Toward Space-Efficient Semantic Querying with Graph Databases

Manually surveying vast databases consumes a lot of precious time and human resources. The aim of this proposed solution is to identify potential business opportunities for a client, against their given products and features which are stored in a knowledge graph by scraping text from the target websites provided by the client. We have considered a chemical entities data source for demonstration. The automation of this process of identifying business opportunities can significantly reduce the resources used. This organization of rich data in a graph format enables efficient querying of compound data based on specific properties or material classes. As an essential contribution, this proposed solution gives a graph format of databases which help carry out semantic search operations that give useful results from the available input corpus.