Linked Open Data for ACademia (LODAC) together with National Museum of Nature and Science have st...
Linked Open Data for ACademia (LODAC) together with National Museum of Nature and Science have started collecting linked data of interspecies interaction and making link prediction for future observations. The initial data is very sparse and disconnected, making it very difficult to predict potential missing links using collaborative filtering alone. In this paper, we introduce Link Prediction on Interspecies Interaction (LPII) to solve this situation using hybrid recommendation approach. Our prediction model is a combination of three scoring functions, and takes into account collaborative filtering, community structure, and biological classification. We have found our approach, LPII, to be more accurate than other combinations of perdition models. Using statistical significance testing, we demonstrate that these scoring functions are important and play different roles depending on the conditions of linked data. This shows that LPII can be applied to deal with other real-world situations of link prediction.