Search

In this internet-based world where a large amount of information exists on the World Wide Web (WWW), the information retrieval systems are mainly centred on topic-based classification. Numerous efforts and time have been consumed for relevant information extraction through major search engines if the data is not organised correctly. Ontologies proved to be an effective technique for representing and retrieving information, which is the key idea in semantic web applications. Ontologies not only help in efficient knowledge representation and information retrieval, but it also helps in mapping the hidden knowledge about a subject. This paper discusses the process and method of building ontology on the "Energy access" domain. The methodology is based on the tools used in developing the ontology. Several tools are used to create an ontology. Protégé is a most popular tool for ontology editing and for developing ontology.1 In this paper, various aspects like super class and subclass hierarchy, creating a subclass, instances for class illustration, query retrieval process, visualisation, and graph views have been demonstrated by using protégé software. [ABSTRACT FROM AUTHOR]

During the last two decades, a systematic re-examination of the whole information science field has taken place around the FIS--Foundations of Information Science--initiative. With the occasion of its Fourth Conference in Beijing 2010, a group of selected contributors and leading practitioners of those fields have been invited to contribute to this Special Issue. What is the status of information science today? What is the relationship between information and the laws of nature? Is information merely "physical"? What is the difference between information and computation? Has the genomic revolution changed the contemporary views on information and life? And what about the nature of social information? Cogent answers to these questions and to quite many others are attempted in the contributions that follow. [ABSTRACT FROM AUTHOR]

This paper examines the decision points over the course of ten years of development of an institutional repository. Specifically, the focus is on the impact and influence from the open-source community, the needs of the local institution, the role that team dynamics plays, and the chosen platform. Frequently, the discussion revolves around the technology stack and its limitations and capabilities. Inherently, any technology will have several features and limitations, and these are important in determining a solution that will work for your institution. However, the people running the system and developing the software, and their enthusiasm to continue work within the existing software environment in order to provide features for your campus and the larger open-source community, will play a bigger role than the technical platform. These lenses are analyzed through three points in time: the initial roll out of our institutional repository, our long-term running and maintenance, and eventual new development and why we made the decisions we made at each of those points in time. [ABSTRACT FROM AUTHOR]

Query reformulation is a well-known technique intended to improve the performance of Information Retrieval Systems. Among the several available techniques, Query Expansion (QE) reformulates the initial query by adding similar terms, drawn from several sources (corpus, knowledge resources), to the query terms in order to retrieve more relevant documents. Most QE methods are based on the relationships between the original query term and candidate terms (new terms) in order to select the most similar expansion terms. In this paper, we suggested a new hybrid query reformulation through QE and term re-weighting techniques. The suggested approach aimed to demonstrate the effectiveness of QE with a semantic selection of candidate terms according to the specificity of original query terms in the improvement of retrieval performance. To this end, we exploited both relationships defined by knowledge resources and the distributed semantics, recently revealed by neural network analysis. For term re-weighting, we proposed a new semantic method based on semantic similarity measure that assigns a weight to each term of the expanded query. The conducted experiments on OHSUMED and TREC 2014 CDS test collections, including long and short queries, yielded significant results that outperformed the baseline and state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Proofs of Data Reliability are cryptographic protocols that provide assurance to a user that a cloud storage system correctly stores her data and has provisioned sufficient redundancy to be able to guarantee reliable storage service. In this paper, we consider distributed cloud storage systems that make use of erasure codes to guarantee data reliability. We propose two novel Proof of Data Reliability schemes, namely POROS and PORTOS, that on the one hand guarantees the retrieval of the outsourced data in their entirety through the use of proofs of data possession and on the other hand ensures the actual storage of redundancy. As a result, POROS and PORTOS are compatible with the current cloud computing model where the cloud autonomously performs all maintenance operations without any interaction with the user. Moreover, POROS and PORTOS delegate the burden of generating the redundancy to the cloud as well. The security of both solutions is proved in the face of a rational adversary whereby the cheating cloud provider tries to gain storage savings without increasing its total operational cost. [ABSTRACT FROM AUTHOR]

Implementation of ocean observation sensors are booming in recent years for encouraging research among coastal areas all over the world. This results in a copious amount of big data which makes it difficult for traditional data processing applications to manage them. The complexity in ocean observing community is heterogeneity and interpretation, which directs to a high-end information retrieval system. World Wide Web Consortium (W3C) spreads the usage of Semantic Web (SW) that provide easier way to search, reuse, combine and share information by integrating the data into a single platform. The use of semantic web in big data management helps to increase end-users ability for self-management of data from various sources, to handle the concepts and relationships of a domain and to manage the terminologies while connecting data from a varied data sources. This paper focuses on integrating big data with semantic web technology by developing a knowledge base system through ontology to solve the problem of heterogeneity in ocean observing communities. Ontology refers to a set of machine-readable controlled vocabularies which interprets big data by combining the data concepts with ontology classes. The proposed data model upgrades the information system in terms of improvising data analysis, discovery, retrieval and decision making. In addition to that, this paper also evaluates the quality of proposed ontology and found to be 39.28% improved in completeness, 45.29% reduced in structural complexity, 11% and 37.7% decreased in conciseness and correctness, respectively. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of maximizing the effectiveness of the ranking produced by information retrieval or recommender systems and at the same time maximizing two fairnesses, that of the group and that of the individual. The context of this paper is therefore that of access to information carried out by users, who aim to satisfy their own information needs, to documents produced by authors and curators who aim to be exposed in a fair manner, i.e. without discriminating between groups nor individuals. The paper describes a general method based on the spectral decomposition of mixtures of symmetric matrices, each of which represents a variable to be maximized, and experiments conducted with the use of a test collection. The method described in this paper has explained if and how the trade-offs between effectiveness, group fairness and individual fairness manifest themselves. The experimental results show that maintaining an acceptable level of effectiveness and fairness at the same time is feasible and (b) the trade-offs exist but the order of magnitude of the variations depends on the measure of effectiveness used and therefore by what the user's model of access to information is as well as on the fairness measures and therefore on how authors or editors should be exposed. • Modern information access systems should balance fairness and effectiveness. • A single eigensystem achieves simultaneous maximization. • Fairness, effectiveness, and access measures are crucial in trade-offs. [ABSTRACT FROM AUTHOR]

