Information Retrieval Interface Research Articles

The Impact of ChatGPT for Enhancing Knowledge Management in University Libraries

This study examines the integration of ChatGPT into university libraries to enhance knowledge management practices. Employing a qualitative case study approach, twelve respondents from six universities, including Library and Information Science (LIS) and Information Technology (IT) professionals, were interviewed using a semi-structured questionnaire. The research explores the processes, user experiences, challenges, and perceived benefits of ChatGPT in these academic settings. Findings indicate that ChatGPT significantly improves user interactions by providing a more intuitive and accessible information retrieval interface. The study also highlights the critical role of interdisciplinary collaboration between LIS and IT professionals in successfully implementing ChatGPT, which aligns with existing literature emphasizing user-centric design in knowledge management. Challenges such as data privacy and ongoing system refinement were identified, underscoring the need for robust security measures and continuous improvement. The study concludes that while ChatGPT offers considerable benefits in enhancing knowledge management and user interaction, it requires careful integration and ongoing management to realize its full potential. Future research should broaden the scope of institutions studied and explore longitudinal impacts to enhance generalizability and understand long-term effects.

GWPD: a multifunctional platform to unravel biological risk factors in global engineered water systems

GWPD: a multifunctional platform to unravel biological risk factors in global engineered water systems

An IoT-Based Multimodal Real-Time Home Control System for the Physically Challenged: Design and Implementation

Physical impairments affect a significant proportion of the global populace, emphasizing the need for assistive technologies to increase the ability of these individuals to perform daily activities autonomously. This study discusses the development and implementation of a multimodal home control system, designed to afford physically challenged individuals greater control over their home environments. This system utilizes the Internet of Things (IoT) for its functionality. The system is primarily based on the utilization of the Amazon Alexa Echo Dot, which facilitates speech-based control, and a sequential clap recognition system, both made possible through an internet connection. These methods are further supplemented by an additional manual switching option, thereby ensuring a diverse range of control methods. The processing core of this system consists of an Arduino Uno and an ESP32 Devkit module. In conjunction with these, a sound detector is employed to discern and process a variety of clap patterns, which is set to function at a predefined threshold. The Amazon Alexa Echo Dot serves as the primary interface for voice commands and real-time information retrieval. Furthermore, an Android smartphone, equipped with the Alexa application, provides alternate interfaces for appliance control, through both soft buttons and voice commands. Based on an analysis of this system, it is suggested that it is not only viable but also effective. Key attributes of the system include rapid response times, aesthetic appeal, secure operation, low energy consumption, and most importantly, increased accessibility for physically disabled individuals.

Recommender System for Information Retrieval Using Natural Language Querying Interface Based in Bibliographic Research for Naïve Users

With the increasing of data on the internet, data analysis has become inescapable to gain time and efficiency, especially in bibliographic information retrieval systems. We can estimate the number of actual scientific journals points to around 40,000 with about four million articles published each year. Machine learning and deep learning applied to recommender systems had become unavoidable whether in industry or in research. In this current, we propose an optimized interface for bibliographic information retrieval as a running example, which allows different kind of researchers to find their needs following some relevant criteria through natural language understanding. Papers indexed in Web of Science and Scopus are in high demand. Natural language including text and linguistic-based techniques, such as tokenization, named entity recognition, syntactic and semantic analysis, are used to express natural language queries. Our Interface uses association rules to find more related papers for recommendation. Spanning trees are challenged to optimize the search process of the system.

COVID-19 preVIEW: Semantic Search to Explore COVID-19 Research Preprints.

During the current COVID-19 pandemic, the rapid availability of profound information is crucial in order to derive information about diagnosis, disease trajectory, treatment or to adapt the rules of conduct in public. The increased importance of preprints for COVID-19 research initiated the design of the preprint search engine preVIEW. Conceptually, it is a lightweight semantic search engine focusing on easy inclusion of specialized COVID-19 textual collections and provides a user friendly web interface for semantic information retrieval. In order to support semantic search functionality, we integrated a text mining workflow for indexing with relevant terminologies. Currently, diseases, human genes and SARS-CoV-2 proteins are annotated, and more will be added in future. The system integrates collections from several different preprint servers that are used in the biomedical domain to publish non-peer-reviewed work, thereby enabling one central access point for the users. In addition, our service offers facet searching, export functionality and an API access. COVID-19 preVIEW is publicly available at https://preview.zbmed.de.

