Towards logical association rule mining on ontology-based semantic trajectories

Abstract

Mobility patterns have been investigated from multiple point-of-views, and different trajectory data mining paradigms have been proposed in the literature. Recent Semantic Trajectory representations rely on ontologies or RDF triples to represent different semantics, interlink data, and retrieve information. Nonetheless, current Trajectory Data Mining approaches use traditional transaction-based techniques that are not compatible with the relational nature of ontology-based representations. In this paper, we tackle the task of association rules mining by borrowing from the Knowledge Base Refinement field, the state-of-the-art AMIE 3 algorithm. After mining logical rules from a Foursquare dataset, we discuss the issues of applying off-the-shelf mining algorithms and discuss opportunities to develop a domain-tailored approach.