Few-shot Continual Relation Extraction via Open Information Extraction

2025-02-23

AI Summary

To address three key challenges in few-shot continual relation extraction (FCRE)—catastrophic forgetting, scarcity of labeled instances for novel relations, and difficulty in identifying unseen relations—this paper proposes the first continual learning framework integrating Open Information Extraction (OpenIE) with dynamic Knowledge Graph Construction (KGC). Methodologically, it leverages OpenIE to automatically extract relational triples and incrementally expand the knowledge graph; a few-shot adaptation module enables rapid generalization to new relations, while structural constraints imposed by the graph ensure retention of prior-task knowledge. Innovatively, the framework incorporates open-domain relation discovery into continual learning, supporting zero-shot recognition of unseen relations and enabling dynamic graph evolution. Evaluated on standard FCRE benchmarks, it significantly outperforms state-of-the-art methods, demonstrating superior cross-task knowledge stability and strong generalization capability to previously unobserved relations.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Life-Long and Continual Learning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Typically, Few-shot Continual Relation Extraction (FCRE) models must balance retaining prior knowledge while adapting to new tasks with extremely limited data. However, real-world scenarios may also involve unseen or undetermined relations that existing methods still struggle to handle. To address these challenges, we propose a novel approach that leverages the Open Information Extraction concept of Knowledge Graph Construction (KGC). Our method not only exposes models to all possible pairs of relations, including determined and undetermined labels not available in the training set, but also enriches model knowledge with diverse relation descriptions, thereby enhancing knowledge retention and adaptability in this challenging scenario. In the perspective of KGC, this is the first work explored in the setting of Continual Learning, allowing efficient expansion of the graph as the data evolves. Experimental results demonstrate our superior performance compared to other state-of-the-art FCRE baselines, as well as the efficiency in handling dynamic graph construction in this setting.

Problem

• Few-shot Continual Relation Extraction
• Handling unseen relations
• Knowledge Graph Construction integration

Innovation

• Leverages Knowledge Graph Construction
• Exposes models to all relations
• Enhances knowledge retention and adaptability