Onto Semantic Tourism Information Retrieval

Semantic Search is an area of research which focuses on meaning of terms used in user query. Ontology plays significant role to define the concept and the relationship of terms in domain. Since the understanding of concepts is domain specific, Ontology creation is also domain specific. According to this argument, query interpreted in Tourism domain can have different meaning in some other domain. This paper presents a prototype of information retrieval interface using ontology which can save users time by rendering relevant, precise and efficient search results as compared to traditional search interfaces.

A natural language interface to a graph-based bibliographic information retrieval system

Abstract With the ever-increasing volume of scientific literature, there is a need for a natural language interface to bibliographic information retrieval systems to retrieve relevant information effectively. In this paper, we propose one such interface, NLI-GIBIR, which allows users to search for a variety of bibliographic data through natural language. NLI-GIBIR makes use of a novel framework applicable to graph-based bibliographic information retrieval systems in general. This framework incorporates algorithms/heuristics for interpreting and analyzing natural language bibliographic queries via a series of text- and linguistic-based techniques, including tokenization, named entity recognition, and syntactic analysis. We find that our framework, as implemented in NLI-GIBIR, can effectively represent and address complex bibliographic information needs. Thus, the contributions of this paper are as follows: First, to our knowledge, it is the first attempt to propose a natural language interface for graph-based bibliographic information retrieval. Second, we propose a novel customized natural language processing framework that integrates a few original algorithms/heuristics for interpreting and analyzing bibliographic queries. Third, we show that the proposed framework and natural language interface provide a practical solution for building real-world bibliographic information retrieval systems. Our experimental results show that the presented system can correctly answer 39 out of 40 example natural language queries with varying lengths and complexities.

Searching bibliographic data using graphs: A visual graph query interface

Abstract With the ever-increasing scientific literature, improving the efficiency of searching bibliographic data has become an important issue. With a lack of support of current bibliographic information retrieval systems in expressing complicated information needs, getting relevant bibliographic data is a demanding task. In this paper, we propose a visual graph query interface for bibliographic information retrieval. Through this interface, users can formulate bibliographic queries by interacting with a graph. Visual graph queries use a set of nodes with constraints and links among nodes to represent explicit and precise bibliographic information needs. The proposed visual graph query interface allows users to formulate several complex bibliographic queries (e.g., bibliographic coupling) that are not attainable in current major bibliographic information retrieval systems. In addition, the proposed interface requires less number of queries in completing everyday bibliographic search tasks.

EpiGeNet: A Graph Database of Interdependencies Between Genetic and Epigenetic Events in Colorectal Cancer.

The development of colorectal cancer (CRC)-the third most common cancer type-has been associated with deregulations of cellular mechanisms stimulated by both genetic and epigenetic events. StatEpigen is a manually curated and annotated database, containing information on interdependencies between genetic and epigenetic signals, and specialized currently for CRC research. Although StatEpigen provides a well-developed graphical user interface for information retrieval, advanced queries involving associations between multiple concepts can benefit from more detailed graph representation of the integrated data. This can be achieved by using a graph database (NoSQL) approach. Data were extracted from StatEpigen and imported to our newly developed EpiGeNet, a graph database for storage and querying of conditional relationships between molecular (genetic and epigenetic) events observed at different stages of colorectal oncogenesis. We illustrate the enhanced capability of EpiGeNet for exploration of different queries related to colorectal tumor progression; specifically, we demonstrate the query process for (i) stage-specific molecular events, (ii) most frequently observed genetic and epigenetic interdependencies in colon adenoma, and (iii) paths connecting key genes reported in CRC and associated events. The EpiGeNet framework offers improved capability for management and visualization of data on molecular events specific to CRC initiation and progression.

