Abstract

Iris.ai is tackling the problem of an exponentially expanding corpus of scientific knowledge, and limited human ability to manually sort, navigate and review it. Old school solutions are based on limiting key words and human-made taxonomies, revolving around a biased citation system and with results presented in endless and unstructured lists. The Iris.ai technology, using key concept extraction, contextual synonym enrichment, neural topic modeling and word importance-based document similarity, allows us to build intuitively meaningful content-based indexes. These indexes and their corresponding content are turned into intuitive, helpful visualizations for improved overview and navigation. This poses a novel and more efficient way of performing systematic research landscape mappings, reducing manual labor by 78%.

Bio

Victor Botev is the CTO and Co-Founder of Iris.ai, preciously a researcher from Chalmers University of Technology. He studied individual Master’s degrees in Artificial Intelligence and Computer Systems and Networks at Sofia University St. Kliment Ohridski and Chalmers, respectively. After his degrees he stayed at Chalmers, where he conducted research on clustering and predictive neural network models and the usage of signal processing techniques in studying Big Data. Victor has put his unique combination of AI research, software development lead and ambitious vision to the ultimate test at Iris.ai.