A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems

Applying multi-objective evolutionary algorithms to the automatic learning of extended Boolean queries in fuzzy ordinal linguistic information retrieval systems

This paper describes a computer-supported learning system to teach students the principles and concepts of Fuzzy Information Retrieval Systems based on weighted queries. This tool is used to support the teacher's activity in the degree course Information Retrieval Systems Based on Artificial Intelligence at the Faculty of Library and Information Sciences at the University of Granada. Learning of languages of weighted queries in Fuzzy Information Retrieval Systems is complex because it is very difficult to understand the different semantics that could be associated to the weights of queries together with their respective strategies of query evaluation. We have developed and implemented this computer-supported education system because it allows to support the teacher's activity in the classroom to teach the use of weighted queries in FIRSs and it helps students to develop self-learning processes on the use of such queries. We have evaluated the performance of its use in the learning process according to the students' perceptions and their results obtained in the course's exams. We have observed that using this software tool the students learn better the management of the weighted query languages and then their performance in the exams is improved.

Medical image retrieval can assist physicians in finding information supporting their diagnosis and fulfilling information needs. Systems that allow searching for medical images need to provide tools for quick and easy navigation and query refinement as the time available for information search is often short. Relevance feedback is a powerful tool in information retrieval. This study evaluates relevance feedback techniques with regard to the content they use. A novel relevance feedback technique that uses both text and visual information of the results is proposed. The two information modalities from the image examples are fused either at the feature level using the Rocchio algorithm or at the query list fusion step using a common late fusion rule. Results using the ImageCLEF 2012 benchmark database for medical image retrieval show the potential of relevance feedback techniques in medical image retrieval. The mean average precision (mAP) is used as the evaluation metric and the proposed method outperforms commonly-used methods. The baseline without feedback reached 16 % whereas the relevance feedback with 20 images reached up to 26.35 % with three steps and when using 100 images up to 34.87 % in four steps. Most improvements occur in the first two steps of relevance feedback and then results start to become relatively flat. This might also be due to only using positive feedback as negative feeback often also improves results after more steps. The effect of relevance feedback in automatically spelling corrected and translated queries is investigated as well. Results without mistakes were better than spell-corrected results but the spelling correction more than double results over non-corrected retrieval. Multimodal relevance feedback has shown to be able to help visual medical information retrieval. Next steps include integrating semantics into relevance feedback techniques to benefit from the structured knowledge of ontologies and experimenting on the fusion of text and visual information.

Design and implementation issues are discussed for a fuzzy information retrieval system using a knowledge-based system approach. In this context, system performance is identified as a critical point, and heuristics are suggested for a constrained spreading activation in the knowledge base and for optimization of the retrieval procedure. The design and implementation issues outlined are being tested in a prototype system named FIRST (Fuzzy Information Retrieval SysTem), which is being implemented in Prolog on a personal workstation. >

This research represents one of the steps aimed to address one of the most important challenges on the Web and digital libraries, which is compute  the rank of the document’s, and its importance, and their relevance to the user and to meet their needs for information, and so by taking advantage of the vast potential of logic Fuzzy in dealing with this kind of problems, and provide high flexibility for the user to clarify the issues and areas that interested them.
 
 This research is concernd on the design and implementation of a proposal for the information retrieval system, called Fuzzy Information Retrieval System(FZIRS). This system is designed to deal with a huge distributed database on a group of computers (servers) associated with the Intranet network specially designed to work the system, which includes different types and sizes of text files.
 
 The proposed system has the ability for mining of data mining from the database and retrieve useful information from them and that meet the user's needs well.
 
 This accomlished  through the applying of the proposed algorithms for indexing operations and calculate the rank of documents and generate keywords operations and display the  retrival results, which showed high quality when calculating results compared with other Information retrieving algorithms.

