Towards Knowledge Graph Refinement: Misdirected Triple Identification

In the current digital transformation scenario, Knowledge Graphs (KGs) represent an across-the-board instrument for representing knowledge in a structured form. Such tools allow to effectively enhance the performance of Artificial Intelligence models in manifold contexts, such as reasoning or information retrieval. Nevertheless, the effectiveness of KGs is often affected by the incorrect directionality of some of their edges, due in most cases to human error or the inefficiency of automatic and semi-automatic graph creation methods. This paper proposes a classification-based approach to identify misdirected triples within a KG, aiming to support and assist humans in creating graph refinement. Triples are the main component of KGs, and they model the connection between nodes with a form. Our proposal allows us to refine a KG by devising a classification-based approach for recognizing whether the subjects and objects are not compliant with the logic directionality of the corresponding predicate, meaning that they should be switched (e.g., the triple should be inverted as ). We compare traditional machine learning techniques with cutting-edge advanced methods, including pre-trained language models and large language models. Extensive experiments have been performed across several datasets, confirming the effectiveness of our proposal.