Our client had access to a massive stream of text information from e.g. news articles and social media but no easy way to quickly organize, understand, and operate on this data short of manual effort.
We used a combination of graph theory and modern natural language understanding techniques to construct a tool capable of reading text data and mapping it to our existing knowledge graph. This mapping between natural text and the structure of a knowledge graph unlocks a set of capabilities ranging from intelligent search to smart aggregation and summarization.
Our scientists leveraged graph theory to create a custom AI powered-research assistant for our client. The tool we developed is able to derive context from a search query and provide additional related topics of interest for further research. This allows our end-users to conduct more precise and accurate analysis, save time and make better data-derived decisions.