Middle-school students' online information problem solving behaviors on the information retrieval interface

<p>With the near-overload of online information, it is necessary to equip our students with the skills necessary to deal with Information Problem Solving (IPS). This study also intended to help students develop major IPS strategies with the assistance of an instructor's scaffolding in a designed IPS course as well as on an Online Information Management (OIM) interface. Explicit strategies that students employed to organize information for final projects were identified and hierarchically leveled based on the cognitive complexity they required. Results from a correlation analysis showed a significantly positive relationship among students' project scores, IPS strategies (e.g. organizing information in a logical way), and explicit strategies (e.g. search terms), but no significant relationship involving implicit strategies. Further examinations showed the students with higher project scores had advanced IPS and implicit strategies, while those with intermediate or lower scores likely over- or under-estimated their utilisation of implicit strategies. That is, self-efficacy surveys can be discriminative instruments if students have good self-monitoring abilities. Another feature of the students with high proficiency on IPS was the use of full sentences when using search engines, which implied less demands for the exactitude in search term selection due to the advances of search engines.</p><p> </p>

Learning and Recognition of On-Premise Signs From Weakly Labeled Street View Images

Camera-enabled mobile devices are commonly used as interaction platforms for linking the user's virtual and physical worlds in numerous research and commercial applications, such as serving an augmented reality interface for mobile information retrieval. The various application scenarios give rise to a key technique of daily life visual object recognition. On-premise signs (OPSs), a popular form of commercial advertising, are widely used in our living life. The OPSs often exhibit great visual diversity (e.g., appearing in arbitrary size), accompanied with complex environmental conditions (e.g., foreground and background clutter). Observing that such real-world characteristics are lacking in most of the existing image data sets, in this paper, we first proposed an OPS data set, namely OPS-62, in which totally 4649 OPS images of 62 different businesses are collected from Google's Street View. Further, for addressing the problem of real-world OPS learning and recognition, we developed a probabilistic framework based on the distributional clustering, in which we proposed to exploit the distributional information of each visual feature (the distribution of its associated OPS labels) as a reliable selection criterion for building discriminative OPS models. Experiments on the OPS-62 data set demonstrated the outperformance of our approach over the state-of-the-art probabilistic latent semantic analysis models for more accurate recognitions and less false alarms, with a significant 151.28% relative improvement in the average recognition rate. Meanwhile, our approach is simple, linear, and can be executed in a parallel fashion, making it practical and scalable for large-scale multimedia applications.

Evaluation of a Novel Conjunctive Exploratory Navigation Interface for Consumer Health Information: A Crowdsourced Comparative Study

BackgroundNumerous consumer health information websites have been developed to provide consumers access to health information. However, lookup search is insufficient for consumers to take full advantage of these rich public information resources. Exploratory search is considered a promising complementary mechanism, but its efficacy has never before been rigorously evaluated for consumer health information retrieval interfaces.ObjectiveThis study aims to (1) introduce a novel Conjunctive Exploratory Navigation Interface (CENI) for supporting effective consumer health information retrieval and navigation, and (2) evaluate the effectiveness of CENI through a search-interface comparative evaluation using crowdsourcing with Amazon Mechanical Turk (AMT).MethodsWe collected over 60,000 consumer health questions from NetWellness, one of the first consumer health websites to provide high-quality health information. We designed and developed a novel conjunctive exploratory navigation interface to explore NetWellness health questions with health topics as dynamic and searchable menus. To investigate the effectiveness of CENI, we developed a second interface with keyword-based search only. A crowdsourcing comparative study was carefully designed to compare three search modes of interest: (A) the topic-navigation-based CENI, (B) the keyword-based lookup interface, and (C) either the most commonly available lookup search interface with Google, or the resident advanced search offered by NetWellness. To compare the effectiveness of the three search modes, 9 search tasks were designed with relevant health questions from NetWellness. Each task included a rating of difficulty level and questions for validating the quality of answers. Ninety anonymous and unique AMT workers were recruited as participants.ResultsRepeated-measures ANOVA analysis of the data showed the search modes A, B, and C had statistically significant differences among their levels of difficulty (P<.001). Wilcoxon signed-rank test (one-tailed) between A and B showed that A was significantly easier than B (P<.001). Paired t tests (one-tailed) between A and C showed A was significantly easier than C (P<.001). Participant responses on the preferred search modes showed that 47.8% (43/90) participants preferred A, 25.6% (23/90) preferred B, 24.4% (22/90) preferred C. Participant comments on the preferred search modes indicated that CENI was easy to use, provided better organization of health questions by topics, allowed users to narrow down to the most relevant contents quickly, and supported the exploratory navigation by non-experts or those unsure how to initiate their search.ConclusionsWe presented a novel conjunctive exploratory navigation interface for consumer health information retrieval and navigation. Crowdsourcing permitted a carefully designed comparative search-interface evaluation to be completed in a timely and cost-effective manner with a relatively large number of participants recruited anonymously. Accounting for possible biases, our study has shown for the first time with crowdsourcing that the combination of exploratory navigation and lookup search is more effective than lookup search alone.

