Networked information discovery and retrieval tools: security capabilities and needs

Indexing and abstracting are like Siamese twins in the information retrieval process. Indexing and abstracting are the two approaches to distilling information content into an abbreviated, but comprehensive representation of an information resource(s). They are knowledge organisation tools which usually provide detailed and accurate maps and road signs in the information superhighway. Digital libraries are characterised by an electronic stock of information which can be accessed via computers, and are extension and augmentations of physical libraries in digital forms. They are information retrieval systems (a device interposed between a potential user of information and the information itself) which provide opportunities to access and retrieve information that is often accessible for a variety of reasons. This chapter presents a literature review on indexing and abstracting, information retrieval process, digital libraries pointing out the importance of indexing and abstracting in the information retrieving process and then highlighting the roles played by indexing and abstracting as tools for information retrieval in digital libraries. The chapter posits that indexing and abstracting plays a significant role as information retrieval tools in digital libraries.

Chapter 5 - Information discovery and information literacy

An overview of resource discovery services currently available on the Internet is presented. The authors concentrate on the following discovery tools: the Wide Area Information Servers (WAIS) project, Archie, Prospero, Gopher. The World-Wide Web (WWW), Netfind, the X.500 directory, Indie, the Knowbot Information Service (KIS), Alex, Semantic File Systems, and Nomenclator. The authors summarize the surveyed tools by presenting a taxonomy of their characteristics and design decisions. They also describe where to find and how to access several of the surveyed discovery services. They conclude with a discussion of future directions in the area of resource discovery and retrieval. >

In this paper we focus on the design and implementation of low cost, cross language and cross platform Information Retrieval and Documentation tools capable for the collection, organization and administration of unstructured and semi-structured information imported from various sources. A modular Computer Assisted Information Resources Navigation (CAIRN) software architecture is proposed and the requirements of each module are presented. A discussion of the implementation is based on the experimentation with a prototype of such a software tool. The technologies that are incorporated into the modern operating systems and the opportunities that they offer for implementing the modules of the CAIRN architecture are also examined and evaluated. Some of these technologies are common / independent from the operating systems, while some others are distinctive. In this latter case we face barriers (restrictions) for a straightforward implementation of the CAIRN software systems to the whole range of desktop operating systems (e.g. Windows, Mac OS, Linux, Solaris). Some alternative technologies are presented to avoid this serious constraint. The evaluation of the implementation effort is also discussed and eventually some conclusions and future plans for further improvement of the CAIRN architecture are given.

Objective. The objective of the paper is to show the discovery service from the perspective of the library and user values as a means of access to information. Methods. The literature review on the subject of value approach to the library functioning is provided. In the paper were used data from the user experience study, statistical reports and anecdotal experience. The results show the issues of customization of discovery services functionalities and resources management required. The users’ evaluation of the discovery service as a tool for finding and retrieval of information is also given. Conclusions. Discovery services have special functions and are powerful tools for identifying the required information, which is placed in directories and databases, online libraries, and external full-text databases. The study found that finding relevant information was of paramount importance to users who highly appreciated the usefulness of this service for their research.

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.

In this work, we introduce a new method for information extraction from the semantic web. The fundamental idea is to model the semantic information contained in the microformats of a set of web pages, by using a data structure called semantic network. Then, we introduce a novel technique for information extraction from semantic networks. In particular, the technique allows us to extract a portion—a slice—of the semantic network with respect to some criterion of interest. The slice obtained represents relevant information retrieved from the semantic network and thus from the semantic web. Our approach can be used to design novel tools for information retrieval and presentation, and for information filtering that was distributed along the semantic web.

