Open Access
Research Article
An ontological architecture for context data retrieval and ranking using SVM and DNN
*Pooja MudgilCorresponding authorengineer.pooja90@gmail.comDepartment of Computer Science Banasthali UniversityRajasthan, IndiaView full profile → , Pooja Guptapoojaguptamait@gmail.comDepartment of Computer Science and Technology Maharaja Agrasen Institute of TechnologyDelhi, IndiaView full profile → , Iti Mathurmathur_iti@rediffmail.comDepartment of Computer Science Banasthali UniversityRajasthan, IndiaView full profile → , Nisheeth Joshijnisheeth@banasthali.inDepartment of Computer Science Banasthali UniversityDepartment of Computer Science Banasthali VidyapithJaipur, Rajasthan, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Published Online:
- 01 Apr 2023
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1347
- Pages:
- 369–382
Abstract
Context retrieval and ranking have always been an area of interest for researchers around the world. The ranking provides significance to the data that has to be presented in front of users but it also consumes time if the ranking architecture is not organized. The retrieval is dependent upon the co-relation among the data attributes that are supplied against a class label also referred to as ground truth and the ranking depends upon the sensing polarity that indicates the hold of the outcome towards asked information. This paper illustrates an ontological architecture that involves two phases namely context retrieval and ranking. The ranking phase is composed of three different algorithm architectures namely k-means, Support Vector Machines (SVM), and Deep Neural Networks (DNN). The DNN is tuned to fit and work as per the availability of a total number of samples. The proposed work has been evaluated for both quantitative and qualitative parameters in different sets and scenarios. The proposed work has also been compared with other state of art techniques and is illustrated in the paper itself.
Keywords
Subject Classifications
68T07
References
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