Objective: In 2002, the National Library of Medicine (NLM) introduced semi-automated indexing of Medline using the Medical Text Indexer (MTI). In 2021, NLM announced that it would fully automate its indexing in Medline with an improved MTI by mid-2022. This pilot study examines indexing using a sample of records in Medline from 2000, and how an early, public version of MTI's outputs compares to records created by human indexers. Methods: This pilot study examines twenty Medline records from 2000, a year before the MTI was introduced as a MeSH term recommender. We identified twenty higher- and lower-impact biomedical journals based on Journal Impact Factor (JIF) and examined the indexing of papers by feeding their PubMed records into the Interactive MTI tool. Results: In the sample, we found key differences between automated and human-indexed Medline records: MTI assigned more terms and used them more accurately for citations in the higher JIF group, and MTI tended to rank the Male check tag more highly than the Female check tag and to omit Aged check tags. Sometimes MTI chose more specific terms than human indexers but was inconsistent in applying specificity principles. Conclusion: NLM's transition to fully automated indexing of the biomedical literature could introduce or perpetuate inconsistencies and biases in Medline. Librarians and searchers should assess changes to index terms, and their impact on PubMed's mapping features for a range of topics. Future research should evaluate automated indexing as it pertains to finding clinical information effectively, and in performing systematic searches. [ABSTRACT FROM AUTHOR]

On account of prevalence of mobile devices, a variety of mobile-based services have been conducted recently. In this situation, it is possible to serve the information that fits to users' requests according to the users' context using mobile devices. Therefore, it is necessary to research data mining techniques with mobile devices. Therefore, we researched information retrieval system in mobile environment. We applied semantic data mining method with context information ontology. These techniques of semantic information retrieval can provide the information according to users' context information such as location, weather, user preference etc. exactly and rapidly anytime and anyplace. In modern society, it is essential to develop mobile information retrieval system which is efficient to users. Also, the system can provide users' current location and the location information with Google map APIs in mobile devices. Therefore, in this paper, we researched semantic data mining technique with context information ontology using mobile techniques. And we compared and evaluated our proposed model and the existing model in three areas and showed our proposed model has higher performance. [ABSTRACT FROM AUTHOR]

Query, expressing the user need and requirement, has an important role, in an information retrieval system, for reaching a high accuracy search. In this paper, we present an overview of the different refinement operations that the query may undergo, in the sake to enhance performance of an information retrieval system, such as: automatic query formulation through words prevision, query reformulation, query expansion, and query optimization. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The current practice of users searching for different types of geo-resources in a geographic area and wishing to identify the most convenient routes for visiting the most relevant ones, requires the iterative formulation of several queries: first to identify the more interesting resources and then to select the best route to visit them. In order to simplify this process, in this paper a novel functionality for a geographic information retrieval (GIR) system is proposed, which retrieves and ranks several routes for visiting a number of relevant georeferenced resources as a result of a single query, named flexible trip-planning query. An original retrieval model is defined to identify the relevant resources and to rank the most convenient routes by taking into account personal user preferences. To this end, a graph-based algorithm is defined, exploiting prioritized aggregation to optimize the routes' identification and ranking. The proposed algorithm is applied in the proof-of-concept of a Smart cOmmunity-based Geographic infoRmation rEtrievAl SysTem (SO-GREAT) designed to strengthen local communities: it collects and manages open data from regional authorities describing categories of authoritative territorial resources and services, such as schools, hospitals, etc., and from volunteered geographic services (VGSs) created by citizens to offer services in their neighbourhood. [ABSTRACT FROM AUTHOR]

Modeling information seeking behavior was started in the early 1980s pioneered by Wilson. More than 70 models in the last four decades on information seeking behavior have emerged. The models delineate the concepts from cognate subjects such as psychology and sociology. The authors made an analytical study of these models and identified many concepts of library and information science (LIS); from information processing and retrieval, reference service case studies and the evaluation experiments of information retrieval systems intrinsically are hidden in these models that have not been explicitly expounded as part of information seeking traits. The Chain Indexing devised by Ranganathan has an inclusive concept into information seeking with the phrase "Sought Link" to select the relevant subject heading(s) derived from the document(s) for indexing and to assign as the sought term(s) in the subject catalogue. Lancaster in the evaluation of MEDLARS identified phrases; serendipity and novelty ratio in response to users queries to the system. The paper after an in depth study of these ideas and with an analytical review of the models implies many of the classical concepts from LIS are implicit-in information seeking patterns of users. The paper conjures up to investigate them in the models of information seeking behavior as pragmatic rather only on user perceptions. [ABSTRACT FROM AUTHOR]

Information transmission and storage have gained traction as unifying concepts to characterize biological systems and their chances of survival and evolution at multiple scales. Despite the potential for an information-based mathematical framework to offer new insights into life processes and ways to interact with and control them, the main legacy is that of Shannon's, where a purely syntactic characterization of information scores systems on the basis of their maximum information efficiency. The latter metrics seem not entirely suitable for biological systems, where transmission and storage of different pieces of information (carrying different semantics) can result in different chances of survival. Based on an abstract mathematical model able to capture the parameters and behaviors of a population of single-celled organisms whose survival is correlated to information retrieval from the environment, this paper explores the aforementioned disconnect between classical information theory and biology. In this paper, we present a model, specified as a computational state machine, which is then utilized in a simulation framework constructed specifically to reveal emergence of a "subjective information", i.e., trade-off between a living system's capability to maximize the acquisition of information from the environment, and the maximization of its growth and survival over time. Simulations clearly show that a strategy that maximizes information efficiency results in a lower growth rate with respect to the strategy that gains less information but contains a higher meaning for survival. [ABSTRACT FROM AUTHOR]