The birth of the internet in the 1990‟s did not only change the way of how people communicate but also of how they access information. The amount of information that the internet offers is enormous, it grows by the day and can be retrieved at any time – only technical issues may interrupt its constant availability. That is why the World Wide Web is nowadays the first port for information retrieval. Information retrieval tools are indispensible in this environment. They filter the information mass and allow users to locate the desired information.Search engines are one of the most popular web services.Most of what is known about user search engine behavior comes from query log analysis and clickthrough analysis. Only few studies have so far examined search engine behavior and user satisfaction from a mere user perspective.The number still shrinks when it comes to narrowing down the research focus on users of a specific country or language community. This study investigates user search engine behavior and satisfaction from the perspective of Chinese-speaking users in Malaysia. A survey made among 62 Malaysian Chinese users assesses search engine usage frequency, preferred search engines, and user priorities for search engine selection. It also assesses linguistic aspects such as preferred search language and search engine interface language. The study evaluates the users‟ satisfaction with the search results in general and the search results on the first page, with the search results of English and Chinese search queries, andthe necessity for query rephrasing. Where possible, the study compares the results with existing studies conducted in Malaysia.

The was conducted to investigate utilization of Artificial Intelligence for information retrieval by clinical students at Rasheed Shekoni Teaching Hospital, Federal University Dutse, Jigawa State, Nigeria. The study employs quantitative method using cross-sectional design. The population of the study comprises of clinical students in the hospital which are (267) drawn from level 400, 500 and 600. Census sampling technique where all the respondents served as the sample size, structured questionnaire was used to collect data. Descriptive statistics was used to analyze the data using SPSS version 25.0. The study revealed that 123 (50%) of the respondents had high competency. The study revealed the clinical students don’t knowledge about the advantages of using AI tools for information retrieval with mean score of 1.89. The revealed that 156 (63%) used ChatGPT, 119 (48%) used Google Bard, 133 (54%) used Microsoft Copilot, 127 (51%) used Shadow Health and 138 (56%) used HealthTap AI. The also revealed that more than half of the clinical students encountered in using AI tools for information retrieval. The study concluded that the clinical students are not aware on the use of AI tools for information retrieval. The study recommends that the university should incorporate structured training modules on Artificial Intelligence into the clinical training curriculum which can improve students’ awareness, understanding and practical skills in using AI for medical information retrieval and the hospital management should invest in providing licensed access to a variety of AI platforms relevant to healthcare, such as ChatGPT, HealthTap AI and other specialized medical AI tools, to expand students’ exposure beyond commonly used platforms.

Because of the high impact of high-tech digital crime upon our society, it is necessary to develop effective Information Retrieval (IR) tools to support digital forensic investigations. In this paper, we propose an IR system for digital forensics that targets emails. Our system incorporates WordNet (i.e. a domain independent ontology for the vocabulary) into an Extended Boolean Model (EBM) by applying query expansion techniques. Structured Boolean queries in Backus-Naur Form (BNF) are utilized to assist investigators in effectively expressing their information requirements. We compare the performance of our system on several email datasets with a traditional Boolean IR system built upon the Lucene keyword-only model. Experimental results show that our system yields a promising improvement in retrieval performance without the requirement of very accurate query keywords to retrieve the most relevant emails.

The Agency for Healthcare Research and Quality (AHRQ) National Resource Center for Health Information Technology (NRC) created the Health IT Bibliography that contains peer-reviewed articles in eleven different health informatics categories. To create the bibliography, informatics experts identified what they considered the seminal articles in each category. Using the same eleven categories, an expert searcher (librarian) compiled a list of the "best" health informatics articles using information seeking and retrieval tools. The two sets of articles were then compared using high citation counts as a measure of value. The expert searcher set (8,230) contained more than 3 times the citations to chosen articles compared to the content expert set (2,382). Of 60 articles, 27% of those articles (n = 16) were included in both sets. The frequently cited journals were similar for both sets, and one-third of the same authors were cited in both sets. While citation counts and the timeliness of the articles differed in the two sets, the same authors and same journals were frequently present in both sets. A best practice for locating high-quality articles may be collaboration between expert searchers and content experts.

