Ontology Based Restaurant Recommendation Approach

As the world moves forward, the restaurant industry is rapidly expanding. Customers may never physically evaluate a restaurant based on its services until that customer has practical experience with it. A better recommendation mechanism can always direct the customer to the correct location, resulting in a positive outcome. The paper discusses an approach of a context rich chatbot that can identify the customer’s mode of thinking using a restaurant ontology that suggests relevant restaurants and foods. Most importantly the defined methodology will use a hybrid version of knowledge bases along with a two-way bind to the primary knowledge base. The chatbot will proceed to find relationships in the ontology by tracing concept definitions and properties while feeding information from the database. The main components related to the proposed system are Natural Language Understanding (NLU) pipeline, dialog management module, action server, knowledge query module, and data repository (MongoDB). This mechanism was evaluated through information retrieval measures.