Efficient data management, intelligent information retrieval and flexible user interface in PDM systems

Efficient data management, intelligent information retrieval and flexible user interface in PDM systems

English

A convenient and user -friendly interface for information retrieval through computer is an important issue. Speech being natural mode of communication among human beings has the potential of being a primary as well as convenient mode of interaction with computer. This is a good reason for building systems that communicate with users via speech. Marathi is one of the most widely used language is Maharashtra .An interesting domain for such speech understanding systems is information dialogs where the user wants to get some information by asking the system which takes the role of an Intelligent Machine in the field of interest. Speech recognition having much more application in health sector In order to make such a communication process possible we explore the new approach of Polly Clinic Inquiry System using Marathi Speech Recognition.

Construction and Expression of Database for Computer-Aided Welding Fixture Design (CAWFD) Systems

Welding fixture can ensure welding dimension, improve assembly precision and efficiency. It can prevent the welding deformation. The function of welding fixture was introduced in this paper. The Classification of welding fixture component was described. Information tree and the database tables to save the unit information are designed. The unit information retrieval menu and interface based on UG, Visual C++ and database is realized.

JANE: efficient mapping of prokaryotic ESTs and variable length sequence reads on related template genomes

BackgroundESTs or variable sequence reads can be available in prokaryotic studies well before a complete genome is known. Use cases include (i) transcriptome studies or (ii) single cell sequencing of bacteria. Without suitable software their further analysis and mapping would have to await finalization of the corresponding genome.ResultsThe tool JANE rapidly maps ESTs or variable sequence reads in prokaryotic sequencing and transcriptome efforts to related template genomes. It provides an easy-to-use graphics interface for information retrieval and a toolkit for EST or nucleotide sequence function prediction. Furthermore, we developed for rapid mapping an enhanced sequence alignment algorithm which reassembles and evaluates high scoring pairs provided from the BLAST algorithm. Rapid assembly on and replacement of the template genome by sequence reads or mapped ESTs is achieved. This is illustrated (i) by data from Staphylococci as well as from a Blattabacteria sequencing effort, (ii) mapping single cell sequencing reads is shown for poribacteria to sister phylum representative Rhodopirellula Baltica SH1. The algorithm has been implemented in a web-server accessible at http://jane.bioapps.biozentrum.uni-wuerzburg.de.ConclusionRapid prokaryotic EST mapping or mapping of sequence reads is achieved applying JANE even without knowing the cognate genome sequence.

Text-mining of PubMed abstracts by natural language processing to create a public knowledge base on molecular mechanisms of bacterial enteropathogens.

BackgroundThe Enteropathogen Resource Integration Center (ERIC; ) has a goal of providing bioinformatics support for the scientific community researching enteropathogenic bacteria such as Escherichia coli and Salmonella spp. Rapid and accurate identification of experimental conclusions from the scientific literature is critical to support research in this field. Natural Language Processing (NLP), and in particular Information Extraction (IE) technology, can be a significant aid to this process.DescriptionWe have trained a powerful, state-of-the-art IE technology on a corpus of abstracts from the microbial literature in PubMed to automatically identify and categorize biologically relevant entities and predicative relations. These relations include: Genes/Gene Products and their Roles; Gene Mutations and the resulting Phenotypes; and Organisms and their associated Pathogenicity. Evaluations on blind datasets show an F-measure average of greater than 90% for entities (genes, operons, etc.) and over 70% for relations (gene/gene product to role, etc). This IE capability, combined with text indexing and relational database technologies, constitute the core of our recently deployed text mining application.ConclusionOur Text Mining application is available online on the ERIC website . The information retrieval interface displays a list of recently published enteropathogen literature abstracts, and also provides a search interface to execute custom queries by keyword, date range, etc. Upon selection, processed abstracts and the entities and relations extracted from them are retrieved from a relational database and marked up to highlight the entities and relations. The abstract also provides links from extracted genes and gene products to the ERIC Annotations database, thus providing access to comprehensive genomic annotations and adding value to both the text-mining and annotations systems.

