Since 2004, researchers have been using the mathematical framework of Quantum Theory (QT) in Information Retrieval (IR). QT offers a generalized probability and logic framework. Such a framework has been shown capable of unifying the representation, ranking and user cognitive aspects of IR, and helpful in developing more dynamic, adaptive and context-aware IR systems. Although Quantum-inspired IR is still a growing area, a wide array of work in different aspects of IR has been done and produced promising results. This paper presents a survey of the research done in this area, aiming to show the landscape of the field and draw a road-map of future directions.
This survey paper discusses different forms of malicious techniques that can affect how an information retrieval model retrieves documents for a query and their remedies.
Explainable information retrieval is an emerging research area aiming to make transparent and trustworthy information retrieval systems. Given the increasing use of complex machine learning models in search systems, explainability is essential in building and auditing responsible information retrieval models. This survey fills a vital gap in the otherwise topically diverse literature of explainable information retrieval. It categorizes and discusses recent explainability methods developed for different application domains in information retrieval, providing a common framework and unifying pers…
Text retrieval is a long-standing research topic on information seeking, where a system is required to return relevant information resources to user's queries in natural language. From classic retrieval methods to learning-based ranking functions, the underlying retrieval models have been continually evolved with the ever-lasting technical innovation. To design effective retrieval models, a key point lies in how to learn the text representation and model the relevance matching. The recent success of pretrained language models (PLMs) sheds light on developing more capable text retrieval approac…
The continuous growth in the XML information repositories has been matched by increasing efforts in development of XML retrieval systems, in large parts aiming at supporting content-oriented XML retrieval. These systems exploit the available structural information, as market up in XML documents, in order to return documents components- the so called XML elements-instead of the complement documents in repose to the user query. In this paper, we provide an overview of the different XML information retrieval systems and classify them according to their storage and query evaluation strategies.
There have been suggestions within the Information Retrieval (IR) community that quantum mechanics (QM) can be used to help formalise the foundations of IR. The invoked connection to QM is mathematical rather than physical. The proposed ideas are concerned with information which is encoded, processed and accessed in classical computers. However, some of the suggestions have been thoroughly muddled with questions about applying techniques of quantum information theory in IR, and it is often unclear whether or not the suggestion is to perform actual quantum information processing on the informat…
This paper is a survey discussing Information Retrieval concepts, methods, and applications. It goes deep into the document and query modelling involved in IR systems, in addition to pre-processing operations such as removing stop words and searching by synonym techniques. The paper also tackles text categorization along with its application in neural networks and machine learning. Finally, the architecture of web crawlers is to be discussed shedding the light on how internet spiders index web documents and how they allow users to search for items on the web.
We present a short survey of the literature on indexing and retrieval of mathematical knowledge, with pointers to 72 papers and tentative taxonomies of both retrieval problems and recurring techniques.
Tables are a powerful and popular tool for organizing and manipulating data. A vast number of tables can be found on the Web, which represents a valuable knowledge resource. The objective of this survey is to synthesize and present two decades of research on web tables. In particular, we organize existing literature into six main categories of information access tasks: table extraction, table interpretation, table search, question answering, knowledge base augmentation, and table augmentation. For each of these tasks, we identify and describe seminal approaches, present relevant resources, and…
With the ever increasing size of the web, relevant information extraction on the Internet with a query formed by a few keywords has become a big challenge. Query Expansion (QE) plays a crucial role in improving searches on the Internet. Here, the user's initial query is reformulated by adding additional meaningful terms with similar significance. QE -- as part of information retrieval (IR) -- has long attracted researchers' attention. It has become very influential in the field of personalized social document, question answering, cross-language IR, information filtering and multimedia IR. Rese…
Center Leo Apostel for Interdisciplinary Studies（莱奥·阿波斯泰尔跨学科研究中心）；Brussels Free University（布鲁塞尔自由大学）；University of British Columbia（不列颠哥伦比亚大学）
In recent years, quantum-based methods have promisingly integrated the traditional procedures in information retrieval (IR) and natural language processing (NLP). Inspired by our research on the identification and application of quantum structures in cognition, more specifically our work on the representation of concepts and their combinations, we put forward a 'quantum meaning based' framework for structured query retrieval in text corpora and standardized testing corpora. This scheme for IR rests on considering as basic notions, (i) 'entities of meaning', e.g., concepts and their combination…
The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content-based image retrieval (CBIR), which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problem…
The problem of representing text documents within an Information Retrieval system is formulated as an analogy to the problem of representing the quantum states of a physical system. Lexical measurements of text are proposed as a way of representing documents which are akin to physical measurements on quantum states. Consequently, the representation of the text is only known after measurements have been made, and because the process of measuring may destroy parts of the text, the document is characterised through erasure. The mathematical foundations of such a quantum representation of text are…
SHRAG: AFrameworkfor Combining Human-Inspired Search with RAG
Peoples' Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学）；Joint Institute for Nuclear Research（联合核子研究中心）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；University of Skövde（斯德哥尔摩大学）；Department of AI Convergence（人工智能融合部门）；College of Information Computing（信息计算学院）
