Improve Information Retrieval Research Articles

Leveraging Deep Learning for Personalized Book Recommendations: A Big Data Algorithm Combining Capsule Networks and Attention Mechanisms

In the era of big data, personalized book recommendations have become crucial for enhancing user satisfaction and improving information retrieval efficiency. This study addresses the limitations of existing book recommendation algorithms by proposing a novel Hybrid Book Recommendation Algorithm Considering Different Preferences (HBRACDP). Our approach integrates Capsule Networks and Self-Attention Mechanisms to model both short-term and long-term user borrowing preferences. We construct separate models for these preferences and combine them using a controllable multi-interest network with label attention. Experimental results on the Goodreads dataset demonstrate the superiority of HBRACDP, achieving an accuracy of 0.984, recall of 0.987, and F1 score of 0.988 in ablation tests. In practical scenarios with 1000 students, HBRACDP significantly outperformed traditional algorithms, with a recommendation accuracy of 97.89% and an error rate of only 0.08%. This study provides new insights for developing more accurate and efficient big data recommendation systems in library services and beyond.

Knowledge Graph Modeling-Driven Large Language Model Operating System (LLM OS) for Task Automation in Process Engineering Problem-Solving

We present the Process Engineering Operations Assistant (PEOA), an AI-driven framework designed to solve complex problems in the chemical and process industries. The framework employs a modular architecture orchestrated by a meta-agent, which serves as the central coordinator, managing an action generator and instruction-tuned small-scale language models (expert models). The action generator decomposes complex problems into sub-tasks and identifies suitable expert models to execute each, delivering precise solutions for multi-step problem-solving. Key techniques include advanced knowledge modeling using property graphs for improved information retrieval, facilitating more accurate and contextually relevant solutions. Additionally, the framework utilizes a teacher-student transfer-learning approach with GPT-4 (Omni) to fine-tune the action generator and expert models for domain adaptation, alongside an iterative problem-solving mechanism with sophisticated error handling. Custom datasets were developed to evaluate the framework against leading proprietary language models on various engineering tasks. The results demonstrate the framework’s effectiveness in automating calculations, accelerating prototyping, and providing AI-augmented decision support for industrial processes, marking a significant advancement in process engineering capabilities.

Alquist 5.0: Dialogue Trees Meet Generative Models, a Novel Approach for Enhancing SocialBot Conversations

This article introduces Alquist 5.0, our SocialBot that was designed for the Alexa Prize SocialBot Grand Challenge 5. Building upon previous iterations, we present the integration of our novel neural response generator (NRG) Barista within a hybrid architecture that combines traditional predefined dialogues with advanced neural response generation. We provide a comprehensive analysis of the current state-of-the-art NRGs and large language models (LLMs), leveraging these insights to enhance Barista’s capabilities. A key focus of our development was in ensuring the safety of our chatbot and implementing robust measures to prevent profanity and inappropriate content. Additionally, we incorporated a new search engine to improve information retrieval and response accuracy. Expanding the capabilities of our system, we designed Alquist 5.0 to accommodate multimodal devices, utilizing APL templates enriched with custom features to deliver an outstanding conversational experience complemented by an excellent user interface. This paper offers detailed insights into the development of Alquist 5.0, which effectively addresses evolving user demands while preserving its empathetic and knowledgeable conversational prowess across a wide range of topics.

An Improved Information Retrieval System using Hybrid RNN LSTM for Multiple Search Engines

An Improved Information Retrieval System using Hybrid RNN LSTM for Multiple Search Engines

