A Diffusion-Based Method for Entity Search

Entity search has become an import task in the Web of Data recently. Most solutions developed so far have focused on modelling entity search using standard information retrieval model and adapting graph-based objects to multi-fielded pseudo-documents. Among the models proposed to this regard, we can found bag-of-words, multi-gram, and mixtures of language models. While these works have produced interesting findings, little attention has been put on the graph structure of the Web of data. In this work, we aim to fill this gap by introducing a two-stage method based on a standard information retrieval model combined with a diffusion-based approach. We implemented and tested several diffusion models finding that heat kernel diffusion processes have a competitive performance with state-of-the-art models.