(IEEE, 2017-04) Choudhury, Sutanay; Agarwal, Khushbu; Purohit, Sumit; Zhang, Baichuan; Pirrung, Meg; Smith, Will; Thomas, Mathew; Computer and Information Science, School of Science
The ability to construct domain specific knowledge graphs (KG) and perform question-answering or hypothesis generation is a transformative capability. Despite their value, automated construction of knowledge graphs remains an expensive technical challenge that is beyond the reach for most enterprises and academic institutions. We propose an end-toend framework for developing custom knowledge graph driven analytics for arbitrary application domains. The uniqueness of our system lies A) in its combination of curated KGs along with knowledge extracted from unstructured text, B) support for advanced trending and explanatory questions on a dynamic KG, and C) the ability to answer queries where the answer is embedded across multiple data sources.