Improving Topic Modeling with Knowledge Graph Embeddings

Topic modeling techniques have been applied in many scenarios in recent years, spanning textual content, as well as many different data sources. The existing researches in this field continuously try to improve the accuracy and coherence of the results. Some recent works propose new methods that capture the semantic relations between words into the topic modeling process, by employing vector embeddings over knowledge bases.

In our recent paper presented at the AAAI-MAKE Spring Symposium 2019, held at Stanford University, we studied how knowledge graph embeddings affect topic modeling performance on textual content. In particular, the objective of the work is to determine which aspects of knowledge graph embedding have a significant and positive impact on the accuracy of the extracted topics.

We improve the state of the art by integrating some avanced graph embedding approaches (specifically designed for knowledge graphs) within the topic extraction process.
We also studied how the knowledge base could be expanded by using dataset-specific relations between the words.
We implemented the method and we validated it with a set of experiments with 2 variations of the knowledge base, 7 embedding methods, and 2 methods for incorporation of the embeddings into the topic modeling framework, also considering different parameterizations of topic number and embedding dimensionality.
Besides the specific technical results, the work has also aims at showing the potentials of integrating statistical methods with knowledge-centric methods. The full extent of the impact of these techniques shall be explored further in the future.
The details of the work are reported in the paper, which is available online here, and in the slides, also available online (on SlideShare and here below).

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