| 摘要 |
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. |
| 主权项 |
1. A processor-implemented automated knowledge mining method, comprising:
aggregating, via one or more hardware processors, data encoded according to a plurality of data formats; filtering, via the one or more hardware processors, the aggregated data using a combination of sliding-window and boundary-based filtration techniques to obtain filtered data; applying, via the one or more hardware processors, machine learning and natural language processing to the filtered data to generate a business ontology; identifying automatically, via the one or more hardware processors, using a prediction analysis, one or more recommended classification techniques to apply to the filtered data; clustering, via the one or more hardware processors, the filtered data into an automatically determined number of categories based on the automatically recommended one or more classification techniques; wherein the one or more classification techniques utilize iterative feedback between a supervised learning technique and an unsupervised learning technique; generating automatically, via the one or more hardware processors, correlations between the business ontology and the automatically determined number of categories; and generating, via the one or more hardware processors, a knowledge base using the correlations between the business ontology and the automatically determined number of categories. |