In recent years, the importance of research data and the need to archive and to share it in the scientific community have increased enormously. This introduces a whole new set of challenges for digital libraries. In the social sciences typical research data sets consist of surveys and questionnaires. In this paper we focus on the use case of social science survey question reuse and on mechanisms to support users in the query formulation for data sets. We describe and evaluate thesaurus- and co-occurrence-based approaches for query expansion to improve retrieval quality in digital libraries and…
Soochow University（苏州大学）；Shandong University（山东大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））
In the past few years, cross-modal image-text retrieval (ITR) has experienced increased interest in the research community due to its excellent research value and broad real-world application. It is designed for the scenarios where the queries are from one modality and the retrieval galleries from another modality. This paper presents a comprehensive and up-to-date survey on the ITR approaches from four perspectives. By dissecting an ITR system into two processes: feature extraction and feature alignment, we summarize the recent advance of the ITR approaches from these two perspectives. On top…
Amirkabir University of Technology (Tehran Polytechnic)（阿米尔卡比尔理工大学（德黑兰理工大学））
Text-based Question Answering (QA) is a challenging task which aims at finding short concrete answers for users' questions. This line of research has been widely studied with information retrieval techniques and has received increasing attention in recent years by considering deep neural network approaches. Deep learning approaches, which are the main focus of this paper, provide a powerful technique to learn multiple layers of representations and interaction between questions and texts. In this paper, we provide a comprehensive overview of different models proposed for the QA task, including …
Visual media has always been the most enjoyed way of communication. From the advent of television to the modern day hand held computers, we have witnessed the exponential growth of images around us. Undoubtedly it's a fact that they carry a lot of information in them which needs be utilized in an effective manner. Hence intense need has been felt to efficiently index and store large image collections for effective and on- demand retrieval. For this purpose low-level features extracted from the image contents like color, texture and shape has been used. Content based image retrieval systems emp…
Content based image retrieval, a technique which uses visual contents of image to search images from large scale image databases according to users' interests. This paper provides a comprehensive survey on recent technology used in the area of content based face image retrieval. Nowadays digital devices and photo sharing sites are getting more popularity, large human face photos are available in database. Multiple types of facial features are used to represent discriminality on large scale human facial image database. Searching and mining of facial images are challenging problems and important…
A Survey of Model Architectures in Information Retrieval
Kahlert School of Computing（卡勒特计算学院）；University of Utah（犹他大学）；University of Montreal（蒙特利尔大学）；Capital One（第一资本）；University of Waterloo（滑铁卢大学）；Snowflake Inc.（雪湖公司）；Scientific Computing and Imaging (SCI) Institute（科学计算与成像研究所）
LIM – Music Informatics Laboratory Department of Computer Science University of Milan（米兰大学计算机科学系LIM - 音乐信息学实验室）
Towards improving the performance in various music information processing tasks, recent studies exploit different modalities able to capture diverse aspects of music. Such modalities include audio recordings, symbolic music scores, mid-level representations, motion, and gestural data, video recordings, editorial or cultural tags, lyrics and album cover arts. This paper critically reviews the various approaches adopted in Music Information Processing and Retrieval and highlights how multimodal algorithms can help Music Computing applications. First, we categorize the related literature based on…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
This article gives a survey for bag-of-words (BoW) or bag-of-features model in image retrieval system. In recent years, large-scale image retrieval shows significant potential in both industry applications and research problems. As local descriptors like SIFT demonstrate great discriminative power in solving vision problems like object recognition, image classification and annotation, more and more state-of-the-art large scale image retrieval systems are trying to rely on them. A common way to achieve this is first quantizing local descriptors into visual words, and then applying scalable text…
A Survey of Controllable Learning: Methods and Applications in Information Retrieval
Gaoling School of Artificial Intelligence, Renmin University of China, Beijing 100872, China（中国人民大学人工智能学院）；AI Lab at Lenovo Research, Beijing 100085, China（联想研究院人工智能实验室）
IBM Israel Research Lab（IBM以色列研究实验室）；Hebrew University of Jerusalem（希伯来大学）
Intent detection with semantically similar fine-grained intents is a challenging task. To address it, we reformulate intent detection as a question-answering retrieval task by treating utterances and intent names as questions and answers. To that end, we utilize a question-answering retrieval architecture and adopt a two stages training schema with batch contrastive loss. In the pre-training stage, we improve query representations through self-supervised training. Then, in the fine-tuning stage, we increase contextualized token-level similarity scores between queries and answers from the same …
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
In recent years, cross-modal retrieval has drawn much attention due to the rapid growth of multimodal data. It takes one type of data as the query to retrieve relevant data of another type. For example, a user can use a text to retrieve relevant pictures or videos. Since the query and its retrieved results can be of different modalities, how to measure the content similarity between different modalities of data remains a challenge. Various methods have been proposed to deal with such a problem. In this paper, we first review a number of representative methods for cross-modal retrieval and clas…
Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges
Beijing University of Posts and Telecommunications（北京邮电大学）；University of Georgia（佐治亚大学）；South China University of Technology（华南理工大学）；Technical University of Munich, University of Cologne（慕尼黑技术大学、科隆大学）；SenseTime Research（商汤科技研究院）；Qingyuan Research Institute, Shanghai Jiaotong University（青原研究院，上海交通大学）
Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation
Computer Engineering Department, Sharif University of Technology（沙斐大学计算机工程系）；College of Interdisciplinary Science and Technology, University of Tehran（德黑兰大学跨学科科学与技术学院）；Computer Engineering Department, K.N. Toosi University of Technology（技术学院计算机工程系）；Qatar Computing Research Institute（卡塔尔计算研究院）
Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
Cleveland State University（克利夫兰州立大学）；Kent State University（肯特州立大学）；Northeastern University（东北大学）；Roux Institute at Northeastern University（东北大学鲁克斯研究所）；University of Agder (UiA)（阿格德尔大学）
