Allahim Azzah, Cherif Asma, Imine Abdessamad
IT Department, King Abdulaziz University, Faculty of Computing and Information Technology, Jeddah, Saudi Arabia.
College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia.
PeerJ Comput Sci. 2025 Mar 5;11:e2664. doi: 10.7717/peerj-cs.2664. eCollection 2025.
The internet has been inundated with an ocean of information, and hence, information retrieval systems are failing to provide optimal results to the user. In order to meet the challenge, query expansion techniques have emerged as a game-changer and are improving the results of information retrieval significantly. Of late, semantic query expansion techniques have attracted increased interest among researchers since these techniques offer more pertinent and practical results to the users. These allow the user to retrieve more meaningful and useful information from the web. Currently, few research works provide a comprehensive review on semantic query expansion; usually, they cannot provide a full view on recent advances, diversified data application, and practical challenges. Therefore, it is imperative to go deep in review in order to explain these advances and assist researchers with concrete insights for future development. This article represents the comprehensive review of the query expansion methods, with a particular emphasis on semantic approaches. It overviews the recent frameworks that have been developed within a period of 2015-2024 and reviews the limitations of each approach. Further, it discusses challenges that are inherent in the semantic query expansion field and identifies some future research directions. This article emphasizes that the linguistic approach is the most effective and flexible direction for researchers to follow, while the ontology approach better suits domain-specific search applications. This, in turn, means that development of the ontology field may further open new perspectives for semantic query expansion. Moreover, by employing artificial intelligence (AI) and making most of the query context without relying on user intervention, improvements toward the optimal expanded query can be achieved.
互联网已被海量信息淹没，因此，信息检索系统无法为用户提供最优结果。为应对这一挑战，查询扩展技术应运而生，成为改变局面的关键因素，并显著提升了信息检索的效果。近来，语义查询扩展技术引起了研究人员越来越多的关注，因为这些技术能为用户提供更相关、更实用的结果。它们能让用户从网络中检索到更有意义、更有用的信息。目前，很少有研究工作对语义查询扩展进行全面综述；通常，它们无法全面呈现最新进展、多样化的数据应用及实际挑战。因此，有必要深入综述，以阐释这些进展，并为研究人员提供有助于未来发展的具体见解。本文对查询扩展方法进行了全面综述，尤其着重于语义方法。它概述了2015年至2024年期间开发的最新框架，并审视了每种方法的局限性。此外，它还讨论了语义查询扩展领域固有的挑战，并确定了一些未来的研究方向。本文强调，语言方法是研究人员最有效、最灵活的方向，而本体方法更适合特定领域的搜索应用。这反过来意味着本体领域的发展可能会为语义查询扩展进一步开辟新的视角。此外，通过运用人工智能（AI）并充分利用查询上下文而无需用户干预，可以实现对最优扩展查询的改进。
