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

Abstract

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 in a dataspace system are contentbased 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 Collaborative Query-Answering Framework for a Research Article Dataspace (CQFaRAD) that helps to efficiently answer the users' queries and returns more prominent answers to them. In this work, we present a collaborative approach that adopts the advantages of existing content-based and users' preferences-based approaches. To achieve this task, we use the BERT 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.