The ConceptNet dataset is a semantic network built from nodes representing words or short phrases of natural language, with relationships between them. It aims to enable systems to take human words as input and use them to better search for information, answer questions, and understand user goals. The concept can be applied in various domains such as retail, social startups, and content platforms, where tag-based recommendations and categorizations are used. By translating the ConceptNet5 API into a graph database, it takes away the agony of tag-based recommendations and provides insights on how to categorize products effectively. The dataset is incredibly rich, providing various "senses" in which someone might mean a word and offers a wide variety of relationship types to choose from.