the-art models for knowledge graph extrapolation.
  arXiv  Detail & Related papers  (2023-02-11T09:52:26Z)
• Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information [31.10140298420744]
  We propose a few-shot out-of-graph (OOG) link prediction task for temporal knowledge graphs (TKGs) We predict the missing entities from the links concerning unseen entities by employing a meta-learning framework. Our model achieves superior performance on all three datasets.
  arXiv  Detail & Related papers  (2022-11-15T14:23:07Z)
• Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction [33.36701435886095]
  Few-shot relational learning for static knowledge graphs (KGs) has drawn greater interest in recent years. TKGs contain rich temporal information, thus requiring temporal reasoning techniques for modeling. This poses a greater challenge in learning few-shot relations in the temporal context.
  arXiv  Detail & Related papers  (2022-05-21T15:17:52Z)
• EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs [25.408246523764085]
  Reasoning over temporal knowledge graphs (TKGs) is crucial for many applications to provide intelligent services. We present a problem formulation that unifies the two major problems that need to be addressed for an effective reasoning over TKGs, namely, modeling the event time and the evolving network structure. Our proposed method EvoKG jointly models both tasks in an effective framework, which captures the ever-changing structural and temporal dynamics in TKGs.
  arXiv  Detail & Related papers  (2022-02-15T18:49:53Z)
• Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning [59.004025528223025]
  Key to predict future facts is to thoroughly understand the historical facts. A TKG is actually a sequence of KGs corresponding to different timestamps. We propose a novel Recurrent Evolution network based on Graph Convolution Network (GCN)
  arXiv  Detail & Related papers  (2021-04-21T05:12:21Z)
• Learning Intents behind Interactions with Knowledge Graph for Recommendation [93.08709357435991]
  Knowledge graph (KG) plays an increasingly important role in recommender systems. Existing GNN-based models fail to identify user-item relation at a fine-grained level of intents. We propose a new model, Knowledge Graph-based Intent Network (KGIN)
  arXiv  Detail & Related papers  (2021-02-14T03:21:36Z)

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.