A review of research on human‐computer interfaces for online information retrieval systems

PurposeThe purpose of this article is to review the research on human‐computer interfaces for library‐based commercial online information retrieval (IR) systems.Design/methodology/approachThe review first focuses on basic interface issues for information retrieval such as interface style, end‐user searching, query formulation, relevance feedback and browsing. The second part deals with cognitive engineering in IR including mental models and individual differences. Finally, the topics on user interface engineering are covered. These include user interface guidelines, usability evaluation methods and interface engineering techniques.FindingsThe review shows that user interface design has received a limited attention from IR researchers. There is a need for adopting human‐computer interaction (HCI) techniques into IR interface designs, but this issue has not yet been fully recognised by the commercial database vendors and distributors. The paper recommends that applying HCI techniques could help in developing more usable IR interfaces.Practical implicationsThe review identifies the main activities of a user‐centred design methodology and suggests that IR interface designers should use this method in future. This could have major implications in IR interface design for end‐user searching.Originality/valueThe review is the first to offer an overview of empirical research on IR interface design and IR usability engineering. Both IR researchers and practitioners may benefit from the description of previous research and the user‐centred design advocated by the current research.

Multiple coordinated views for searching and navigating Web content repositories

The advantages and positive effects of multiple coordinated views on search performance have been documented in several studies. This paper describes the implementation of multiple coordinated views within the Media Watch on Climate Change, a domain-specific news aggregation portal available at www.ecoresearch.net/climate that combines a portfolio of semantic services with a visual information exploration and retrieval interface. The system builds contextualized information spaces by enriching the content repository with geospatial, semantic and temporal annotations, and by applying semi-automated ontology learning to create a controlled vocabulary for structuring the stored information. Portlets visualize the different dimensions of the contextualized information spaces, providing the user with multiple views on the latest news media coverage. Context information facilitates access to complex datasets and helps users navigate large repositories of Web documents. Currently, the system synchronizes information landscapes, domain ontologies, geographic maps, tag clouds and just-in-time information retrieval agents that suggest similar topics and nearby locations.

I-SEGOPubmed: a web interface for semantic enabled browsing of PubMed using Gene Ontology

The rapid progress in bioinformatics and functional genomics lead to a vast amount of scientific literature. PubMed is one of the leading repositories and has approximately 20 million abstracts. Browsing such a huge repository to find relevant information as well as providing e-learning service requires new generation of interfaces. Methods such as GOPubmed are using keyword-based techniques. Keyword-based search methods often yield results that are not meaningful to the query. This problem can be partially addressed using a semantic enabled interface for browsing and information retrieval. A web interface called i-SEGOPubmed is being developed in CVPIA lab at the University of Memphis. i-SEGOPubmed enables semantically meaningful browsing of large volume of semi-structured data such as Pubmed using Gene Ontology. i-SEGOPubmed employs a client-server computing model. The SEGOPubmed engine [1] is used to process queries and web interface is using the Java Web Start, which uses Java Network Launching Protocol (JNLP) for downloading the application from the server to the client. Once the application is downloaded, Java Web Start initializes the application to check for the local resources. i-SEGOPubmed requires JRE (Java Runtime Environment) for its execution. It uses the Java Sockets to establish a data communication between the interface running on the client machine and the query processing done on the server machine. The server machine has a dedicated Server Socket listening to client requests. Once the server receives a request, it initializes a full duplex communication channel between the server and the client machine. Using this data channel, the interface sends the query to the server for further processing. Figure ​Figure11 shows the system architecture and the information flow of the proposed i-SEGOPubmed. Figure 1 Schematic diagram and information flow of the proposed i-SEGOPubmed.