Mandolin: A Knowledge Discovery Framework for the Web of Data
This addresses the problem of fragmented approaches in knowledge discovery for Semantic Web data, though it is incremental as it builds on existing techniques.
The paper tackles the lack of a comprehensive workflow for knowledge discovery on RDF datasets by introducing Mandolin, a framework that integrates rule mining, grounding, and inference, showing it scales well and achieves at least comparable results to other algorithms on link prediction.
Markov Logic Networks join probabilistic modeling with first-order logic and have been shown to integrate well with the Semantic Web foundations. While several approaches have been devised to tackle the subproblems of rule mining, grounding, and inference, no comprehensive workflow has been proposed so far. In this paper, we fill this gap by introducing a framework called Mandolin, which implements a workflow for knowledge discovery specifically on RDF datasets. Our framework imports knowledge from referenced graphs, creates similarity relationships among similar literals, and relies on state-of-the-art techniques for rule mining, grounding, and inference computation. We show that our best configuration scales well and achieves at least comparable results with respect to other statistical-relational-learning algorithms on link prediction.