Information Retrieval Research Articles

Leveraging Generative AI in Short Document Indexing

The efficiency of information retrieval systems primarily depends on the effective representation of documents during query processing. This representation is mainly constructed from relevant document terms identified and selected during their indexing, which are then used for retrieval. However, when documents contain only a few features, such as in short documents, the resulting representation may be information-poor due to a lack of index terms and their lack of relevance. Although document representation can be enriched using techniques like word embeddings, these techniques require large pre-trained datasets, which are often unavailable in the context of domain-specific short documents. This study investigates a new approach to enrich document representation during indexing using generative AI. In the proposed approach, relevant terms extracted from documents and preprocessed for indexing are enriched with a list of key terms suggested by a large language model (LLM). After conducting a small benchmark of several renowned LLM models for key term suggestions from a set of short texts, the GPT-4o model was chosen to experiment with the proposed indexing approach. The findings of this study yielded notable results, demonstrating that generative AI can efficiently fill the knowledge gap in document representation, regardless of the retrieval technique used.

Design and Implementation of an Interactive Question-Answering System with Retrieval-Augmented Generation for Personalized Databases

This study introduces a novel approach to personalized information retrieval by integrating retrieval augmentation generation (RAG) with a personalized database system. Recent advancements in large language models (LLMs) have shown impressive text generation capabilities but face limitations in knowledge accuracy and hallucinations. Our research addresses these challenges by combining LLMs with structured, personalized data to enhance search precision and relevance. By tagging keywords within personal documents and organizing information into context-based categories, users can conduct efficient searches within their data repositories. We conducted experiments using the GPT-3.5 and text-embedding-ada-002 models and evaluated the RAG assessment framework with five different language models and two embedding models. Our results indicate that the combination of GPT-3.5 and text-embedding-ada-002 is effective for a personalized database question-answering system, with potential for various language models depending on the application. Our approach offers improved accuracy, real-time data updates, and enhanced user experience, making a significant contribution to information retrieval by LLMs and impacting various artificial intelligence applications.

Prior entanglement exponentially improves one-server quantum private information retrieval for quantum messages

Quantum private information retrieval (QPIR) for quantum messages is a quantum communication task, in which a user retrieves one of the multiple quantum states from the server without revealing which state is retrieved. In the one-server setting, we find an exponential gap in the communication complexities between the presence and absence of prior entanglement in this problem with the one-server setting. To achieve this aim, as the first step, we prove that the trivial solution of downloading all messages is optimal under QPIR for quantum messages, which is a similar result to that of classical PIR but different from QPIR for classical messages. As the second step, we propose an efficient one-server one-round QPIR protocol with prior entanglement by constructing a reduction from a QPIR protocol for classical messages to a QPIR protocol for quantum messages in the presence of prior entanglement.

DO CHATBOTS PROVIDE RELIABLE INFORMATION ABOUT MOBILE APPS IN AUDIOLOGY?

IntroductionIn light of the growing interest in utilizing AI for information retrieval, assessing the accuracy and reliability of tools such as chatbots is essential. This study aims to evaluate the efficacy of chatbots in providing accurate information about mobile applications (apps) in the field of audiology.Material and methodsThe responses of the Gemini and ChatGPT chatbots to eight open-ended questions posed in Polish and English were compared. Each answer was assessed for correctness.ResultsGemini_ENG achieved the highest correctness with a score of 5 points (62.5%), while ChatGPT_PL scored 2 points (25%), and both Gemini_PL and ChatGPT_ENG scored 1 point (12.5%). Chatbots were most effective in recommending apps for older adults, with three of the four chatbots providing accurate recommendations. However, they struggled when asked to recommend apps for non-English speakers, to describe apps, or to provide direct links, with none of them scoring points in these areas.ConclusionsChatbots are currently unreliable sources of information about audiology apps. Depending on the language, there is significant variability in response accuracy. A good example is that Gemini_ENG performed far better than Gemini_PL. A major issue for all of them was the frequent fabrication of data, including the creation of nonexistent app names and incorrect links.

