  Topic modeling algorithms traditionally model topics as list of weighted
terms. These topic models can be used effectively to classify texts or to
support text mining tasks such as text summarization or fact extraction. The
general procedure relies on statistical analysis of term frequencies. The focus
of this work is on the implementation of the knowledge-based topic modelling
services in a KNIME workflow. A brief description and evaluation of the
DBPedia-based enrichment approach and the comparative evaluation of enriched
topic models will be outlined based on our previous work. DBpedia-Spotlight is
used to identify entities in the input text and information from DBpedia is
used to extend these entities. We provide a workflow developed in KNIME
implementing this approach and perform a result comparison of topic modeling
supported by knowledge base information to traditional LDA. This topic modeling
approach allows semantic interpretation both by algorithms and by humans.
