The integration of online stores with chatbots is something that must be done so that chatbot answers are able to customize customer needs. Semantic-based product search is done to find products according to the context sought so that it will help customers find their products. The integration between online stores with intelligent chatbots and semantic-based search can significantly improve the user experience in online shopping. An intelligent chatbot assists users in finding products quickly, providing relevant product information, and addressing customer queries or concerns directly. Semantic-based search allows users to find products more accurately based on their context and preferences. The integration of online stores with intelligent chatbots and semantic-based search has great potential to improve services in the context of e-commerce. The cosine similarity method is used to give weight to each search result obtained, so that the search results obtained are more relevant to the keywords. Testing using the precission method to calculate the relevance value of the results obtained from the ontology, while testing with kappa statistics is used to calculate the value of the cosine similarity results by comparing the results obtained from the system and the results according to expert observations, it is expected that semantic-based product search is able to find products with a precision level of 98% so that customers will be satisfied.