Feature/vector entity retrieval and disambiguation techniques to create a supervised and unsupervised semantic table interpretation approach

Recently, there has been an increasing interest in extracting and annotating tables on the Web. This activity allows the transformation of textual data into machine-readable formats to enable the execution of various artificial intelligence tasks, e.g., semantic search and dataset extension. Semantic Table Interpretation (STI) is the process of annotating elements in a table. The paper explores Semantic Table Interpretation, addressing the challenges of Entity Retrieval and Entity Disambiguation in the context of Knowledge Graphs (KGs). It introduces LamAPI, an Information Retrieval system with string/type-based filtering and s-elBat, an Entity Disambiguation technique that combines heuristic and ML-based approaches. By applying the acquired know-how in the field and extracting algorithms, techniques and components from our previous STI approaches and the state of the art, we have created a new platform capable of annotating any tabular data, ensuring a high level of quality.

LLM-AIx: An open source pipeline for Information Extraction from unstructured medical text based on privacy preserving Large Language Models.

In clinical science and practice, text data, such as clinical letters or procedure reports, is stored in an unstructured way. This type of data is not a quantifiable resource for any kind of quantitative investigations and any manual review or structured information retrieval is time-consuming and costly. The capabilities of Large Language Models (LLMs) mark a paradigm shift in natural language processing and offer new possibilities for structured Information Extraction (IE) from medical free text. This protocol describes a workflow for LLM based information extraction (LLM-AIx), enabling extraction of predefined entities from unstructured text using privacy preserving LLMs. By converting unstructured clinical text into structured data, LLM-AIx addresses a critical barrier in clinical research and practice, where the efficient extraction of information is essential for improving clinical decision-making, enhancing patient outcomes, and facilitating large-scale data analysis. The protocol consists of four main processing steps: 1) Problem definition and data preparation, 2) data preprocessing, 3) LLM-based IE and 4) output evaluation. LLM-AIx allows integration on local hospital hardware without the need of transferring any patient data to external servers. As example tasks, we applied LLM-AIx for the anonymization of fictitious clinical letters from patients with pulmonary embolism. Additionally, we extracted symptoms and laterality of the pulmonary embolism of these fictitious letters. We demonstrate troubleshooting for potential problems within the pipeline with an IE on a real-world dataset, 100 pathology reports from the Cancer Genome Atlas Program (TCGA), for TNM stage extraction. LLM-AIx can be executed without any programming knowledge via an easy-to-use interface and in no more than a few minutes or hours, depending on the LLM model selected.

The Characteristics, Difficulties, and Countermeasures of Translating Tang Dynasty Cultural Relics into English

This article examines the characteristics, difficulties, and solutions for translating Tang Dynasty cultural relics into English. It highlights the unique translation process, the importance of communicative principles, the need for understanding cultural backgrounds, and challenges from complex craftsmanship and cultural differences. Suggestions for improving translation quality include collective efforts, multichannel information retrieval, and enhancing translators skills.

Performance aware algorithm design for elastic resource workflow management of cluster consolidation to handle enterprise big data

<span lang="EN-US">Integration and deployment of big data and business analytics application with cloud computing are more attractive as a service and are trending practice. This hybrid workflow is rapidly increasing and will trigger a revolution for enterprise data handling, information retrieval and computing. This paper presents hybrid workflow management framework for big data and multi cloud computing systems in a two-step approach. Linear optimization-based resource assessment algorithm is planned in the first step. Cluster oriented elastic resource allocation and workflow management techniques are concentrated in the second step. This paper also focus on performance evaluation parameters includes execution time, through put with multi task work flow optimization model. The proposed framework is efficiently managed the implementation of hybrid workflows by finetuning the evaluation attributes and provides improvement in terms of response time an average of 6%.</span>

TourMapQA: using deep learning to develop a vietnam map-based tourism question answering system

