This disclosure relates to systems and methods for improved knowledge mining. In one embodiment, a method is disclosed, which comprises filtering aggregated data encoded according to multiple data formats, using a combination of sliding-window and boundary-based filtration techniques. Machine learning and natural language processing are applied to the filtered data to generate a business ontology. Also, using a prediction analysis, one or more recommended classification techniques are automatically identified. The filtered data is clustered into an automatically determined number of categories based on the automatically recommended one or more classification techniques. The one or more classification techniques may utilize iterative feedback between a supervised learning technique and an unsupervised learning technique. Furthermore, the method includes generating automatically correlations between the business ontology and the automatically determined number of categories, and generating a knowledge base using the correlations between the business ontology and the automatically determined number of categories.