The information retrieval system contains either a list of subject terms (taxonomy) or a list of collaborative tags (folksonomy) or both. The taxonomy and folksonomy come together as called hybrid subject devices. The main purpose of this paper is to apply machine learning techniques in the dataset from the library domain like others and analyse a large quantity of data for critical problems with accuracy. This research reveals to perform EDA (Exploratory data analysis), prediction analysis, and similarity measurement between folksonomy and taxonomy terms with new emerging technologies. Data science deals with big data that means unstructured data, messy data, a large volume of data. The size is of a large amount of data in terms of GB, TB. Machine learning tools manage this type of data. Usually, the Excel, or other spreadsheets package could not manage the file size in GB or TB, and that's why ML tools, and techniques are applied. At present, the library science domain also contains a large amount of data like 20/30 years of circulation data or subject descriptors, collaborative tags etc. Library professionals can apply machine learning tools for analysing this kind of data in the library domain. In this paper, the authors have introduced the applications of tools and techniques in the library domain and they have tested with 2642 taxonomy and folksonomy terms. This research work includes - EDA, prediction analysis, and similarity measurement of a folksonomy and taxonomy dataset. In the EDA part, the research work has performed a lot of analysis that includes frequency of LCSH (Library of Congress Subject Heading - taxonomy) terms, pair plots, joint plots, and heat map of LCSH and folksonomy terms. The logistic regression (LR) model for prediction analysis has been used in the folksonomy and taxonomy dataset. These 2642 terms of folksonomy and taxonomy both terms are taken as data for this research work. The EDA has been performed with the attributes in the dataset. The accuracy value of logistic regression (f1-score) is 0.37 at the training percentage of 69. The percentage of similarity between LCSH terms and folksonomy terms is 30 per cent (0.30151134), and the angle between these two vectors is 27 degrees. The novelty of this research work is that library data has been analysed using machine learning techniques the ever used before. [ABSTRACT FROM AUTHOR]

PURPOSE: Despite advances in molecular therapeutics, few anticancer agents achieve durable responses. Rational combinations using two or more anticancer drugs have the potential to achieve a synergistic effect and overcome drug resistance, enhancing antitumor efficacy. A publicly accessible biomedical literature search engine dedicated to this domain will facilitate knowledge discovery and reduce manual search and review. METHODS: We developed RetriLite, an information retrieval and extraction framework that leverages natural language processing and domain-specific knowledgebase to computationally identify highly relevant papers and extract key information. The modular architecture enables RetriLite to benefit from synergizing information retrieval and natural language processing techniques while remaining flexible to customization. We customized the application and created an informatics pipeline that strategically identifies papers that describe efficacy of using combination therapies in clinical or preclinical studies. RESULTS: In a small pilot study, RetriLite achieved an F 1 score of 0.93. A more extensive validation experiment was conducted to determine agents that have enhanced antitumor efficacy in vitro or in vivo with poly (ADP-ribose) polymerase inhibitors: 95.9% of the papers determined to be relevant by our application were true positive and the application's feature of distinguishing a clinical paper from a preclinical paper achieved an accuracy of 97.6%. Interobserver assessment was conducted, which resulted in a 100% concordance. The data derived from the informatics pipeline have also been made accessible to the public via a dedicated online search engine with an intuitive user interface. CONCLUSION: RetriLite is a framework that can be applied to establish domain-specific information retrieval and extraction systems. The extensive and high-quality metadata tags along with keyword highlighting facilitate information seekers to more effectively and efficiently discover knowledge in the combination therapy domain. [ABSTRACT FROM AUTHOR]

This paper presents ARCA, a software system that enables semantic search and exploration over a book catalog. The main purpose of this work is twofold: to propose a general paradigm for a semantic enrichment workflow and to evaluate a visual approach to information retrieval based on extracted information and existing knowledge graphs. ARCA has been designed and implemented following a user-centered design approach. Two different releases of the system have incrementally and iteratively developed and evaluated. The first release has evaluated the quality and usefulness of the extracted data. The second release, whose design was a refinement based on the previous evaluation results, was assessed by several users. Moreover, a comparative test with other information retrieval systems was conducted in order to study the potential added-value of the system. ARCA is employed in a real editorial scenario to visually search and explore the books of a publishing house. [ABSTRACT FROM AUTHOR]

Due to the tremendous use of computers, the need for dealing with digital information continues to grow. This has led to the need for constructing a structure for saving information in a way that eases data retrieval and processing. An information system is a table form that contains all the data needed for a user to reach a decision. Information systems are the most common form used for knowledge and data representation, and attribute reduction plays an important role in data processing. This paper is devoted to proposing a strategy for attributes reduction based on the similarity relation and pretopological concepts. Moreover, new types of pretopological spaces are to be constructed from the information system. Furthermore, a comparison between pretopologies and their pre-interiors constructed from the information system, as well as the different types of pretopological spaces, is investigated. Finally, the concept of the cover pretopology is applied to the information system. [ABSTRACT FROM AUTHOR]

Query expansion is an important approach utilized to improve the efficiency of data retrieval tasks. Numerous works are carried out by the researchers to generate fair constructive results; however, they do not provide acceptable results for all kinds of queries particularly phrase and individual queries. The utilization of identical data sources and weighting strategies for expanding such terms are the major cause of this issue which leads the model unable to capture the comprehensive relationship between the query terms. In order to tackle this issue, we developed a novel approach for query expansion technique to analyze the different data sources namely WordNet, Wikipedia, and Text REtrieval Conference. This paper presents an Improved Aquila Optimization-based COOT(IAOCOOT) algorithm for query expansion which retrieves the semantic aspects that match the query term. The semantic heterogeneity associated with document retrieval mainly impacts the relevance matching between the query and the document. The main cause of this issue is that the similarity among the words is not evaluated correctly. To overcome this problem, we are using a Modified Needleman Wunsch algorithm algorithm to deal with the problems of uncertainty, imprecision in the information retrieval process, and semantic ambiguity of indexed terms in both the local and global perspectives. The k most similar word is determined and returned from a candidate set through the top-k words selection technique and it is widely utilized in different tasks. The proposed IAOCOOT model is evaluated using different standard Information Retrieval performance metrics to compute the validity of the proposed work by comparing it with other state-of-art techniques. [ABSTRACT FROM AUTHOR]

