Knowledge Graph of Mergers and Acquisitions

Context driven decision making is a key factor to make critical decisions in business applications. We present the design and application of a knowledge graph to aid the context driven decision making for studying the patterns in Mergers and Acquisitions (M&A) activities in the industry. Using text data from news articles we make use of a Natural Language Processing pipeline to extract entities and relations to build a knowledge graph. The entity recognition model was 90.97% accurate in detecting the entities. The graph is further enriched with metadata from Wikipedia. We finally demonstrate two use cases to showcase the utility of the knowledge graph for making business decisions.