The are several knowledge graphs in form of hierarchies of categories present on the world wide web. Most of them belong to information sharing websites. These include product category hierarchies like those of Amazon, or task hierarchies like that of Wikihow. These hierarchies are very rich in information and contains several insights to how certain categories should be arranged. Internet is ever growing and categories of products, tasks etc. are increasing rapidly. However, these additions over time are accounted for, manually in these knowledge bases. It is a very tedious process to handle these manual changes due to the massive information they hold and the strict guidelines one has to follow.
We propose a novel concept which aims towards enriching such knowledge bases based on the prior information they contain. Previous works include automatic hierarchy generation based on the semantics. However, we aim at inferring knowledge from the existing hierarchy by observing the clustering strategy used by human experts and extrapolating that strategy to new incoming categories. This way we ensure automatic enrichment of hierarchy with the information flow.