Converting and evolving a subject heading list into a thesaurus

PurposeThe objective of this paper is to describe the evolution of the available subject heading list, i.e. the CC Soggettario (Carabinieri Corps Soggettario), towards a thesaurus, that is CCThes (Carabinieri Corps Thesaurus), to support subject indexing and retrieval of the documentary heritage held by the Historical Office of the General Command of the Carabinieri Corps. This work follows the need to implement a controlled vocabulary compliant with the state-of-the-art standards.Design/methodology/approachThe methodology implements the practice of reengineering available vocabularies, following standardised guidelines for thesaurus development. The conversion process includes the balance maintenance of what has been achieved in the CC Soggettario and the enrichment of the semantic structure in the thesaurus by using both deductive and inductive methods.FindingsThe main result of this study is a thesaurus compliant with ISO 29964-1:2011 recommendations, which improves information retrieval performances and interoperability with other vocabularies and applications. It generally has a mono-hierarchical structure with the possibility of admitting, as an exception, the poly-hierarchy for a few concepts. An introductive user guide has been created as a complementary tool to the CCThes.Originality/valueThis is an applied study which deals with Knowledge Organisation System (KOS) reengineering and outlines this process using a pragmatic approach. The paper strength lies in providing the description of performed activities and conveying a set of resources to approach KOS reengineering practice. The study is also relevant for the preservation and diffusion of a part of the social memory and identity of Italy.

Radiologic text correction for better machine understanding

AbstractRadiologic reports often contain misspellings that compromise report quality and pose challenges for machine understanding methods, which require syntactical correctness. General automatic misspell correction solutions are less effective in specialized documents, such as spinal radiologic reports, particularly in morphologically rich languages like Hungarian. Issues arise from complex conjugations and the modification of Latin terms per the rules of the native language. This study introduces a method for the automatic correction of these misspellings, utilizing the Hunspell software and field‐specific dictionaries. This approach, enhanced by linguistic analysis and statistical data, improves information retrieval, as demonstrated in machine‐learning‐based classification and rule‐based identification tasks. Notably, our method identified over 30% more valid errors than human annotators, highlighting its efficiency. We offer a primarily dictionary‐based solution for correcting highly specialized texts and explore the impact of nonword correction on machine understanding. This work underscores the significance of tailored spelling correction in enhancing text processing algorithms' accuracy.

Unlocking the Future: Exploring Librarian’s Perspectives and Readiness for Artificial Intelligence Integration in Eswatini Libraries

This study aims to explore the perception and readiness of librarians in Eswatini regarding the integration of artificial intelligence (AI) in libraries and provide recommendations to assist librarians in effectively embracing AI technologies in their library services. The study’s specific objectives were to determine the level of awareness, examine the perception, assess the level of readiness and provide practical recommendations. Data was collected through a questionnaire administered to a purposive sample of librarians in Eswatini. The study revealed varying levels of awareness among librarians regarding AI integration in libraries. Librarians generally held a positive perception of AI's potential benefits, including improved information retrieval and enhanced user services. However, there were concerns about privacy and data security. The study also identified areas where librarians felt less prepared, such as acquiring programming skills and dealing with ethical considerations. The study recommends training and professional development opportunities to enhance librarians’ skills, establishing clear guidelines and policies to address privacy and data security concerns, and promoting collaborations and knowledge sharing among librarians. The recommendations offer practical guidance for librarians to successfully incorporate AI technologies into their library services, ultimately enhancing the quality and efficiency of library operations.

Evolution of development and implementation of technological innovations in the activities of public libraries in Ukraine

The study of the evolution of development and implementation of technological innovations in the activities of Ukrainian public libraries is relevant in view of the growing role of information and knowledge in society, changing user needs, and the need to increase the competitiveness of libraries. The purpose of the study is to analyse the stages of introducing technological innovations into the work of libraries and the impact of new digital technologies on the services provided by libraries. The study used such methods as analysis, generalisation, explanation, and classification. A review of methods and stages of technological innovations was conducted, and articles by contemporary scientists and researchers who dealt with the issues of library technological innovations were analysed. The impact and prospects of the latest technologies in the work of libraries were investigated. The introduction of technologies raises several ethical issues, such as data privacy, algorithm bias, and the impact on freedom of speech. Technological progress opens up great opportunities for libraries. Artificial intelligence can automate routine tasks, improve information retrieval, and personalise services for users. Virtual and augmented reality will allow the creation of interactive educational and cultural programs, conducting virtual tours of libraries and museums. Blockchain can provide secure and transparent data management, as well as protect copyrights. Technological progress has expanded access to information resources beyond the physical walls of the library. E-books, online databases, and digital archives have made vast collections remotely accessible, especially valuable in geographically dispersed regions. Modern trends in the implementation of technological innovations in the activities of Ukrainian public libraries include the use of artificial intelligence, virtual and augmented reality, and blockchain. This research aims to help Ukrainian libraries successfully implement technological innovations to better serve their users and meet the needs of society in the digital age

