Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks (1903.01306v1)

Abstract: We propose a distance supervised relation extraction approach for long-tailed, imbalanced data which is prevalent in real-world settings. Here, the challenge is to learn accurate "few-shot" models for classes existing at the tail of the class distribution, for which little data is available. Inspired by the rich semantic correlations between classes at the long tail and those at the head, we take advantage of the knowledge from data-rich classes at the head of the distribution to boost the performance of the data-poor classes at the tail. First, we propose to leverage implicit relational knowledge among class labels from knowledge graph embeddings and learn explicit relational knowledge using graph convolution networks. Second, we integrate that relational knowledge into relation extraction model by coarse-to-fine knowledge-aware attention mechanism. We demonstrate our results for a large-scale benchmark dataset which show that our approach significantly outperforms other baselines, especially for long-tail relations.

• The paper proposes a novel distance supervised method combining knowledge graph embeddings and graph convolution networks to improve relation extraction for long-tail, rare relations in imbalanced datasets.
• The method leverages a hierarchy of class labels and a coarse-to-fine knowledge-aware attention mechanism to transfer relational information from frequent to infrequent classes.
• Experiments on the NYT dataset demonstrate that the proposed approach significantly outperforms baseline models, particularly enhancing performance on underrepresented long-tail relation types.

Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks

The paper "Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks" presents a novel methodology to address the challenge of relation extraction (RE) in the context of long-tailed, imbalanced datasets, typical in many real-world scenarios. The authors focus on a distance supervised RE approach, leveraging knowledge graph (KG) embeddings in conjunction with graph convolution networks (GCNs) to enhance the extraction process for rarely occurring, "few-shot" relations.

In relation extraction tasks, the objective is to identify and classify the semantic relationships between entities within a text. Conventional supervised models require substantial labeled data, which is often unavailable for many rare relation types—referred to as the "long tail" of the relation distribution. This paper proposes a strategy to enhance the performance of such underrepresented relation types by borrowing knowledge from the more frequent "head" classes.

The authors introduce a two-fold approach: leveraging implicit relational knowledge through KG embeddings and explicit knowledge via GCNs. This explicit knowledge retrieval is structured and processed by employing a hierarchy of class labels, allowing for the transfer of relational information from frequent to infrequent classes through a coarse-to-fine knowledge-aware attention mechanism.

The paper demonstrates significant improvements against baseline models through comprehensive experiments on a benchmark dataset, specifically focusing on both common and long-tail relations. Evaluations using the NYT dataset show that the proposed method outperforms traditional approaches, particularly in the domain of long-tail relation extraction where data is scarce.

The implications of this research are manifold. Practically, the methodology provides a pathway to enhance the capabilities of RE systems in handling realistic, imbalanced data scenarios. Theoretically, it underscores the potential of integrating relational knowledge through graph-based architectures, pushing the frontier on how semantic similarity and structure in data can be harnessed effectively.

Looking forward, the approach offers promising avenues for further research, potentially expanding into zero-shot relation extraction and adapting to other NLP tasks beyond RE, such as text classification and logical reasoning problems. Future work could explore integrating additional layers of knowledge in the embedding and convolution processes, addressing the sparsity of hierarchical information for enhanced learning outcomes. This research marks a step toward more robust and versatile information extraction models that can operate effectively in the often unpredictable data landscapes of practical applications.