Link prediction of LOD by multiple label propagation algorithm considering semantic distance
Toshitaka Maki, Kazuki Takahashi, Akihiro Yamaguchi, Toshihiko Wakahara, Toru Kobayashi, Akihisa Kodate, Noboru Sonehara · 2016
This paper presents link prediction of Linked Open Data (LOD) by Multiple Label Propagation Algorithm (MLPA). The current LOD do not have enough links. Therefore, the LOD have not been able to exert so much semantic characteristics. In order to solve this problem, we proposed the MLPA considering semantic distance. The MLPA can expand potential links of each data contained in the LOD. The experimental result of the MLPA shows the good performance and the validity of this algorithm is confirmed.