<p>A question answering system is an important task in information retrieval. In recent years, this system has been interested in research and achieved outstanding results. In general, the output of the question answering is text. However, few studies have used a map as an answer to the question answering in Vietnam tourism. This paper introduces a question answering system integrating long short-term memory (LSTM) on the Vietnam map. Specifically, our model received an input question about any road in Vietnam. Then, the model used LSTM to indicate the coordinate of that road and called the Dijkstra algorithm to find the shortest path from the current location to the input road. Next, from the coordinate of the input road, we leveraged the LSTM model to identify sightseeing places that were on the shortest path. Finally, our system showed all the sightseeing places on the Vietnam map. Technically, the experimental results showed that our model’s performance was improved than previous models such as recurrent neural network, recurrent neural network with embedding, bidirectional recurrent neural network, and encoder-decoder recurrent neural network. Practically in terms, we applied our method to build a real application and compared it with Google Maps, and Bing Map.</p>

Exploring the Potential of Neutrosophic Topological Spaces in Computer Science

Neutrosophic topological spaces (NTS) offer a novel framework for uncertainty modeling by incorporating degrees of truth, indeterminacy, and falsity. This paper investigates the potential applications of NTS in computer science. We provide background on neutrosophic sets and their extension to topological spaces. We then explore how NTS could be used for uncertainty modeling in data analysis (e.g., handling noisy data in sensor networks), pattern recognition (e.g., improving image classification with imprecise features), and information retrieval (e.g., enhancing search results by considering relevance uncertainty). We discuss the challenges associated with applying NTS and highlight promising areas for future research, such as developing efficient algorithms for NTS operations. Overall, this paper aims to stimulate further exploration of how neutrosophic topological spaces can contribute to advancements in various computer science domains.

Generating grating in cavity magnomechanics

Abstract We investigate the phenomenon of magnomechanically induced grating (MMIG) within a cavity magnomechanical system, comprising magnons (spins in a ferromagnet, such as yttrium iron garnet), cavity microwave photons, and phonons (Li et al 2018 Phys. Rev. Lett. 121 203601). By applying an external standing wave control, we observe modifications in the transmission profile of a probe light beam, signifying the presence of MMIG. Through numerical analysis, we explore the diffraction intensities of the probe field, examining the impact of interactions between cavity magnons, magnon-phonon interactions, standing wave field strength, and interaction length. MMIG systems leverage the unique properties of magnons, and collective spin excitations with attributes like long coherence times and spin-wave propagation. These distinctive features can be harnessed in MMIG systems for innovative applications in information storage, retrieval, and quantum memories, offering various orders of diffraction grating.

Investigation of Exercise Interventions on Postoperative Recovery in Lung Cancer Patients: A Qualitative Study Using Web Crawling Technology.

Rapid recovery after lung cancer surgery is challenging. Exercise is a low-cost, effective method to expedite recovery. Despite numerous exercise interventions, many fail to consider patient perspectives, leading to low adherence and short-term effects. Understanding lung cancer patients' perspectives on postoperative exercise and exploring their exercise-related concerns and needs are crucial for enhancing the effectiveness of exercise-based rehabilitation programs. This study aims to analyze lung cancer patients' perspectives on postoperative exercise in their daily lives, exploring their concerns and needs related to postoperative exercise to help healthcare professionals develop personalized exercise plans. An internet crawling technique collected online inquiries from Baidu webpages about postoperative physical activity in lung cancer patients, using "lung cancer", "surgery", and"exercise" as keywords. The data was encoded, categorized, and analyzed using a large-scale semantic analysis platform in natural language processing and information retrieval to examine term frequency, sentiment tendencies, and attributes in the inquiry texts. Initially, 2727 queries were retrieved; after screening, deduplication, and cleansing, 201 unique queries were identified. Queries related to "modes of exercise" constituted the largest proportion. The most frequently occurring words in the word frequency analysis were "lung", "cancer", "should", "can", "long", "early", and "surgery", "exercise", "respiratory". Postoperative lung cancer patients demonstrate significant interest in whether they should engage in exercise, as well as in the appropriate types and duration of such activities, indicating a strong need for detailed guidance and knowledge related to exercise. The sentiment analysis showed a positive score of 87.5% and a negative score of 12.5%, indicating that postoperative lung cancer patients view exercise positively and have an enthusiastic attitude towards it. Among the positive sentiment attributes, "good" was the most frequently mentioned term, whereas "bad" and "surprising" were the most prevalent terms within the negative sentiment attributes. Postoperative physical activity receives limited attention from lung cancer patients, who emphasize their preferences for exercise modalities. Their inquiries often reflect psychological concerns, such as fear and helplessness caused by symptoms. Understanding patients' perspectives on postoperative physical activity within their real-life contexts can help integrate psychological support into exercise plans. This integration could guide healthcare professionals in developing more personalized postoperative exercise regimens for lung cancer patients.

