Ontology based Recommendation System for Domain Specific Seekers
Prabha Selvaraj, Vijay Kumar Burugari, D. Sumathi, Rudra Kalyan Nayak, Ramamani Tripathy · 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · 2019
Search engines are used to get the required information from the web. On the other hand, internet will not be available everywhere to search and even the search results on the web may not be the required information. Querying is one of the basic functionalities expected from database systems. Query efficiency will be adversely affected by increasing tables. Therefore, meta search engines combine the results of different search engines and improve the effectiveness of web search because of a wide coverage of web indexed data. Then the given query would be more specific to retrieve the more relevant information. By considering those problems, a system for recommendation is proposed using semantic similarity measure that refines the input query in a more specific manner for the generation of multiple queries. Initially, this method uses multiple queries instead of a single query with the help of wordnet ontology and it will result in a query-specific search. Semantics are applied on data and the database to make it meaningful and provide solution to those problems. Relationships among entities are not limited to syntactic constraints by applying semantic connections among it so that it identifies invisible, tacit and intangible among them. It extracts those hidden relationships between unrelated entity sets and stores them in a touchable form.