Active Learning Framework for Improving Knowledge Graph Accuracy

Knowledge graphs are graph-structured data models that provide a robust scheme for representing real-world relational facts with structured triples. The structural and factual information in knowledge graphs are extensively leveraged in various downstream applications. Unfortunately, knowledge graphs often contain incorrect triples due to the automated extraction processes. Therefore, to ensure the reliability and usability of knowledge graphs, it is crucial to identify and rectify these incorrect triples. However, this remains a challenging task, as knowledge graphs are intricate structures encompassing a vast number of triples formed by diverse entities, relations, and their complex interconnections. This paper proposes an effective method to enhance knowledge graph accuracy by introducing an active learning framework. The proposed framework integrates the advantages of machine-based models and human involvement to enable efficient and reliable improvement in knowledge graph accuracy. Additionally, the proposed method includes sampling strategies that consider the relation distribution in knowledge graphs to maximize the effectiveness of the active learning framework. Extensive experimental results demonstrate the effectiveness of the proposed active learning framework and sampling strategies in improving knowledge graph accuracy. Furthermore, this paper provides an exploration of the level of human involvement and a discussion of practical approaches to improve knowledge graph accuracy in real-world scenarios.