In this chapter an overview of the application of fuzzy set theory to soften Information Retrieval Systems is presented. It starts with the description of the main functionalities of an Information Retrieval System, then the main information retrieval models defined in the literature are reviewed and a classification of fuzzy information retrieval models is proposed. Further the fuzzy indexing procedures based on the computation of the significance of the document’s descriptors are illustrated and the introduction of soft requirements into queries, based on both numeric and linguistic weights and soft aggregation operators is discussed. The chapter presents also the fuzzy associative retrieval models based on thesauri, pseudothesauri, and documents clustering and relevance feedback techniques. Finally, in the last section some evaluation issues of IRSs are introduced.

Genetic programming is applied to an information retrieval system in order to improve Boolean query formulation via relevance feedback. This approach brings together the concepts of information retrieval and genetic programming. Documents are viewed as vectors in index term space. A Boolean query, viewed as a parse tree, is an organism in the genetic programming sense. Through the mechanisms of genetic programming, the query is modified in order to improve precision and recall. Relevance feedback is incorporated, in part, via user defined measures over a trial set of documents. The fitness of a candidate query can be expressed directly as a function of the relevance of the retrieved set. Preliminary results based on a testbed are given. The form of the fitness function has a significant effect upon performance and the proper fitness functions take into account relevance based on topicality (and perhaps other factors).< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

BK-FIRM (Bandler-Kohout fuzzy information retrieval model) suggested by Kohout et al. (1984) has been expanded from Boolean information retrieval model. It uses the concept of fuzzy relation which represents relative containments between documents and terms, and it is able to retrieve documents in the way based not on morphology but on semantics, dissimilar to traditional information retrieval theories. It also has merits such that it builds thesaurus automatically and it provides the system with fuzzy ranked documents. BK-FIRM, however, is not able to accept so many domains because its operations have very high time complexity. A-FIRM (advanced Bandler-Kohout fuzzy information retrieval model) improves time complexity of BK-FIRM by using reduced set which is the subset of the term set. In this paper, we suggest A-FIRM which improves time complexity of BK-FIRM's operations.

Recent advancements in Generative AI, such as Large Language Models (LLMs), have demonstrated remarkable success across various general tasks. Extensive studies have explored leveraging generative models in finance, but significant challenges persist. This half-day workshop explores potential approaches and research directions to address these challenges by equipping generative models with advanced Information Retrieval (IR) models. Specifically, this workshop seeks to provide a platform for discussing innovative ideas that facilitate the advancement of IR technology to enrich generative models in finance from four key perspectives: (i) financial IR techniques (ii) financial IR benchmarking and evaluation (iii) financial systems and agents/assistants (iv) and trustworthiness, privacy and security when applying financial IR and generative models. This workshop aims to deepen understanding, accelerate progress, and support the advancement of IR technology to enhance generative models to address financial challenges.

Technology advances in the areas of image processing (IP) and information retrieval (IR) have evolved separately for a long time. However, successful content-based image retrieval systems require the integration of the two. There is an urgent need to develop integration mechanisms to link the image retrieval model to text retrieval model, such that the well established text retrieval techniques can be utilized. Approaches of converting image feature vectors (IF domain) to weighted-term vectors (IR domain) are proposed in this paper. Furthermore, the relevance feedback technique from the IR domain is used in content-based image retrieval to demonstrate the effectiveness of this conversion. Experimental results show that the image retrieval precision increases considerably by using the proposed integration approach.

Term mismatch is a common limitation of traditional information retrieval (IR) models where relevance scores are estimated based on exact matching of documents and queries. Typically, good IR model should consider distinct but semantically similar words in the matching process. In this paper, we propose a method to incorporate word embedding (WE) semantic similarities into existing probabilistic IR models for Arabic in order to deal with term mismatch. Experiments are performed on the standard Arabic TREC collection using three neural word embedding models. The results show that extending the existing IR models improves significantly baseline bag-of-words models. Although the proposed extensions significantly outperform their baseline bag-of-words, the difference between the evaluated neural word embedding models is not statistically significant. Moreover, the overall comparison results show that our extensions significantly improve the Arabic WordNet based semantic indexing approach and three recent WE-based IR language models.

