Triple confidence measurement in knowledge graph with multiple heterogeneous evidences

Knowledge graph (KG) is a representative technique of knowledge engineering, and it is often used in various intelligence applications, which assume that all triples in knowledge graphs (KGs) are correct. However, due to the noise brought by automatic KG construction techniques and the fuzziness of knowledge in specific fields, measuring uncertainty of KGs (i.e., the confidence of each triple being true) is important to the tasks of error detection and fact verification. Existing studies on triple confidence measurement either only relies on explicit evidences or merely depends on embedding evidences, which causes the resulting confidences are not precise enough. To solve this problem, in this paper, we propose a new triple confidence measurement (TCM) method, which combines multiple heterogeneous evidences including explicit evidences (i.e., concept paths and neighbor concept subgraphs) and different embedding evidences acquired by large language model, KG embedding models, contrastive learning, and graph convolutional network. Experiments on different real-world datasets demonstrate not only the superiority of TCM in the tasks of error detection and link prediction, but also the effectiveness of all proposed explicit evidences and embedding evidences.