Entity resolution for media metadata based on structural clustering

An increasing amount of media metadata are published by different organizations on the Web which leads to a fragmented dataset landscape. Identifying media metadata from disparate datasets and integrating heterogeneous datasets have many applications but also pose significant challenges. To tackle this problem, entity resolution methods are commonly used as an essential prerequisite for integrating media information from different sources and effectively foster the re-use of existing data sources. While the amount of media metadata published on the Web grows steadily, how to scale it well to large media knowledge bases while maintaining a high matching quality is a critical challenge. This article investigates the relationships between media entities. To that end, the media database is formulated as a knowledge graph with entities as nodes and the associations between related entities as edges. Thus, media entities are grouped into communities by how they share neighbors. Then, a structural clustering-based model is proposed to detect communities and discover anchor vertices as well as isolated vertices. Specifically, an initial seed set of matched anchor vertex pairs is obtained. Furthermore, an iterative propagation approach for identifying the matched entities in the whole graph is developed, where community similarity is introduced into the measure function to control the total measurement of candidate pairs. Therefore, starting with the elements of the initial seed set, the entity resolution algorithm updates the matching information over the whole network along with the neighbor relationships iteratively. Extensive experiments are conducted on real datasets to evaluate how the seed set impacts the matching process and performance. The experiment results show this model can achieve an excellent balance between accuracy and efficiency and is a clear improvement compared to state-of-the-art methods.