Hypermedia systems have become increasingly popular as tools for user-driven learning and information retrieval. This paper describes a dynamic linkage method, by which links of hypermedia change according to the difference in users and users' behaviors. Although some researches have been made in this field of adaptive hypermedia, our approach differs in that our method deals with both path history and user parameter at the same time, and the two are based on rules expressed as logical equations. A prototype system for CAI was developed based on the proposed method and was evaluated by some experiments.

Most national Spatial Data Infrastructures (SDI) were built based on a clearinghouse concept. The concept requires all of the service providers to maintain a Web (map) service recourse which leads to duplication of efforts from data collectors. The objectives of developing a geo-service broker are to make data services and other services within an SDI centrally organized, available and accessible to consumers. A geo-service broker offers a centralized and harmonized view on resources. The objective of this research is to investigate how a geo-service broker is built as a tool for intelligent search and information retrieval in the semantic Web for better service delivery of geo-services over the Internet.

Artificial intelligence (AI), manifested in the forms of technologies, systems, tools, and applications, has advanced rapidly, especially in recent years. It has permeated many aspects of human behavior and nearly all sectors of society, such as healthcare and education. In the context of early childhood education (ECE), AI has afforded valuable opportunities that directly and indirectly enhance children’s learning and development. While there are already two existing reviews of the literature on AI in ECE, they show either a lack of descriptive information concerning selected studies or inconsistencies between inclusion/exclusion criteria and selected studies, thereby raising concerns about their rigor. Representing a more methodologically rigorous effort and a significant contribution to the field of AI in ECE, this scoping study aimed to achieve three main goals: (1) “mapping” the global landscape of the current extent, range, and nature of relevant studies on the affordances of AI for use in ECE, (2) identifying potential research gaps, and (3) charting future research directions. Specifically, it addressed this overarching research question: What is the global landscape of the current state of knowledge concerning the affordances of AI for use in ECE? Specifically, the state of knowledge here refers to three aspects: (1) extent, (2) range, and (3) nature. First, regarding the extent aspect, the empirical knowledge was derived from 18 research articles in 11 countries and 16 peer-reviewed academic journals between 2005 and 2023, with 14 of these articles published in the past four years (2020–2023). Second, with respect to the range of study populations, it covered 15,081 children in early childhood (ages 2 to 8 years) across these 11 countries. Third, thematic analysis of these studies revealed four areas of AI affordances: (1) AI as tangible and intangible tools for interactive learning and information retrieval, (2) AI as technology for predicting/classifying children’s conditions, (3) AI as the object for learning by adapting to and personalizing children’s learning, and (4) AI as the subject for children's learning about it. Based on these findings, this scoping review identified three research gaps for future studies: (1) interviewing and/or surveying education stakeholders (parents, educators, policymakers) to explore the affordances of appropriate AI for use with, by, and for children bearing ethical considerations; (2) conducting group comparisons to investigate contextual factors contributing to the “AI divide” among children from different socioeconomic backgrounds; and (3) comparing sociocultural influences on AI use in ECE across cultures.

Digital imaging is becoming a powerful tool for data storage and information retrieval. Image comparison and similarity evaluation has become part of the information market and it is today a common part of, for example, web search engines. The cork oak tree (Quercus suber L.), the dominant species of the ‘montado’ woodland system is, due to its cultural and socio-economic value, protected by law that prevents extensive destructive studies on an essential part of the tree—the root. Especially in the Mediterranean zone, where the water is the limiting growth factor, the root development studies are of significant interest. In this work we present a method of using digital images for cork oak coarse root systems-evaluation by means of digital imaging. Acquired images of structural roots are processed automatically to prevent subjective decisions by the human observer. The performance of the method, its potential for semantic retrieval and similarity assessment is demonstrated, having as example eight young cork oak root systems, and critical issues for evaluation and conclusion-making, are discussed.