Mining Heterogeneous Data for Formulation Design

Formulated products such as cosmetics, personal care, pharmaceutical products and industrial products such as paints and coatings are a multi-billion dollar industry. Experts carry out designing of new formulations in most of these industries based on their knowledge and basic search from online and offline resources. Reference data for formulation design comes in several formats and from multiple sources with diverse representation. We present an approach to mine the heterogeneous data for formulation design with case studies of cosmetics and steel coating industries. Our contribution is threefold. First, we show data extraction and mining techniques from multi-source and multi-modal text data. Second, we describe how we store and retrieve the data in graph databases. Lastly, we demonstrate the use of extracted and stored data for a simple recommendation system based on data search techniques that aid the experts for the synthesis of new formulation design.