Type Prediction Combining Linked Open Data and Social Media

Linked Open Data (LOD) and social media often contain the representations of the same real-world entities, such as persons and organizations. These representations are increasingly interlinked, making it possible to combine and leverage both LOD and social media data in prediction problems, complementing their relative strengths: while LOD knowledge is highly structured but also scarce and obsolete for some entities, social media data provide real-time updates and increased coverage, albeit being mostly unstructured. In this paper, we investigate the feasibility of using social media data to perform type prediction for entities in a LOD knowledge graph. We discuss how to gather training data for such a task, and how to build an efficient domain-independent vector representation of entities based on social media data. Our experiments on several type prediction tasks using DBpedia and Twitter data show the effectiveness of this representation, both alone and combined with knowledge graph-based features, suggesting its potential for ontology population.