RESUMEN
Knowledge verification is an important task in the quality management of knowledge graphs (KGs). Knowledge is a summary of facts and events based on human cognition and experience. Due to the nature of knowledge, most knowledge quality (KQ) management methods are designed by human experts or the characteristics of existing knowledge, which may be limited by human cognition and the quality of existing knowledge. Numerical data contain a wealth of potential information that may be helpful in verifying knowledge, which is rarely explored. However, due to the implicit representation of numerical data to facts as well as the noise in the data, it is challenging to use data to verify the knowledge. Therefore, this article proposes a knowledge verification method, which discovers the correlation and causality from numerical data to validate knowledge and then evaluate the quality of knowledge. Moreover, to address the impact of noise, the method integrates multisource knowledge to jointly evaluate the KQ. Specifically, an iterative update method is designed to update KQ by utilizing the consistency between multisource knowledge while designing knowledge verification factors based on data causality and correlation to manage update process. The method is validated with multiple datasets, and the results demonstrate that the proposed method could evaluate KQ more accurately and has strong robustness to noise in the data.
RESUMEN
A new research idea may be inspired by the connections of keywords. Link prediction discovers potential nonexisting links in an existing graph and has been applied in many applications. This article explores a method of discovering new research ideas based on link prediction, which predicts the possible connections of different keywords by analyzing the topological structure of the keyword graph. The patterns of links between keywords may be diversified due to different domains and different habits of authors. Therefore, it is often difficult for a single learner to extract diverse patterns of different research domains. To address this issue, groups of learners are organized with negative correlation to encourage the diversity of sublearners. Moreover, a hierarchical negative correlation mechanism is proposed to extract subgraph features in different order subgraphs, which improves the diversity by explicitly supervising the negative correlation on each layer of sublearners. Experiments are conducted to illustrate the effectiveness of the proposed model to discover new research ideas. Under the premise of ensuring the performance of the model, the proposed method consumes less time and computational cost compared with other ensemble methods.