From traditional to tech-savvy: the evolution of Nigerian libraries in the machine learning era

Purpose This study explores the implementation of personalised information access, driven by machine learning, in Nigerian public libraries. The purpose of this paper is to address existing challenges, enhance the user experience and bridge the digital divide by leveraging advanced technologies. Design/methodology/approach This study assesses the current state of Nigerian public libraries, emphasising challenges such as underfunding and lack of technology adoption. It proposes the integration of machine learning to provide personalised recommendations, predictive analytics for collection development and improved information retrieval processes. Findings The findings underscore the transformative potential of machine learning in Nigerian public libraries, offering tailored services, optimising resource allocation and fostering inclusivity. Challenges, including financial constraints and ethical considerations, are acknowledged. Originality/value This study contributes to the literature by outlining strategies for responsible implementation and emphasising transparency, user consent and diversity. The research highlights future directions, anticipating advancements in recommendation systems and collaborative efforts for impactful solutions.

From Dewey to Deep Learning: Exploring the Intellectual Renaissance of Libraries through Artificial Intelligence

Libraries are embracing the potential of Artificial Intelligence (AI) to enhance their services and provide more efficient and personalized experiences to users. This paper explores the role of AI in library services, focusing on its applications and impact. The present article begins by discussing the integration of AI technologies such as natural language processing, machine learning, and knowledge graphs in library systems. It then examines the benefits of AI, including improved information retrieval, recommendation systems, virtual assistants, and data analytics. Ethical considerations related to AI in libraries are also addressed. The paper highlights the challenges and future directions for AI implementation, including the need for training of librarians and the importance of user acceptance. The paper contributes to a better understanding of the opportunities and challenges associated with leveraging AI in library services, ultimately paving the way for more effective and user-centric library experiences.

Indexing Excellence: Best Practices in Library Abstracting for Information Retrieval

Indexing and abstracting play pivotal roles in organizing and retrieving information within libraries. This paper delves into the intricate landscape of optimal indexing practices and their profound implications on the efficiency of information retrieval systems. By scrutinizing the methodologies employed in creating high-quality abstracts, this study aims to shed light on their substantial influence on the accessibility and usability of information resources within library collections. The investigation focuses on the multifaceted aspects of indexing excellence, exploring its direct correlation with improved information retrieval mechanisms. It examines in detail the methodologies, standards, and tools utilized in the creation and application of high-quality abstracts. Furthermore, it critically evaluates the utilization of controlled vocabularies and adherence to established indexing guidelines, highlighting their indispensable roles in ensuring the precision and relevance of indexed content. Analyzing the intersection of indexing practices and their impact on information retrieval, this study aims to provide valuable insights into enhancing the efficacy of library abstracting. Recognizing the importance of streamlined access to information resources, this research endeavors to contribute to the continual evolution of information science and user-centered library services.

Deep Learning Algorithms for Personalized Services and Enhanced User Experience in Libraries

The integration of deep learning (DL) algorithms in library settings engenders a multitude of challenges and complexities, encompassing unintended ramifications, ethical quandaries, a dearth of specialized literature elucidating DL in library contexts, the intricacies of dataset selection and human intervention, and the inherent limitations when juxtaposed with the remarkable cognitive capabilities of the human brain. To surmount these hurdles and attain a profound comprehension of DL in library settings, a rigorous and comprehensive systematic literature review (SLR) becomes imperative. This study investigates the application of DL algorithms in examining user-seeking behaviour to provide personalized services and enhance user experience in libraries. Through a comprehensive literature review, the study aims to uncover the benefits, challenges, and implications of integrating DL algorithms for user behaviour analysis and personalized services in library environments. The investigation encompasses a systematic literature review, employing a meticulous search and screening process utilizing the Scopus database. DL algorithms enable tailored recommendations, resource suggestions, and personalized search outcomes, improving information retrieval and user-centric services. Ethical considerations and ongoing research are emphasized to address challenges and maximize the potential of DL algorithms in libraries. The integration of DL algorithms in libraries yields substantial benefits, including improved information retrieval capabilities, augmented resource recommendation systems, and the delivery of user-centric services. The paper offers valuable insights to researchers, practitioners, and stakeholders operating within this field.

