Enhanced Semi-supervised Relation Extraction Based on Label Confusion Learning and Multisource Semantic Aggregation

Abstract Knowledge Graph (KG) plays a significant role in the field of artificial intelligence. The search engines, intelligent recommendations, and various other domains such as architecture, finance, and healthcare rely on KG. KG is widely utilized to support decision-making and information retrieval. Relation Extraction(RE) is a crucial technique in constructing the KG. Traditional RE based on supervised learning demand a substantial amount of manually annotated data, leading to long processing times and high costs. The methods based on remote supervision and semi-supervised learning has the problem of label noise. To address these challenges, this paper proposes an innovative semi-supervised learning approach to model the relationships between labels and instances, and utilizes a novel label distribution to supervise model training. Additionally, by integrating entity location information and label information, the information of context representation is enriched. Extensive experiments on SemEval datasets prove that the proposed method outperforms the existing approaches.