Abstract
Multimedia content is increasing immensely as there are various websites available to upload images. Image retrieval is a method of searching for, viewing, and retrieving images from a database. Close text choices are often accepted by image search engines. It is difficult for search engines to comprehend users’ search intent merely through terms, resulting in unclear and quavering search results that are far from satisfying. To solve the dilemma in text-based picture retrieval, it is important to employ content-first search. The proposed OntoKnowNHS model is composed of Domain Ontology based query term enrichment and Knowledge enrichment of the images with the help of Google’s Knowledge Base API and Wikidata, incorporated with the Knowledge Graph of images which is compared using Convolutional Neural Networks and, the semantic similarity is computed using Kullback Leibler Divergence, Concept Similarity, and Normalised Compression Distance which recommends images from both Knowledge Graphs and Redefined Image Tag set. All of these factors work together to improve accuracy. The Flickr30k dataset is used to integrate user preferences for picture suggestions, which are then categorized using Convolutional Neural Networks with the aid of extracted query terms from the user information using domain ontology. The developed approach has an accuracy of 96.41% and outperforms the alternative reference models by weakening the robustness of conventional image Recommender Systems.
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Roopak, N., Deepak, G. (2021). OntoKnowNHS: Ontology Driven Knowledge Centric Novel Hybridised Semantic Scheme for Image Recommendation Using Knowledge Graph. In: Villazón-Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S., Goyal, A., Jabbar, M. (eds) Knowledge Graphs and Semantic Web. KGSWC 2021. Communications in Computer and Information Science, vol 1459. Springer, Cham. https://doi.org/10.1007/978-3-030-91305-2_11
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