GORetriever: reranking protein-description-based GO candidates by literature-driven deep information retrieval for protein function annotation.

The vast majority of proteins still lack experimentally validated functional annotations, which highlights the importance of developing high-performance automated protein function prediction/annotation (AFP) methods. While existing approaches focus on protein sequences, networks, and structural data, textual information related to proteins has been overlooked. However, roughly 82% of SwissProt proteins already possess literature information that experts have annotated. To efficiently and effectively use literature information, we present GORetriever, a two-stage deep information retrieval-based method for AFP. Given a target protein, in the first stage, candidate Gene Ontology (GO) terms are retrieved by using annotated proteins with similar descriptions. In the second stage, the GO terms are reranked based on semantic matching between the GO definitions and textual information (literature and protein description) of the target protein. Extensive experiments over benchmark datasets demonstrate the remarkable effectiveness of GORetriever in enhancing the AFP performance. Note that GORetriever is the key component of GOCurator, which has achieved first place in the latest critical assessment of protein function annotation (CAFA5: over 1600 teams participated), held in 2023-2024. GORetriever is publicly available at https://github.com/ZhuLab-Fudan/GORetriever.

Development of Drug Discovery Platforms Using Artificial Intelligence and Cheminformatics.

Recently, remarkable progress has been achieved in artificial intelligence (AI), including machine learning. Various AI models have been proposed for drug discovery, including the design of small molecules, activity prediction, and three-dimensional (3D) structure prediction of proteins. AI consists of diverse elements, including information retrieval and machine learning, and can be used in a wide range of drug discovery scenarios. In this review, we focused on AI for small-molecule drug discovery with respect to molecular design, activity prediction, and prediction of the binding poses of compounds to target molecules. We also discussed the applications of AI in academic drug discovery.

Assessing the efficacy of mind mapping as a learning technique to enhance information retrieval in nursing students

Assessing the efficacy of mind mapping as a learning technique to enhance information retrieval in nursing students

Combining hierarchies for spatial reasoning of hierarchical relations among places in different contexts

Spatial reasoning of hierarchical relations is an important research topic in geographic information science. Existing literature focuses mainly on hierarchical relations according to ordered containment of point sets based on the 9-Intersection Model (9IM). However, in natural language descriptions, hierarchical relations expressed by prepositions such as in can have spatial configurations conflicting with those hierarchical relations based on 9IM. This paper proposes an algorithm employing combined hierarchies to infer hierarchical relations among places in different contexts. Two graph databases containing combined hierarchies conforming to administrative reach and geometric enclosure are established with places represented by polygons with misaligned boundaries on prototypical and real data. The 13170 pairs of conflicting hierarchical relations with the same start nodes but different end nodes detected in the combined hierarchies on real data validate the proposed algorithm for spatial reasoning of hierarchical relations in different contexts. This research facilitates consistent spatial reasoning of hierarchical relations in different contexts for spatial information retrieval and question answering.

