A New Multiple Label Propagation Algorithm for Linked Open Data

Abstract

In recent years, Linked Open Data (LOD) has been attracting attention in the world since the LOD have features such as semantic web and ontology. Also, applications using the LOD have been increasing year after year in Japan and in all over the world. However, there are a problem that most of existing LOD are not linked with other LOD. Therefore, original features of the LOD have been lost. In this study, we propose a new Multiple Label Propagation algorithm for linking each other the existing LOD to bring out the original features by semi-supervised learning. This algorithm predicts semantic labels of individual data of the LOD using an assumption that adjacent nodes belong to a same or similar class. Therefore, the algorithm has new formula to consider semantic distance. Thereby, the algorithm is possible to extend links between neighborhood nodes (data). In addition, the algorithm is possible to correspond multi-label data. The results of experiments showed that the algorithm can give the label prediction values representing semantic distances, and can be processed much faster than a conventional algorithm.