Bigdata Analysis Of Digital Scientific Literature: A Futuristic Platform For Collaborative Knowledge Extraction Enabled With Artificial Intelligence
Numerous techniques including information extraction, document classification, document clustering and information visualization have beendeveloped to support the understanding of information embedded within scientific articles and to speed up research and development activities including drug design. However, the embedded knowledge is too complex to extract by using simple pattern matching techniques and most of these methods do not help users directly understand key concepts and their semantic relationships in document corpora, which are critical for capturing their conceptual structures. The problem arises due to the fact that most of the information is embedded within unstructured or semi-structured texts that computers cannot interpret very easily.
Here we present a novel informatics platform developed by HubScience, addressing the current challenges in order to enable users to extract knowledge from large amount of digital information, and beyond; finding specific responses for specific scientific questions, building a topic specific artificial intelligence on a collaborative platform, combining artificial intelligence algorithms and easily extracting the relevant data for further data analysis.
Keywords: Big Data Analysis, Text mining, Artificial Intelligence, BioMed literature, Pharmaceutical literature, Natural Sciences