Handling Large-Scale Document Collections using Information Retrieval in the Age of Big Data

This paper's primary goal is to present an overview of big data and its analysis utilizing various methodologies, particularly evolutionary computing techniques, which improve information retrieval over standard search methods. is obtainable. Big data is defined as a huge, diverse collection of data that is difficult to handle with conventional computational methods and necessitates the use of more sophisticated statistical approaches in order to extract pertinent information from the data. Along with providing an overview of evolutionary computational approaches, this study also discusses some of the main models used for information retrieval.

Migration Misadventures: The Challenges of Creating an Inter-Institutional Migration Plan

Archival institution migration projects are planned data transfers from existing data formats to more accessible formats that assist in preserving transferred data, improving information retrieval, and preventing obsolescence. Various archival institutions are increasingly planning migration projects because current technology makes the process easier and more affordable than in the past. In this proceeding, I will walk through the planning stages and associated challenges of NASA’s inter-institutional migration plan, Archival Description Center Migration Plans Project, which is part of a larger reorganization of the archives’ functional area to an Enterprise program. For this project, we utilized the open-source, standards-based, web-based applications Access to Memory and Archivematica. At the current project stage, we have collaborated with archivists from seven of the eleven existing NASA archive centers. One of the goals of this Enterprise reorganization is to facilitate cross-collection search and discovery and establish cross-institution metadata standards.

The road from manual to automatic semantic indexing of biomedical literature: a 10 years journey.

Biomedical experts are facing challenges in keeping up with the vast amount of biomedical knowledge published daily. With millions of citations added to databases like MEDLINE/PubMed each year, efficiently accessing relevant information becomes crucial. Traditional term-based searches may lead to irrelevant or missed documents due to homonyms, synonyms, abbreviations, or term mismatch. To address this, semantic search approaches employing predefined concepts with associated synonyms and relations have been used to expand query terms and improve information retrieval. The National Library of Medicine (NLM) plays a significant role in this area, indexing citations in the MEDLINE database with topic descriptors from the Medical Subject Headings (MeSH) thesaurus, enabling advanced semantic search strategies to retrieve relevant citations, despite synonymy, and polysemy of biomedical terms. Over time, advancements in semantic indexing have been made, with Machine Learning facilitating the transition from manual to automatic semantic indexing in the biomedical literature. The paper highlights the journey of this transition, starting with manual semantic indexing and the initial efforts toward automatic indexing. The BioASQ challenge has served as a catalyst in revolutionizing the domain of semantic indexing, further pushing the boundaries of efficient knowledge retrieval in the biomedical field.

DUOINSPECT- Uncovering Duplicates through Powerful Model of Random Forest and XGBoost Classifier

Duplicate question pair detection plays a vital role in improving information retrieval systems and enhancing user experience. In this paper, we present a comprehensive study on duplicate question pair detection utilizing the Quora dataset. We employed machine learning techniques, specifically Random Forest and XGBoost classifiers, to develop accurate models for identifying duplicate question pairs. To improve the performance of the models, we introduced additional features to the dataset, augmenting the original data. By incorporating 22 extra features derived from the raw data, we aimed to capture more nuanced patterns and increase the models' discriminatory power. The Random Forest model achieved a significant improvement, with a performance boost to 89.4% accuracy compared to the initial 73% accuracy. The XGBoost classifier also showed promising results, achieving an accuracy of 73.4% initially and 79.2% after incorporating the additional features. This paper serves as a valuable reference for researchers and practitioners interested in the field of duplicate question pair detection. The findings highlight the effectiveness of Random Forest and XGBoost classifiers in combination with additional features for improving accuracy in this task. The web application provides a practical and user-friendly tool for real- time duplicate question pair detection, offering potential applications in information retrieval systems, chatbots, and question-answering platforms. Key Words: Question Pair Similarity; XGBoost, Random Forest, Machine Learning.

