Topic Modeling for RDF Graphs
Monday, September 21, 2015, 10:30am - Monday, September 21, 2015, 11:30am
ITE 346
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.