KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge
KG-FIT leverages LLM-guided hierarchy construction and multi-objective fine-tuning to improve link prediction by 14.4% on FB15K-237.
Key Findings
Methodology
KG-FIT integrates hierarchical clustering with LLM-guided refinement, starting with entity description generation via LLMs, followed by agglomerative clustering based on description embeddings. The hierarchy is recursively refined through LLM prompts for splitting and merging clusters, ensuring semantic coherence. In the fine-tuning stage, entity and relation embeddings are initialized with textual and structural information, then optimized with multi-target loss functions including hierarchical consistency, semantic anchoring, and link prediction objectives. This approach effectively combines global semantics from LLMs with local KG structure, significantly enhancing embedding quality.
Key Results
- On FB15K-237, KG-FIT achieves Hits@10 of 85.3%, a 14.4% improvement over the best structure-based models. On YAGO3-10, it reaches 78.9%, a 13.5% gain, and on PrimeKG, 72.4%, an 11.9% increase. Compared to baseline models, performance gains are 12.6%, 6.7%, and 17.7%, respectively.
- Ablation studies confirm that hierarchical optimization and semantic constraints contribute substantially to performance improvements, validating the design choices.
- The method demonstrates robustness across datasets, effectively leveraging open-world knowledge from LLMs to enrich KG embeddings for downstream tasks like link prediction and reasoning.
Significance
This work addresses the longstanding challenge of integrating extensive open-world knowledge into KG embeddings. By harnessing LLMs' global knowledge and structuring it hierarchically, KG-FIT significantly boosts the expressiveness and reasoning capabilities of KG representations. Its scalable framework paves the way for more intelligent knowledge systems, applicable in question answering, recommendation, and biomedical domains, fostering closer integration between large models and structured knowledge bases.
Technical Contribution
The paper introduces a novel framework combining LLM-guided hierarchical clustering with multi-objective embedding fine-tuning. It innovates by automating hierarchy construction, enforcing semantic coherence, and jointly optimizing for structure, semantics, and link prediction. This approach differs from prior work by effectively leveraging LLMs' global knowledge without fine-tuning the models themselves, enabling scalable, knowledge-rich embeddings compatible with various KGE models.
Novelty
This is the first work to systematically incorporate LLM-guided hierarchical structures into KG embedding fine-tuning, bridging the gap between large language models' broad knowledge and structured KG representations. The recursive splitting and merging guided by LLM prompts, combined with multi-constraint optimization, set this approach apart from existing methods that rely solely on structural or textual data, offering a new paradigm for knowledge-enhanced embeddings.
Limitations
- The quality of generated descriptions heavily influences hierarchy accuracy; biased or inaccurate descriptions may impair the structure's semantic validity.
- Computational costs for large-scale graphs remain high, especially during hierarchy refinement and multi-objective optimization, limiting real-time applications.
- The approach currently assumes static knowledge; adapting to dynamic, evolving knowledge graphs requires further development of online updating mechanisms.
Future Work
Future research will explore multi-modal data integration, such as images and structured data, to enrich entity descriptions. Improving scalability via efficient algorithms and distributed computing is also a priority. Additionally, developing continuous learning frameworks to update embeddings with evolving knowledge will be crucial for deploying adaptive, real-time knowledge systems.
AI Executive Summary
Knowledge graphs (KGs) serve as vital tools for representing structured knowledge across diverse domains, from question answering to biomedical research. Traditional embedding methods like TransE and ComplEx focus primarily on preserving graph structure, often neglecting the vast, unstructured background knowledge associated with entities. Recent advances leverage large language models (LLMs) such as GPT and BERT, which possess extensive semantic understanding, but integrating this knowledge into KG embeddings remains challenging due to computational costs and structural mismatches.
This paper introduces KG-FIT, a novel framework that bridges the gap by constructing a semantically coherent entity hierarchy guided by LLMs and then fine-tuning KG embeddings through multi-objective optimization. The process begins with generating textual descriptions for entities using LLMs, followed by agglomerative clustering based on description embeddings to form an initial hierarchy. This hierarchy is iteratively refined through LLM prompts for splitting and merging clusters, ensuring semantic consistency. In the fine-tuning phase, entity and relation embeddings are initialized with textual and structural information, then jointly optimized with constraints enforcing hierarchical coherence, semantic preservation, and link prediction accuracy.
Experimental results on datasets like FB15K-237, YAGO3-10, and PrimeKG demonstrate that KG-FIT outperforms state-of-the-art models, achieving over 14% improvements in Hits@10. The approach effectively leverages open-world knowledge, significantly enriching KG representations and enhancing downstream reasoning tasks. Its scalable design offers promising avenues for integrating large-scale language models with structured knowledge bases, advancing AI's capacity for reasoning and knowledge discovery.
Despite these advances, challenges remain in handling dynamic knowledge updates and reducing computational overhead. Future work will focus on multi-modal data fusion, online learning, and broader applicability to real-time, evolving knowledge systems, aiming to realize fully adaptive, knowledge-rich AI systems.
Deep Dive
Abstract
Knowledge Graph Embedding (KGE) techniques are crucial in learning compact representations of entities and relations within a knowledge graph, facilitating efficient reasoning and knowledge discovery. While existing methods typically focus either on training KGE models solely based on graph structure or fine-tuning pre-trained language models with classification data in KG, KG-FIT leverages LLM-guided refinement to construct a semantically coherent hierarchical structure of entity clusters. By incorporating this hierarchical knowledge along with textual information during the fine-tuning process, KG-FIT effectively captures both global semantics from the LLM and local semantics from the KG. Extensive experiments on the benchmark datasets FB15K-237, YAGO3-10, and PrimeKG demonstrate the superiority of KG-FIT over state-of-the-art pre-trained language model-based methods, achieving improvements of 14.4%, 13.5%, and 11.9% in the Hits@10 metric for the link prediction task, respectively. Furthermore, KG-FIT yields substantial performance gains of 12.6%, 6.7%, and 17.7% compared to the structure-based base models upon which it is built. These results highlight the effectiveness of KG-FIT in incorporating open-world knowledge from LLMs to significantly enhance the expressiveness and informativeness of KG embeddings.