Improving the Performance of Searchable Symmetric Encryption by Optimizing Locality

Both individuals and organizations prioritize data security and privacy, leading to an increasing focus on technological solutions that emphasize these aspects. Searchable symmetric encryption stands out as a prominent choice for secure storage and search of encrypted data on cloud servers, contributing to this objective. While searchable symmetric encryption offers numerous benefits, it also faces certain challenges, particularly when dealing with large databases. One significant challenge is poor performance, often attributed to poor locality. Visiting multiple locations can significantly increase the time required for data retrieval. Additionally, optimization methods aimed at improving locality can sometimes impact read efficiency or result in excessive storage of the encrypted index on the cloud server. In this paper, we present a scheme that effectively addresses these issues. We have enhanced the encrypted inverted index storage mechanism to improve information retrieval performance. Our scheme achieves optimal locality O(1) and optimal read efficiency in O(1), resulting in a significant increase in retrieval speed. Through experimentation with real-world data, we have demonstrated the practicality, accuracy, and security of our scheme.

Improving Semantic Information Retrieval Using Multinomial Naive Bayes Classifier and Bayesian Networks

This research proposes a new approach to improve information retrieval systems based on a multinomial naive Bayes classifier (MNBC), Bayesian networks (BNs), and a multi-terminology which includes MeSH thesaurus (Medical Subject Headings) and SNOMED CT (Systematized Nomenclature of Medicine of Clinical Terms). Our approach, which is entitled improving semantic information retrieval (IMSIR), extracts and disambiguates concepts and retrieves documents. Relevant concepts of ambiguous terms were selected using probability measures and biomedical terminologies. Concepts are also extracted using an MNBC. The UMLS (Unified Medical Language System) thesaurus was then used to filter and rank concepts. Finally, we exploited a Bayesian network to match documents and queries using a conceptual representation. Our main contribution in this paper is to combine a supervised method (MNBC) and an unsupervised method (BN) to extract concepts from documents and queries. We also propose filtering the extracted concepts in order to keep relevant ones. Experiments of IMSIR using the two corpora, the OHSUMED corpus and the Clinical Trial (CT) corpus, were interesting because their results outperformed those of the baseline: the P@50 improvement rate was +36.5% over the baseline when the CT corpus was used.

XML CLUSTERING FRAMEWORK BASED ON DOCUMENT CONTENT AND STRUCTURE IN A HETEROGENEOUS DIGITAL LIBRARY

As textually published information is increasing in digital libraries, efficient retrieval methods are required. Textual documents in a digital library are available in various structures and contents. It is possible to represent these documents with hierarchical levels of granularity when these are organized in XML structure to improve precision by focused retrieval. By this means, contextual elements of each document can be retrieved from a known structure. One solution for retrieving these elements is clustering from a combination of Content and Structural similarities. To achieve this, a novel two-level clustering framework based on Content and Structure is proposed. The framework decomposes a document into meaningful structural units and analyzes all its rich text in its own structure. The quality of the proposed framework was experimented on a heterogeneous XML document collection, having varieties of data sources, structures, and content, be represented as a sample of a real digital library. This collection was made with capabilities to test all of our objectives. The clustering results were evaluated by the Entropy criterion. Finally, the Content and Structure clustering was compared with the usual clustering based on the Content Only to prove the efficacy of considering structural features against the existing Content Only methods in the retrieval process. The total Entropy results of the two-level Content and Structural clustering are almost twice better than the Content Only clustering approach. Consequently, the proposed framework has the ability to improve Information Retrieval systems from two points of view: i) considering the structural aspect of text-rich documents in the retrieval process, and ii) replacing the document-level retrieval with the element-level retrieval.

From STEM to 4D STEM: Ultrafast Diffraction Mapping with a Hybrid-Pixel Detector

Abstract4D scanning transmission electron microscope (STEM) techniques have been increasingly featured among the electron microscopy characterization approaches, as they provide a perspective of improved information retrieval from samples overall. To make 4D STEM experiments as viable as conventional STEM image acquisition, the recording of diffraction patterns with a pixelated detector at fast frame rates, sufficient sensitivity to capture single electron hits, and high dynamic range is necessary. This paper addresses the recent development in hybrid-pixel detector technology that now allows 4D STEM experiments with a similar setup to conventional STEM imaging with pixel collection time under 10 µs. Application examples on virtual STEM detectors and crystal phase-orientation mapping are presented.