CQFaRAD: Collaborative Query-Answering Framework for a Research Article Dataspace

• Published in: International journal of information technology (Singapore. Online) 2024-03, Vol.16 (3), p.1873-1886
• Main Authors: Singh, Mrityunjay, Pandey, Shivam, Saxena, Rohaan, Chaudhary, Maheep, Lal, Niranjan
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Computer Imaging; Computer Science; Image Processing and Computer Vision; Machine Learning; Original Research; Pattern Recognition and Graphics; Software Engineering; Vision
• ISSN: 2511-2104; 2511-2112
• DOI: 10.1007/s41870-023-01518-x

Dataspace systems cope with the problem of integrating a variety of data based on its structures and semantics such as structured, semi-structured, and unstructured data, and returns the best-effort or approximate answers to their users. The existing works on query answering from a dataspace system are content-based and paid attention to return the best answers to the users without taking care of their preferences. This paper aims to consider not only the content-based information but also the users’ preferences while answering the users’ queries. Therefore, we present a framework, known as Collaborative Query-Answering Framework for a Research Article Dataspace, to answer the users’ queries in efficient manner and returns more prominent answers to the users. In this work, we present a collaborative approach that adopts the advantages of the existing content-based and users’ preferences-based approaches. To achieve the objectives, we use the Bidirectional Encoder Representations from Transformers model to represent our dataspace and users’ query. We have validated our proposed approach on the research papers dataset available on Kaggle. The experimental results show that our approach works fairly well to return relevant information to the users.