Searching for studies: A guide to information retrieval for Campbell systematic reviews.

This guide outlines general issues in searching for studies; describes the main sources of potential studies; and discusses how to plan the search process, design, and carry out search strategies, manage references found during the search process and document and report the search process.

Citywalk in China: analysis of regional and route characteristics

ABSTRACT The West introduced Citywalk to China; since then, the concept has undergone a significant transformation from mere walking or hiking activities, impacting people’s choices in transportation and urban spatial experiences. However, the defining characteristics of Citywalk remain unclear. To address this gap, this study considers representative cities in China as examples and employs a comprehensive approach including website information retrieval, SPSS software, and word frequency analysis to collect and analyze data on 611 Citywalk routes in 48 cities. The study objective is to examine the regional characteristics of Citywalk routes and elucidate the factors that shape Citywalk characteristics. Descriptive statistics are used to describe the regional characteristics of Citywalk routes, and three indicators – urban attributes, route features, and node types – are identified. Subsequently, correlation analysis is applied within and between groups. The results indicate a significant correlation among urban attributes, route features, and certain node types with high reliability. This study provides bottom-up data support for the design of linear spatial experiences in Chinese cities and the improvement of tourist route planning, thus offering valuable references for future endeavors in this field.

Decoding compositional complexity: Identifying composers using a model fusion-based approach with nonlinear signal processing and chaotic dynamics

Music, a universal medium that effortlessly transcends the confines of language and culture, serves as a vessel for the distinctive expression of a composer's ingenuity, particularly palpable through the elaborate symphony of melodies, harmonies, and rhythms. This phenomenon is acutely observable in the realm of Turkish Classical Music, where the identification of individual composers poses a formidable challenge due to a confluence of diverse stylistic expressions and sophisticated techniques. Shaped by centuries of cultural interchanges, this genre is celebrated for its convoluted rhythmic frameworks and deep melodic modes, often exhibiting fractal characteristics that compound the complexity of composer classification based on mere audio signals. In response to these complexities, this study introduces an advanced analytical paradigm that amalgamates Multi-resolution analysis, spectral entropy assessments, and a spectrum of multidimensional chaotic and statistical descriptors. By invoking chaos theory, the research delineates distinct patterns and features inherent to musical compositions, subsequently deploying these discoveries for composer categorization. Employing a model fusion-based strategy, the approach utilizes esteemed base estimators for section-level probabilistic determinations, subsequently amalgamated at the song level through a Long Short-Term Memory (LSTM) neural network model to classify a corpus of 380 compositions from 15 distinct composers. The results of this study not only highlight the efficacy of chaos-based approaches in Musical Information Retrieval but also provide a nuanced understanding of the unique characteristics of Turkish Classical Music, thus advancing the boundaries of how musicological data is scrutinized and conceptualized within scholarly discourse.

Cross media knowledge information retrieval model based on D-S evidence theory

Cross media knowledge information retrieval provides strong support for information processing and utilization in the information society, but there are problems such as heterogeneity in cross media knowledge information. Therefore, a cross media knowledge information retrieval model using D-S evidence theory is proposed, which involves using approximate calculation methods to improve this theory for information fusion, reducing computational complexity, and using deep networks for fine-grained information retrieval to improve retrieval accuracy. The results showed that the improved theory enhanced computational efficiency by about 27.23 %. The memory usage was <60 %, and the average accuracy of information fusion reached 93.14 %. It also exhibited high recall and low false alarm rates. The cross media knowledge information retrieval model proposed in the study achieved accuracy of 92.64 %, 96.49 %, and 97.46 % on the three datasets used in the experiment, respectively. The study provides an effective, computationally efficient, and highly accurate model for cross media knowledge information retrieval, which is expected to promote research and application in this field. The combination of improved D-S evidence theory and deep networks provides a powerful approach to solving the problem of cross media heterogeneous information retrieval, which has a positive promoting effect on the processing and utilization of information in the information society.