AR Search Engine: Semantic Information Retrieval for Augmented Reality Domain

With the emergence of the metaverse, the popularity of augmented reality (AR) is increasing; accessing concise, accurate, and precise information in this field is becoming challenging on the world wide web. In regard to accessing the right information through search engines, semantic information retrieval via a semantic analysis delivers more relevant information pertaining to the user’s query. However, there is insufficient research on developing semantic information retrieval methods in the AR domain that ranks and clusters AR-based search results in a fair fashion. This paper develops an AR search engine that automatically organizes, understands, searches, and summarizes web documents to enhance the relevancy scores in AR domains. The engine enables users to organize and manage relevant AR documents in various AR concepts and efficiently discover more accurate results in terms of relevancy in the AR field. First, we propose an AR ontology for clustering AR documents into AR topics and concepts. Second, we developed an ontology-based clustering method using the k-means clustering algorithm, vector space model, and term frequency-inverse document frequency (TF-IDF) weighting model with ontology to explore and cluster the AR documents. Third, an experiment was designed to evaluate the proposed AR search engine and compare it with the custom search engine in the AR domains. The results showed that the AR search engine accessed the right information about 42.33% faster and with a 34% better ranking.