The popularity of chatbots, such as ChatGPT, has brought research attention to question answering systems, capable to generate natural language answers to user’s natural language queries. However, also in other kinds of systems, flexibility of querying, including but also going beyond the use of natural language, is an important feature. With this consideration in mind 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 that is not limited to question answering in natural language, but comprises other query forms and interaction modalities, which aim to provide powerful means and techniques for better reflecting human preferences and intentions to retrieve relevant information. It adopts methods at the crossroad of several disciplines among which Information Retrieval (IR), databases, knowledge based systems, knowledge and data engineering, Natural Language Processing (NLP) and the semantic web may be mentioned. The analysis principles are inspired by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework, characterized by a top-down process, starting with relevant keywords for the topic of interest to retrieve relevant articles from meta-sources And complementing these articles with other 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 the selected publications. issues dealt with are related to query answering methods Both model-based and data-driven (the latter based on either machine learning or deep learning) And to their needs for explainability and fairness to deal with 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.

ChatGPT 等聊天机器人的流行引起了对问答系统的研究关注，该系统能够针对用户的自然语言查询生成自然语言答案。然而，在其他类型的系统中，查询的灵活性（包括但也超出了自然语言的使用）也是一个重要特征。考虑到这一点，本文对灵活查询应答系统 (FQAS) 的最新发展、趋势和挑战进行了批判性和全面的分析。灵活查询回答是一个多学科的研究领域，不仅限于自然语言的问答，还包括其他查询形式和交互方式，旨在为更好地反映人类检索相关信息的偏好和意图提供有力的手段和技术。它采用多个学科交叉的方法，其中可以提到信息检索（IR）、数据库、基于知识的系统、知识和数据工程、自然语言处理（NLP）和语义网。分析原则受到系统评价和荟萃分析首选报告项目（PRISMA）框架的启发，其特点是自上而下的过程，从感兴趣主题的相关关键词开始，从元源中检索相关文章并补充这些文章与来自种子来源的其他相关文章通过自下而上的过程识别。为了挖掘检索到的出版物数据，执行网络分析，这允许以综合方式呈现所选出版物的内在主题。所处理的问题与基于模型和数据驱动的查询回答方法（后者基于机器学习或深度学习）以及处理大数据的可解释性和公平性的需求有关，特别是考虑到数据的准确性。结论指出了趋势和挑战，以帮助更好地塑造 FQAS 领域的未来。