Multi-source relational data fusion

Abstract

Focusing on the problem of relational data fusion in the environment with “information isolated island”, this paper presents a multi-sources relational data fusion (MSF) framework. The framework consists of three components: schema matching, entity alignment, and entity fusion. Based on the Hungarian algorithm, we propose an alignment discovery mechanism for the attributes alignment among multi-sources relational data. By extracting the multi-dimensional features of attribute values, we efficiently realized schema matching of multi-sources relational data. To link the tuple pairs from multi-source data, we introduced the diversity sampling strategy and the entity feature extraction approach. These can effectively improve the performance of entity alignment. Finally, linked entities are fused to provide a unified view of data analysis. To verify the usefulness and efficiency of the proposed methods, we implemented a fusion system called Data Puzzle, which is verified with the real public multi-field data. Experimental results demonstrate that the proposed methods can fuse multi-source relational data efficiently with high recall and precision.