OntoTKGE: Ontology-Enhanced Temporal Knowledge Graph Extrapolation
arXiv:2604.05468v1 Announce Type: new 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 eno
arXiv:2604.05468v1 Announce Type: new 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.
Executive Summary
The article introduces OntoTKGE, an innovative encoder-decoder framework designed to enhance temporal knowledge graph (TKG) extrapolation by integrating ontological knowledge. Traditional TKG models struggle with entities lacking sufficient historical interactions, leading to sparse data challenges. OntoTKGE addresses this by leveraging an ontology-view knowledge graph (KG), which captures hierarchical relations among abstract concepts and their connections to entities, enabling entities to inherit behavioral patterns from conceptually similar entities. The framework is model-agnostic, improving the performance of various TKG extrapolation models across four datasets, and outperforming several state-of-the-art baselines. This work highlights the untapped potential of ontological knowledge in temporal reasoning tasks.
Key Points
- ▸ OntoTKGE addresses the sparsity issue in temporal knowledge graph (TKG) extrapolation by integrating ontological knowledge from an ontology-view KG.
- ▸ The framework employs an encoder-decoder architecture to effectively fuse ontological and temporal knowledge, enhancing entity embeddings for improved prediction of future facts.
- ▸ OntoTKGE is adaptable to multiple TKG extrapolation models and demonstrates superior performance over state-of-the-art baselines across four benchmark datasets.
Merits
Novel Integration of Ontological Knowledge
The paper presents a groundbreaking approach by incorporating ontological knowledge into TKG extrapolation, addressing the sparsity issue through hierarchical concept-entity relationships, which has been overlooked in prior research.
Model-Agnostic Flexibility
OntoTKGE's adaptability to various TKG extrapolation models enhances its practical utility, allowing for seamless integration with existing frameworks without requiring significant architectural modifications.
Empirical Robustness and Superior Performance
Extensive experiments across four datasets demonstrate that OntoTKGE not only improves the performance of multiple baseline models but also surpasses several state-of-the-art methods, underscoring its empirical robustness and effectiveness.
Demerits
Dependency on High-Quality Ontology-View KG
The effectiveness of OntoTKGE is contingent upon the availability and quality of the ontology-view KG. Poorly constructed or incomplete ontologies may limit the framework's performance, introducing potential biases or inaccuracies in entity embeddings.
Computational Overhead
Integrating ontological knowledge into TKG extrapolation may introduce additional computational complexity, particularly during the training phase, which could pose challenges for scalability or real-time applications.
Limited Generalizability to Non-Hierarchical Ontologies
The current framework is tailored for hierarchical ontologies. Its performance with non-hierarchical or flat ontologies, or those with complex relational structures, remains untested and may require further validation.
Expert Commentary
The work presented in 'OntoTKGE' represents a significant advancement in the field of temporal knowledge graph extrapolation by addressing a longstanding challenge: the sparsity of historical interaction data for certain entities. The authors' innovative approach of leveraging ontological knowledge to enrich entity embeddings is both theoretically sound and empirically validated. By demonstrating the framework's adaptability and superior performance across multiple datasets, they have established a new benchmark for TKG extrapolation models. This research not only bridges the gap between symbolic and statistical AI paradigms but also opens avenues for exploring the synergy between ontologies and temporal reasoning in broader machine learning contexts. However, the success of OntoTKGE is contingent upon the quality of the ontology-view KG, which may pose practical challenges in domains where such structured knowledge is scarce or poorly defined. Future work could explore methods to automatically construct or refine ontologies to further enhance the framework's robustness. Additionally, the computational overhead introduced by ontological integration warrants attention, particularly in resource-constrained environments.
Recommendations
- ✓ Further research should investigate the scalability of OntoTKGE, particularly in scenarios with large-scale or dynamically evolving ontologies, to assess its feasibility for real-time applications.
- ✓ Explore techniques to automate or semi-automate the construction of ontology-view KGs to mitigate dependency on manually curated ontologies, thereby enhancing the framework's adaptability across domains.
- ✓ Conduct ethical impact assessments to evaluate potential biases in the ontology-view KG and their propagation through the model, ensuring fairness and transparency in downstream applications.
- ✓ Develop standardized benchmarks specifically tailored to evaluate the performance of ontology-enhanced TKG models, enabling more precise comparisons with existing methods.
Sources
Original: arXiv - cs.AI