关键词: 知识融合, 机器学习, 概念漂移, 反向验证, 大数据

Abstract: Knowledge fusion is one of the fundamental factors of the knowledge graph technology. However, traditional machine learning（ML） based approaches adapted to the knowledge fusions may show limited accuracy and instantaneity in heterogeneous big data environment. Hence, the paper proposes a reliable and low-complexity knowledge fusion method which integrates a concept drifting detection algorithm and a reverse verification algorithm. Firstly, an entity matching and an attribute fusion are performed by Bayesian estimation. Meanwhile, an iForset based concept drifting detection is performed on historical data samples to enhance the reliability of data model. On other hand, a self-organizing map （SOM） based unsupervised reversing verification is periodically followed to disambiguate the entities in heterogeneous databases. Based on these, the shortcomings of the supervised ML and unsupervised ML, which are the dependence on the historical data and the high computational complexity, respectively, are overcome. The proposed method has been tested on an open dataset and a knowledge graph system developed by the state grid Anhui electric power research institute. The knowledge fusion performances in terms of the reliability of data model, the entity matching ability, the F1 scores and the running time are compared and the practicability of the proposed method is analyzed and validated by experimental result in heterogeneous big data environment.

Key words: knowledge fusion, machine learning, concept drifting, reversing verification, big data