Topic Modeling for RDF Graphs

Topic models are widely used to thematically describe a collection of text documents and have become an important technique for systems that measure document similarity for classification, clustering, segmentation, entity linking and more. While they have been applied to some non-text domains, their use for semi-structured graph data, such as RDF, has been less explored. We present a framework for applying topic modeling to RDF graph data and describe how it can be used in a number of linked data tasks. Since topic modeling builds abstract topics using the co-occurrence of document terms, sparse documents can be problematic, presenting challenges for RDF data. We outline techniques to overcome this problem and the results of experiments in using them. Finally, we show preliminary results of using Latent Dirichlet Allocation generative topic modeling for several linked data use cases.