PURPOSE: Despite advances in molecular therapeutics, few anticancer agents achieve durable responses. Rational combinations using two or more anticancer drugs have the potential to achieve a synergistic effect and overcome drug resistance, enhancing antitumor efficacy. A publicly accessible biomedical literature search engine dedicated to this domain will facilitate knowledge discovery and reduce manual search and review. METHODS: We developed RetriLite, an information retrieval and extraction framework that leverages natural language processing and domain-specific knowledgebase to computationally identify highly relevant papers and extract key information. The modular architecture enables RetriLite to benefit from synergizing information retrieval and natural language processing techniques while remaining flexible to customization. We customized the application and created an informatics pipeline that strategically identifies papers that describe efficacy of using combination therapies in clinical or preclinical studies. RESULTS: In a small pilot study, RetriLite achieved an F 1 score of 0.93. A more extensive validation experiment was conducted to determine agents that have enhanced antitumor efficacy in vitro or in vivo with poly (ADP-ribose) polymerase inhibitors: 95.9% of the papers determined to be relevant by our application were true positive and the application's feature of distinguishing a clinical paper from a preclinical paper achieved an accuracy of 97.6%. Interobserver assessment was conducted, which resulted in a 100% concordance. The data derived from the informatics pipeline have also been made accessible to the public via a dedicated online search engine with an intuitive user interface. CONCLUSION: RetriLite is a framework that can be applied to establish domain-specific information retrieval and extraction systems. The extensive and high-quality metadata tags along with keyword highlighting facilitate information seekers to more effectively and efficiently discover knowledge in the combination therapy domain. [ABSTRACT FROM AUTHOR]

Cross-language information cognitive retrieval has grown in importance as a study area due to the multilingual character of Internet resources and the diversifying languages spoken by users. This paper analyses and illustrates the key characteristics of Japanese literature from three aspects: ideological structure, structural form, and emotional expression, and makes a straightforward comparison with Chinese literature using a number of well-known Japanese literary masterpieces as examples. An important aspect of this literature is that it is full of lingering feelings, leaving room for the readers to imagine. The lingering charm is endless, obscure, and meaningful, which is the traditional style of Japanese literature. It also focuses on the depoliticization of Japanese literature, that is, the relative separation between literature and politics. The subjectivity of Japanese writing, which goes beyond just conveying feelings but involves less objective description, is related to this. The process of cross-language information retrieval in foreign nations is primarily separated into three stages, according to the changes in research objects. Currently, adding a language conversion mechanism to a monolingual information retrieval system serves as the primary language information retrieval solution. Currently, nearly 40% of the global popularity of literature comes from Japan. In this article, the background and concept of cross-language information retrieval are introduced, and its types, system models, and several key cross-language information retrieval methods are explained, and some solutions to the factors influencing the cross-language information retrieval effect are suggested. [ABSTRACT FROM AUTHOR]

The Information Retrieval user experience has remained largely unchanged since its inception for computers and mobile devices alike. However, recent developments in Virtual Reality hardware (pioneered by Oculus Rift in 2013) could introduce a new environment for Information Retrieval. This paper reports the results of a Scoping Literature Review (PRISMA-ScR) by rigorously examining the entire body of relevant literature with reproducible methods. The following research questions are answered: "What prototypes and concepts of Virtual Reality Information Retrieval systems with current generation hardware exist?", "How are user interaction and especially user input realised in these systems?", "What Retrieval features are used in these systems?", "How are search results displayed in these systems?" and "Can these VR IR systems compare to traditional (non-VR) IR systems?". After querying Google Scholar, Scopus and Web of Science, 1042 documents were reviewed in depth. Key features and attributes of the systems are summarised and discussed. Sketches of the user interfaces are included as well. The 30 documents that were relevant to the research questions include 16 distinct systems or theories. They discuss and utilise several user input technologies, ranging from controllers, voice input or hand tracking. Although conventional retrieval features are less common, systems enable retrieval of literature, 3D objects, images, books and texts and arrange them in a virtual space (e.g. as grids, arcs or maps). Finally, many of these systems were compared to conventional counterparts through user evaluation (n = 10). Most found user task times to be shorter or equal (n = 5, n = 3). In the seven papers that measured user performance (rate of correct solutions), three reported better performance (one equal). Notably, users always were more satisfied with the Virtual Reality systems compared to conventional ones. Possible limitations of these evaluations are demographic selection and the quality of baseline systems (control). [ABSTRACT FROM AUTHOR]

Information retrieval aims to retrieve the documents that answer users' queries. A typical search process consists of different phases for which a variety of components have been defined in the literature; each one having a set of hyper-parameters to tune. Different studies focused on how and how much the components and their hyper-parameters affect the system performance in terms of effectiveness, others on the query factor. The aim of these studies is to better understand information retrieval system effectiveness. This paper reviews the literature of this domain. It depicts how data analytics has been used in IR for a better understanding system effectiveness. This review concludes lack of full understanding of system effectiveness according to the context although it has been possible to adapted the query processing to some contexts successfully. This review also concludes that, even if it is possible to distinguish effective from non effective system on average on a query set, nor the system component analysis nor the query features analysis were successful in explaining when and why a particular system fails on a particular query. [ABSTRACT FROM AUTHOR]

Quantum cybersecurity is the study of all facets regarding the security of communication and computation in a distributed network. Significant developments in quantum technologies have outclassed their classical counterparts, thus envisioning the realization of a quantum internet. However, such quantum resources, in the hands of an adversary, can jeopardize network security. In this paper, we study two secure network connectivity concerns, namely, privacy and anonymity, in quantum information retrieval systems. To this end, we propose a state-of-the-art single-server multi-user quantum anonymous private information retrieval (QAPIR) protocol. To actualize this, we utilize anonymous entanglement as a quantum resource. We show that the QAPIR protocol not only provides privacy but also introduces anonymity as an added layer of security in quantum networks. Furthermore, we also detail a comparative security analysis that establishes the desirable properties of our proposal. [ABSTRACT FROM AUTHOR]

Since the end of the nineteenth century in U.S. libraries and research centers there has been a growth in book collections that, in 1944, is calculated as exponential. Growth that undergoes a sharp increase after World War II, noting, at the same time, a high rate of overlap between collections, which reduces the total holdings to about two-thirds of the world's published holdings. Using the technology of the time, based essentially on procedures for microfilming documents, proposals for deduplicating collections and reducing the amount of space occupied on the shelf are being developed from a lot of quarters, while electrical, or electronic, devices are being designed and, at times, realized combining the optical element, microfilms, with information retrieval techniques to achieve information discovery integrated with document retrieval. It was actually a series of devices based on the same operating principles, such as electrical photocells, microfilm and high-speed photography, invented, reinvented and reengineered, from which some prototypes were created, until, completely rethought by another inventor, one went into production and was successfully and commercially used. Some of the figures who gravitated around these devices have either remained in the shadows or have been forgotten: this paper, beyond documenting a pioneering period that was very significant for librarianship and for the developments of future information retrieval systems, aims at representing the importance and the role that these people played. [ABSTRACT FROM AUTHOR]

