K-BERT: Enabling Language Representation with Knowledge Graph

Abstract: Pre-trained language representation models, such as BERT, capture a general language representation from large-scale corpora, but lack domain-specific knowledge. When reading a domain text, experts make inferences with relevant knowledge. For machines to achieve this capability, we propose a knowledge-enabled language representation model (K-BERT) with knowledge graphs (KGs), in which triples are injected into the sentences as domain knowledge. However, too much knowledge incorporation may divert the sentence from its correct meaning, which is called knowledge noise (KN) issue. To overcome KN, K-BERT introduces soft-position and visible matrix to limit the impact of knowledge. K-BERT can easily inject domain knowledge into the models by equipped with a KG without pre-training by-self because it is capable of loading model parameters from the pre-trained BERT. Our investigation reveals promising results in twelve NLP tasks. Especially in domain-specific tasks (including finance, law, and medicine), K-BERT significantly outperforms BERT, which demonstrates that K-BERT is an excellent choice for solving the knowledge-driven problems that require experts.

• The paper introduces a knowledge-augmented BERT that integrates structured knowledge graphs via a soft-position embedding and visible matrix to preserve sentence semantics.
• It demonstrates significant performance gains on domain-specific tasks, including clinical NER and legal and financial analyses, compared to standard BERT.
• The approach enables rapid domain adaptation by allowing seamless switching of underlying knowledge graphs, paving the way for advanced AI applications.

K-BERT: Enabling Language Representation with Knowledge Graph

Introduction

The integration of knowledge graphs (KGs) into pre-trained language representation (LR) models has been an ongoing area of research, particularly to address the limitations of domain-specific knowledge capture. While models like BERT achieve remarkable success on open-domain tasks through extensive corpus pre-training, they exhibit deficiencies in applications necessitating domain expertise, such as financial analysis, legal document processing, and medical diagnostics. The adaptation proposed in K-BERT rectifies this deficiency by incorporating knowledge graphs directly, thus enriching textual representation with structured, domain-specific knowledge.

Model Architecture and Methodology

K-BERT enhances a traditional BERT framework by integrating a knowledge layer that injects relevant triples from KGs into the input sentences, forming a knowledge-rich sentence tree. This tree structure retains both the original text’s semantics and the additional knowledge context. The methodology hinges on two critical constructs: the soft-position embedding and the visible matrix.

• Soft-Position Embedding: This aspect of K-BERT ensures efficient preservation of the original sentence structure by allowing tokens inserted from KGs to not interfere destructively with sentence semantics. This is crucial for maintaining linguistic coherence when supplemental knowledge is interspersed.
• Visible Matrix: To mitigate "knowledge noise" (KN)—excessive and potentially irrelevant knowledge incorporation—the visible matrix determines the visibility scope for token interactions, ensuring that only pertinent information influences token representation.

K-BERT’s architecture, therefore, enables seamless alignment with pre-trained BERT models, allowing direct parameter adoption and facilitating practical deployment without the overhead of exhaustive pre-training on domain-specific corpora.

Experimental Evaluation

The efficacy of K-BERT was evaluated across twelve diverse NLP tasks, including open-domain benchmarks and targeted domain-specific applications. The experimental setup quantitatively demonstrated K-BERT’s enhanced performance over baseline BERT models:

• Domain-Specific Tasks: Significant performance improvements were noted across domain-centric tasks in finance, law, and medicine, underscoring K-BERT’s ability to harness domain knowledge effectively. For instance, utilizing a medical KG notably enhanced the F1 score in clinical named entity recognition tasks.
• Open-Domain Tasks: While the improvements were less pronounced, they confirmed the utility of K-BERT in leveraging additional semantic information to refine results in more generalized NLP tasks.

K-BERT’s integration with both language-oriented KGs (e.g., HowNet) and encyclopedic sources (e.g., CN-DBpedia) averts cumbersome pre-training, allowing focused fine-tuning on available datasets for immediate applicability.

Implications and Future Directions

The introduction of K-BERT marks a significant step towards operationalizing knowledge graph integration within LLMs, presenting clear implications for the field of AI:

• Practical Implications: K-BERT’s architecture permits rapid adaptation to new domains by simply switching or updating the underlying KG. This aligns with use cases where domain knowledge rapidly evolves, such as legal and regulatory contexts.
• Theoretical Implications: The results demonstrate the potential of structured knowledge integration to overcome inherent limitations of corpus-only training, emphasizing the need for further exploration into optimal knowledge representation and integration strategies.

Future developments could include refining K-Query processes to dynamically adjust relevance weighting for integrated triples, and extending the K-BERT methodology to other LR models such as ELMo or XLNet, further pushing the boundaries of domain-specific AI applications.

Conclusion

K-BERT presents a promising enhancement to the BERT model by incorporating knowledge graphs to improve performance on domain-specific tasks. This approach significantly contributes to bridging the gap between existing open-domain language representations and the necessity for context-rich domain understanding. The versatility and efficiency of K-BERT point to a future where AI can more adeptly navigate specialized areas requiring deep knowledge integrations.