  Topical Segmentation poses a great role in reducing search space of the
topics taught in a lecture video specially when the video metadata lacks topic
wise segmentation information. This segmentation information eases user efforts
of searching, locating and browsing a topic inside a lecture video. In this
work we propose an algorithm, that combines state-of-the art language model and
domain knowledge graph for automatically detecting different coherent topics
present inside a long lecture video. We use the language model on
speech-to-text transcription to capture the implicit meaning of the whole video
while the knowledge graph provides us the domain specific dependencies between
different concepts of that subjects. Also leveraging the domain knowledge we
can capture the way instructor binds and connects different concepts while
teaching, which helps us in achieving better segmentation accuracy. We tested
our approach on NPTEL lecture videos and holistic evaluation shows that it out
performs the other methods described in the literature.