With the development of information technologies, more and more information retrieval tools, containing a wide variety of information, can be found. Internet resources, universal and specialized search engines, virtual libraries are becoming the main information retrieval tools for students. The article introduces an exploratory (pilot) research aiming to find out the differences of information retrieval between Lithuanian and Portuguese students. Šiauliai State College offers a unique study programme Information Management, having a signed contract of a double diploma with Porto (Portugal) Polytechnic Institute Porto Accounting and Business School (ISCAP). 10 Portuguese and 10 Lithuanian students participated in the research. The online questionnaire was forwarded to the students of ISCAP Porto Accounting and Business School and to the second year students of the study programme of Information Management at Šiauliai State College. The research revealed that the respondents Lithuanian students look for scientific papers and books, whereas Portuguese students search for basic information and scientific papers. All the respondents of the survey identified Google Scholar as the most important search engine for scientific information retrieval. [ABSTRACT FROM AUTHOR]

The World Wide Web(WWW) comprises a wide range of information, and it is mainly operated on the principles of keyword matching which often reduces accurate information retrieval. Automatic query expansion is one of the primary methods for information retrieval, and it handles the vocabulary mismatch problem often faced by the information retrieval systems to retrieve an appropriate document using the keywords. This paper proposed a novel approach of hybrid COOT-based Cat and Mouse Optimization (CMO) algorithm named as hybrid COOT-CMO for the appropriate selection of optimal candidate terms in the automatic query expansion process. To improve the accuracy of the Cat and Mouse Optimization (CMO) algorithm, the parameters are tuned with the help of the Coot algorithm. The best suitable expanded query is identified from the available expanded query sets also known as candidate query pools. All feasible combinations in this candidate query pool should be obtained from the top retrieved documents. Benchmark datasets such as the GOV2 Test Collection, the Cranfield Collections, and the NTCIR Test Collection are utilized to assess the performance of the proposed hybrid COOT-CMO method for automatic query expansion. This proposed method surpasses the existing state-of-the-art techniques using many performance measures such as F-score, precision, and mean average precision (MAP). [ABSTRACT FROM AUTHOR]

Purpose: Belkin and Robertson (1976a) reflected on the ethical implications of theoretical research in information science and warned that there was potential for abuse of knowledge gained by undertaking such research and applying it to information systems. In particular, they identified the domains of advertising and political propaganda that posed particular problems. The purpose of this literature review is to revisit these ideas in the light of recent events in global information systems that demonstrate that their fears were justified. Design/methodology/approach: The authors revisit the theory in information science that Belkin and Robertson used to build their argument, together with the discussion on ethics that resulted from this work in the late 1970s and early 1980s. The authors then review recent literature in the field of information systems, specifically information retrieval, social media and recommendation systems that highlight the problems identified by Belkin and Robertson. Findings: Information science theories have been used in conjunction with empirical evidence gathered from user interactions that have been detrimental to both individuals and society. It is argued in the paper that the information science and systems communities should find ways to return control to the user wherever possible, and the ways to achieve this are considered. Research limitations/implications: The ethical issues identified require a multidisciplinary approach with research in information science, computer science, information systems, business, sociology, psychology, journalism, government and politics, etc. required. This is too large a scope to deal with in a literature review, and we focus only on the design and implementation of information systems (Zimmer, 2008a) through an information science and information systems perspective. Practical implications: The authors argue that information systems such as search technologies, social media applications and recommendation systems should be designed with the recipient of the information in mind (Paisley and Parker, 1965), not the sender of that information. Social implications: Information systems designed ethically and with users in mind will go some way to addressing the ill effects typified by the problems for individuals and society evident in global information systems. Originality/value: The authors synthesize the evidence from the literature to provide potential technological solutions to the ethical issues identified, with a set of recommendations to information systems designers and implementers. [ABSTRACT FROM AUTHOR]

Background: Information specialists conducting searches for systematic reviews need to consider key questions around which and how many sources to search. This is particularly important for public health topics where evidence may be found in diverse sources. Objectives: The objective of this review is to give an overview of recent studies on information retrieval guidance and methods that could be applied to public health evidence and used to guide future searches. Methods: A literature search was performed in core databases and supplemented by browsing health information journals and citation searching. Results were sifted and reviewed. Results: Seventy‐two papers were found and grouped into themes covering sources and search techniques. Public health topics were poorly covered in this literature. Discussion: Many researchers follow the recommendations to search multiple databases. The review topic influences decisions about sources. Additional sources covering grey literature eliminate bias but are time‐consuming and difficult to search systematically. Public health searching is complex, often requiring searches in multidisciplinary sources and using additional methods. Conclusions: Search planning is advisable to enable decisions about which and how many sources to search. This could improve with more work on modelling search scenarios, particularly in public health topics, to examine where publications were found and guide future research. [ABSTRACT FROM AUTHOR]

Medical images are growing dramatically both in quantity and application in the data era and the emerging Big Data problem. Searching and finding the proper medical image among such a huge number of medical images is not possible unless using a proper medical search engine. Indexing is the backend process in information retrieval systems in which documents are annotated with the index entry to be retrieved more accurately and efficiently. Most of the indexing techniques for medical images are content-based that are more complex and time consuming than the text-based ones. In this paper, a text-based medical image indexing technique is proposed that use medical images' attributes and fragments them with the hybrid fragmentation approach (that are used in distributed database design) and re-form each of such attribute fragments into a hierarchy, constructing a multidimensional index. Hybrid fragmentation approach uses both Horizontal and vertical fragmentation of medical images' attributes provided in header of medical image standard formats (such as the DICOM). Horizontal fragmentation uses values of image attributes (i.e., image content and properties dependent), whilst the vertical fragmentation uses pairwise affinity and correlation of the attributes in the application domain (i.e., application dependent). So, the proposed hybrid fragmentation approach based indexing of medical images aim to consider both the image properties and application statistics together to provide a better functionality. As the experimental performance evaluation results illustrate, the proposed multidimensional indexing can provide better precision of information retrieval rather that a single index or a set of multiple indexes, since that considers semantic relationship of the medical image's attributes via the hybrid (horizontal and vertical) fragmentation. Moreover, the hybrid fragmentations approach based indexing also outperforms the vertical fragmentation-based multidimensional medical image indexing technique in terms of precision, recall and response time. [ABSTRACT FROM AUTHOR]