The relevance feedback in information retrieval (IR) is used for query formulation and consists of assessing a sample of retrieved documents. Probably, the most known probabilistic model based on learning from relevance feedback is the so called Robertson and Sparck-Jones&apos; model (RSJ) (S. Robertson and K. Sparck-Jones, 1976; C. van Rijsbergen, 1979), which assigns a value to the indexing terms. The RSJ function can be alternatively seen as a term ranking function which measures the discriminating power of the term, namely the ability of terms to discriminate relevant from non-relevant documents. The process of relevance feedback is similar to that of two-class learning tasks in machine learning (ML). On the basis of a set of positive and negative examples, the learning task aims to select the “optimal hypothesis” which satisfies the data as much as possible and rightly classifies forthcoming data. We address some theoretical issues which arise with the conventional approaches of both IR and ML, suggest a correspondence between IR and ML relatively to the problem of learning from examples. (4 pages)

Image retrieval is an active area of research, which is growing very rapidly. Indeed, stimulated by the rapid growth in storage capacity and processing speed, the number of images in electronic collections and the World Wide Web has considerably increased over the last few years. However, with this abundance of information, people are continuously looking for tools that help them find the image(s) they are looking for within a reasonable amount of time. These tools are image retrieval engines. When using an image retrieval engine, the user is continuously interacting with the machine. First, he1 uses the system’s interface to formulate a query that expresses his needs. Second, he provides feedback about the retrieved results at each search iteration. This allows the engine to provide more accurate results by using relevance feedback (RF) techniques. Third, he may be asked to assign a goodness score or weight to each image retrieved, which helps evaluating the system’s performance. In this chapter, we will review the main interactions between human and the machine in the context of image retrieval. We will address several issues, including: Query formulation: • How the user expresses his needs and what he is looking for • The different ways the query can be formulated: keywords-based, sentence-based, query by example image, query by sketch, query by feature values, composite queries, etc. • Query by region of interest (ROI) vs. global query. • Queries with positive example only vs. queries with both positive and negative examples. • Page zero problem: finding a good image to initiate a retrieval session. Relevance feedback: we will try to answer questions like: • Why do systems use relevance feedback? • How can the user express his needs during the relevance feedback process • How this information is exploited by the system to perform operations like feature selection or the identification of the sought image. 1 Note that the masculine gender has been used strictly to facilitate reading, and is to be understood to include the feminine. Advances in Human-Computer Interaction 216 • The different families of RF techniques. • Relevance feedback with retrieval memory, i.e., taking into account the value of old iteration queries when constructing the new one. • Whether it is useful for the system to create user profiles, and the challenges it has to face. • The number of RF iterations required to obtain satisfactory results. Viewing retrieval results: • Existing viewing techniques: 2D linear presentation, 3D-based presentation, etc. • Different ways the resulting images may be ordered and presented to the user: similarity-ordered, time-ordered, event-ordered, etc. Evaluation of the retrieval performance by the user: • How the user can express his satisfaction/dissatisfaction about the retrieved images • What about the ground truth in image retrieval evaluation? • System response time and its influence on user satisfaction. • The ease of use of the system’s interface. Other issues: • User’s needs: He may be looking for a specific image, for images that meet a given need (e.g. illustrate a concept) or simply browsing the collection looking for potentially “good” images.

Synthetic pattern generation for imbalanced learning in image retrieval

In the current decade, we are witnessing a great interest in Content Based Image Retrieval (CBIR) together with a wealth of promising technologies, paved for a large number of new mechanisms and systems. In terms of mechanisms, a strong trend towards the employment of diverse Relevance Feedback (RF) approaches in CBIR systems to capture image(s) of interest has emerged. However, the need to select a particular technique in a given application domain depends on the nature of images in the collection at hand. So our paper mainly reviews and compares different approaches of CBIR using RF. Its ultimate goal is to present information about image database aspects and image features setting so as to support the selection of the appropriate CBIR with RF Techniques.