The Power and Potentials of Flexible Query Answering Systems: a Critical and Comprehensive Analysis

The paper presents a critical and comprehensive analysis of recent developments, trends and challenges of Flexible Query Answering Systems (FQASs). Flexible query answering is a multidisciplinary research field at the crossroad of several disciplines among which Information Retrieval (IR), databases, knowledge based systems, Natural Language Processing (NLP) and the semantic web, which aims to provide powerful means and techniques for better reflecting human preferences and intentions to retrieve relevant information.
The analysis follows the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines characterized by a top-down process, starting with relevant keywords for the topic of interest to retrieve relevant articles from meta-sources, and complementing them with relevant articles from seed sources identified by a bottom-up process. To mine the retrieved publication data a network analysis is performed which allows to present in a synthetic way intrinsic topics of publications by revealing aspects of interest. Issues dealt with are related to both query answering methods, both model-based and data-driven, the latter based on Machine Learning, and to their needs for explainability and fairness, and big data, notably by taking into account data veracity. Conclusions point out trends and challenges to help better shaping the future of the FQAS field.