Laboratory services form an integral part of medical care in the decision-making of physicians, including those working at ambulatory care centers. Information exchange is essential between ambulatory care centers and laboratories. Inevitable errors have always existed in the exchange of such information on paper, which can be to some extent avoided by developing appropriate computer-based interfaces. Therefore, this review aimed to examine studies conducted to determine the effect of electronic communication between ambulatory care centers and laboratories. This systematic review was conducted on the basis of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Studies were searched in the PubMed, Embase, Cochrane, and Web of Science, and those written in English and published between 2000 and February 2019 with full texts available were selected. From a total of 3898 papers retrieved from the studied databases, 24 papers were eligible for entering this study after removing similar and nonrelated studies. Electronic exchanges between ambulatory care centers and laboratories can have numerous benefits in terms of financial, organizational, and quality. This evidence for the value of electronic communications is an important factor contributing to its local investment and adoption. [ABSTRACT FROM AUTHOR]

Rules in artificial intelligence are able to provide inference facilities for making effective decisions in information retrieval. In such a scenario, a keyword-based information retrieval systems use the keywords as indexes generated by Web crawlers without considering semantics. Moreover, many Web search engines perform re-ranking based on relevance feedback. However, the relevant documents in e-learning applications must be stored in local repositories and must be updated dynamically based on the level of the users. Therefore, it is necessary to have an information retrieval system which performs relevant information extraction and stores such relevant information dynamically in local e-learning repositories. In order to address these issues, a new information retrieval and local storage system is proposed in this paper by applying rules for making effective decisions in the storage and retrieval algorithms which are newly proposed in this paper. For this purpose, two new algorithms called intelligent rule-based relevant information retrieval algorithm with semantics and a secured information storage using semantic knowledge representation are proposed in this paper for effectively retrieving the e-learning contents from the Web on computer science subject and to store them in local repositories with semantic indexing. The major advantages of the proposed information retrieval system include increase in accuracy, reduction in retrieval time and effective storage in local repositories for further use. [ABSTRACT FROM AUTHOR]

Access to public information (that generated by public institutions) is a right recognized by both European (Unión Europea, 2001) and Spanish legislation (España, 1992; 1997; 2013). The bodies of the Spanish State must to make available to the citizens, free of charge and in a transparent way, all the documents that they generate, unless there are national security reasons. Many national, regional and local governments have created information systems or public information platforms in the form of newsletters or gazettes. The purpose of this paper is to find out how is, on a documentary level, the main informative gazette of Spain, the Boletín oficial del Estado (BOE). The metadata of the published documents are studied and the metadata used for documentary description are analyzed in a deeper way. The results reflect the absence of documentary descriptors in more than 89% of the total documentary BOE' corpus. In recent years this figure has barely exceeded 50%. The paper ends with a few recommendations for improvement. [ABSTRACT FROM AUTHOR]

Ranking models are the main components of information retrieval systems. Several approaches to ranking are based on traditional machine learning algorithms using a set of hand-crafted features. Recently, researchers have leveraged deep learning models in information retrieval. These models are trained end-to-end to extract features from the raw data for ranking tasks, so that they overcome the limitations of hand-crafted features. A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking. In this paper, we compare the proposed models in the literature along different dimensions in order to understand the major contributions and limitations of each model. In our discussion of the literature, we analyze the promising neural components, and propose future research directions. We also show the analogy between document retrieval and other retrieval tasks where the items to be ranked are structured documents, answers, images and videos. [ABSTRACT FROM AUTHOR]

Retrieving required medical images from a huge amount of images is one of the most widely used features in medical information systems, including medical imaging search engines. For example, diagnostic decision making has traditionally been accompanied by patient data (image or non-image) and previous medical experiences from similar cases. Indexing as part of search engines (or retrieval system), increases the speed of a search. The goal of this study, is to provide an effective and efficient indexing technique for medical images search engines. In this paper, in order to archive this goal, a multidimensional indexing technique for medical images is designed using the normalization technique that is used to reduce redundancy in relational database design. Data structure of the proposed multidimensional index and also different required operations are designed to create and handle such a multidimensional index. Time complexity of each operation is analyzed and also average memory space required to store any medical image (along with its related metadata) is calculated as the space complexity analysis of the proposed indexing technique. The results show that the proposed indexing technique has a good performance in terms of memory usage, as well as execution time for the usual operations. Moreover, and may be more important, the proposed indexing techniques improves the precision and recall of the information retrieval system (i.e., search engine) which uses this technique for indexing medical images. Besides, a user of such search engine can retrieve medical images which s/he has specified its attributes is some different aspects (dimensions), e.g., tissue, image modality and format, sickness and trauma, etc. So, the proposed multidimensional indexing techniques can improve effectiveness of a medical image information retrieval system (in terms of precision and recall), while having a proper efficiency (in terms of execution time and memory usage), and can improve the information retrieval process for healthcare search engines. [ABSTRACT FROM AUTHOR]

This paper investigates error correction in two‐dimensional (2‐D) intersymbol interference (ISI) channels using polar codes. 2‐D channels offer high data storage capacity compared to traditional one‐dimensional (1‐D) channels but suffer from greater ISI effects. Error correction codes can be used to mitigate the effects of ISI in 2‐D data storage systems and improve their performance and reliability. Polar codes achieve the symmetric capacity for the class of binary‐input discrete memoryless channels (B‐DMCs). A post‐processing technique is proposed to improve the performance of polar codes in the 2‐D ISI channel. The simplified successive cancellation (SSC) polar decoder is useful for data storage systems owing to its good waterfall region performance, low error floors and good throughput rates and is therefore adopted for the study. Simulation results indicate that the proposed technique has an error correction performance comparable to some existing decoders and has better performance than other decoding techniques. [ABSTRACT FROM AUTHOR]

