**3. Improving product recommendations using semantic sentiments**

The recommender system proposed in this work is a knowledge-based recommender system that encapsulates the product catalog knowledge in the form of classes in the ontology and product functional knowledge in the form of facts in the ontology. The user profile is created as and when the user navigates the web pages for the products. The user profile is indexed with the product information from the ontology.

The principal objective of recommending products using sentiments learned from the ontology is to utilize the taxonomical and non-taxonomical constraints mined from ontology for sentiments. The detailed procedures expressed in algorithmic form for learning taxonomical constraints and non-taxonomical constraints are presented below.
