Efficient Retrieval of Data Using Semantic Search Engine Based on NLP and RDF

Abstract

With the evolution of Web 3.0, the traditional algorithm of searching Web 2.0 would become obsolete and underperform in retrieving the precise and accurate information from the growing semantic web. It is very reasonable to presume that common users might not possess any understanding of the ontology used in the knowledge base or SPARQL query. Therefore, providing easy access of this enormous knowledge base to all level of users is challenging. The ability for all level of users to effortlessly formulate structure query such as SPARQL is very diverse. In this paper, semantic web based search methodology is proposed which converts user query in natural language into SPARQL query, which could be directed to domain ontology based knowledge base. Each query word is further mapped to the relevant concept or relations in ontology. Score is assigned to each mapping to find out the best possible mapping for the query generation. Mapping with highest score are taken into consideration along with interrogative or other function to finally formulate the user query into SPARQL query. If there is no search result retrieved from the knowledge base, then instead of returning null to the user, the query is further directed to the Web 3.0. The top “k” documents are considered to further converting them into RDF format using Text2Onto tool and the corpus of semantically structured web documents is build. Alongside, semantic crawl agent is used to get <Subject-Predicate-Object> set from the semantic wiki. The Term Frequency Matrix and Co-occurrence Matrix are applied on the corpus following by singular Value decomposition (SVD) to find the results relevant for the user query. The result evaluations proved that the proposed system is efficient in terms of execution time, precision, recall and f-measures.

KMSBOT: Enhancing Educational Institutions with an AI-Powered Semantic Search Engine and Graph Database

“…The data undergo classification through an ontology-based method, then evaluating the model's performance in precision, recall, f1-score, and latency time. The analysis indicates that the proposed model outperforms existing ones [12]. This comprehensive review examines the use of affective computing and recommender systems in educational contexts, shedding light on integrating emotional and cognitive aspects into recommendation processes.…”