Over the last decade, a huge number of medical visual data are widely used for diagnose, treatment, and follow-up. Retrieving needed medical image(s) from a huge number of images is one of the most widely used features in medical information systems, especially in medical image search engines. Indexing as part of search engines (or information retrieval systems), increases the speed (efficiency) of search and the information retrieval process. In this paper, a multidimensional indexing technique for medical images is presented that can improve effectiveness and efficiency of medical image search engines. The structure of the proposed multi-dimensional indexing technique and its main operations (i.e., creation, insertion, deletion and search) is designed and evaluated. In order to create this multidimensional index, the "vertical fragmentation" approach (which is usually applied for distributed database design) is used to determine the each of dimensions; roughly speaking, dimensions are different aspects of the medical images for a/some information need (e.g., image type and format, type of disorder, etc.). Accordingly, data structure of the proposed multidimensional indexing technique (which is named "Diamond") is formed by using the vertical fragmentation of medical image attributes (to differentiate the dimensions), and then using agglomerative hierarchical clustering to build up the hierarchy in each dimension. The proposed multi-dimensional indexing technique is implemented using the open-source search engine Lucene and compared with the built-in indexing technique available in the Lucene search engine, and also with the Terrier Platform (available for the benchmarking of information retrieval systems). In this evaluation, efficiency and effectiveness measures of the proposed multidimensional indexing technique (Diamond) are evaluated experimentally, beside the analysis of the designed data structure and its operations. For the experimental evaluation data set, images from Tabriz Behbood Hospital and a subset of TCIA images were used. Experimental evaluation results show that Diamond, the proposed multidimensional indexing technique improves both efficiency and effectiveness for a medical image search engine. [ABSTRACT FROM AUTHOR]

Distracted driving due to smartphone use is one of the key reasons for road accidents. However, the 6G super-heterogeneous network systems and highly differentiated application scenarios require highly elastic and endogenous information services involving the use of smart apps, and related information retrieval by drivers in modern Vehicle-to-People (V2P) Networks. The tension raised due to the conflicting attention requirements of driving and information retrieval can be resolved by designing information retrieval solutions that demand minimal user interaction. In this paper, we construct a Personalized Search Query Generator (PSQG) to reduce driver-mobile interaction during information retrieval in the 6 G era. This system has a query generator and a query recommendation component that update two sets of relationships dynamically: one is the query and the title, another is search and recommendation. The proposed system learns a user's intent based on historical query records and recommends personalized queries, thus reducing the driver-mobile interaction time. We deploy the system into a real search engine and conduct several online experiments. These experiments are conducted using a custom constructed dataset comprising ten million samples. We use the BLEU-score metric and perform A/B testing. The results demonstrate that our system can assist users in making precise queries efficiently. The proposed system can improve drivers’ safety if used in smartphones and other information retrieval systems in vehicles. [ABSTRACT FROM AUTHOR]

In essence, information retrieval is a cognitive process, which is the matching process between the reader's or user's cognition and the system's pre-set cognition. At present, cognitive view has become a research hotspot in the field of information retrieval. The research of information retrieval cognitive model has laid a theoretical foundation for the development of information retrieval system. Based on scarcity theory, this paper analyses the dynamic change of user's cognitive ability and constructs a cognitive model of information retrieval based on scarcity theory. After analysis, this paper evaluates the effect of information retrieval cognitive model based on scarcity theory, and clarifies the specific problems that the model can solve. [ABSTRACT FROM AUTHOR]

Interval scales are assumed by several basic descriptive statistics, such as mean and variance, and by many statistical significance tests which are daily used in IR to compare systems. Unfortunately, so far, there has not been any systematic and formal study to discover the actual scale properties of IR measures. Therefore, in this paper, we develop a theory of Information Retrieval (IR) evaluation measures, based on the representational theory of measurements, to determine whether and when IR measures are interval scales. We found that common set-based retrieval measures—namely Precision, Recall, and F-measure—always are interval scales in the case of binary relevance while this happens also in the case of multi-graded relevance only when the relevance degrees themselves are on a ratio scale and we define a specific partial order among systems. In the case of rank-based retrieval measures—namely AP, gRBP, DCG, and ERR—only gRPB is an interval scale when we choose a specific value of the parameter $p$ and define a specific total order among systems while all the other IR measures are not interval scales. Besides the formal framework itself and the proof of the scale properties of several commonly used IR measures, the paper also defines some brand new set-based and rank-based IR evaluation measures which ensure to be interval scales. [ABSTRACT FROM AUTHOR]

The transfer of knowledge has not changed fundamentally for many hundreds of years: It is usually document-based-formerly printed on paper as a classic essay and nowadays as PDF. With around 2.5 million new research contributions every year, researchers drown in a flood of pseudo-digitized PDF publications. As a result research is seriously weakened. In this article, we argue for representing scholarly contributions in a structured and semantic way as a knowledge graph. The advantage is that information represented in a knowledge graph is readable by machines and humans. As an example, we give an overview on the Open Research Knowledge Graph (ORKG), a service implementing this approach. For creating the knowledge graph representation, we rely on a mixture of manual (crowd/expert sourcing) and (semi-)automated techniques. Only with such a combination of human and machine intelligence, we can achieve the required quality of the representation to allow for novel exploration and assistance services for researchers. As a result, a scholarly knowledge graph such as the ORKG can be used to give a condensed overview on the state-of-the-art addressing a particular research quest, for example as a tabular comparison of contributions according to various characteristics of the approaches. Further possible intuitive access interfaces to such scholarly knowledge graphs include domain-specific (chart) visualizations or answering of natural language questions. [ABSTRACT FROM AUTHOR]

Health-related data is stored in a number of repositories that are managed and controlled by different entities. For instance, Electronic Health Records are usually administered by governments. Electronic Medical Records are typically controlled by health care providers, whereas Personal Health Records are managed directly by patients. Recently, Blockchain-based health record systems largely regulated by technology have emerged as another type of repository. Repositories for storing health data differ from one another based on cost, level of security and quality of performance. Not only has the type of repositories increased in recent years, but the quantum of health data to be stored has increased. For instance, the advent of wearable sensors that capture physiological signs has resulted in an exponential growth in digital health data. The increase in the types of repository and amount of data has driven a need for intelligent processes to select appropriate repositories as data is collected. However, the storage allocation decision is complex and nuanced. The challenges are exacerbated when health data are continuously streamed, as is the case with wearable sensors. Although patients are not always solely responsible for determining which repository should be used, they typically have some input into this decision. Patients can be expected to have idiosyncratic preferences regarding storage decisions depending on their unique contexts. In this paper, we propose a predictive model for the storage of health data that can meet patient needs and make storage decisions rapidly, in real-time, even with data streaming from wearable sensors. The model is built with a machine learning classifier that learns the mapping between characteristics of health data and features of storage repositories from a training set generated synthetically from correlations evident from small samples of experts. Results from the evaluation demonstrate the viability of the machine learning technique used. [ABSTRACT FROM AUTHOR]

The lack of Interoperable healthcare data presents a major challenge, towards achieving ubiquitous health care. The plethora of diverse medical standards, rather than common standards, is widening the gap of interoperability. While many organizations are working towards a standardized solution, there is a need for an alternate strategy, which can intelligently mediate amongst a variety of medical systems, not complying with any mainstream healthcare standards while utilizing the benefits of several standard merging initiates, to eventually create digital health personas. The existence and efficiency of such a platform is dependent upon the underlying storage and processing engine, which can acquire, manage and retrieve the relevant medical data. In this paper, we present the Ubiquitous Health Profile (UHPr), a multi-dimensional data storage solution in a semi-structured data curation engine, which provides foundational support for archiving heterogeneous medical data and achieving partial data interoperability in the healthcare domain. Additionally, we present the evaluation results of this proposed platform in terms of its timeliness, accuracy, and scalability. Our results indicate that the UHPr is able to retrieve an error free comprehensive medical profile of a single patient, from a set of slightly over 116.5 million serialized medical fragments for 390,101 patients while maintaining a good scalablity ratio between amount of data and its retrieval speed. [ABSTRACT FROM AUTHOR]

In this paper, we perform an in-depth study into how clinicians represent their information needs and the influence this has on information retrieval (IR) effectiveness. While much research in IR has considered the effectiveness of IR systems, there is still a significant gap in the understanding of how users contribute to the effectiveness of these systems. The paper aims to contribute to this by studying how clinicians search for information. Multiple representations of an information need-from verbose patient case descriptions to ad-hoc queries-were considered in order to understand their effect on retrieval. Four clinicians provided queries and performed relevance assessment to form a test collection used in this study. The different query formulation strategies of each clinician, and their effectiveness, were investigated. The results show that query formulation had more impact on retrieval effectiveness than the particular retrieval systems used. The most effective queries were short, ad-hoc keyword queries. Different clinicians were observed to consistently adopt specific query formulation strategies. The most effective queriers were those who, given their information need, inferred novel keywords most likely to appear in relevant documents. This study reveals aspects of how people search within the clinical domain. This can help inform the development of new models and methods that specifically focus on the query formulation process to improve retrieval effectiveness. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to report the findings of a systematic literature review carried out to investigate the applications and potential roles of thesaurus in digital libraries. It also describes the process of systematic literature review (SLR) followed to carry out this study. 466 unique publications from Library and Information Science Abstracts (LISA) and Scopus had been subjected to SLR, and 29 papers have been analysed. Various possible applications of thesaurus in digital libraries have been identified and described. Also different approaches followed by researchers in choice of the thesaurus have been identified. Search and browse functionalities offered by thesaurus to the users of digital libraries are described. The discussion presented in this paper can play as cues for the digital library administrators in decision making towards implementing thesaurus for enhanced information retrieval. This study would allow researchers of information retrieval systems, including that of digital libraries, to proceed from more informed standpoints. Also, the discussion can be used to evaluate and improve education in library and information science. [ABSTRACT FROM AUTHOR]

Digital data concerning the flora of Italy are largely fragmented among different resources hosted on different platforms, and often with different data standards, which are neither connected by a common access point, nor by web services, thus constituting a relevant obstacle to data access and usage. Taxonomic incongruences add a further complication. This paper describes "FlorItaly", an online information system which allows to access and query updated information on the checklist of the flora of Italy, aiming at becoming an aggregator for Italian botanical resources. "FlorItaly" was developed in a collaborative effort by more than 50 taxonomists, with the support of the Italian Botanical Society, and of Project "Dryades" (University of Trieste), to provide a better and reliable organization of botanical knowledge in Italy, as well as a relevant simplification for data retrieval, and a further stimulus towards a more collaborative approach in botanical research. [ABSTRACT FROM AUTHOR]

Telecare medical information system (TMIS) is substantially desir- able to patients by allowing them to remotely access medical services. Authentication schemes for TMIS try to ensure secure and autho- rized access. Recently, some smart card-based password authentica- tion schemes have been proposed, but privacy is not duly addressed. In this paper, we propose a key agreement technique for a medical server environment to overcome the limitations of the existing schemes. In the proposed scheme, we suggest bringing together the advance of both the crypto and Information Retrieval (IR) community to enhance the health-care process between patients and doctors by supporting an ef- ficient communicating platform on the internet which overcomes some drawbacks in traditional health-care services. However, to keep pa- tient medical data confidential against untrusted patients, we apply cryptographic methods by disclosing the data decryption key only to authorized patients and doctors to retrieve medical files containing certain keywords. We show that the proposed scheme is secure in the random oracle model (ROM) under the Bilinear Diffie-Hellman Problem BDHP. [ABSTRACT FROM AUTHOR]

Evaluation of information retrieval systems is a fundamental topic in Library and Information Science. The aim of this paper is to connect the system-oriented and the user-oriented approaches to relevant philosophical schools. By reviewing the related literature, it was found that the evaluation of information retrieval systems is successful if it benefits from both system-oriented and user-oriented approaches (composite). The system-oriented approach is rooted in Parmenides' philosophy of stability (immovable) which Plato accepts and attributes to the world of forms; the user-oriented approach is rooted in Heraclitus' flux philosophy (motion) which Plato defers and attributes to the tangible world. Thus, using Plato's theory is a comprehensive approach for recognizing the concept of relevance. The theoretical and philosophical foundations determine the type of research methods and techniques. Therefore, Plato's dialectical method is an appropriate composite method for evaluating information retrieval systems. [ABSTRACT FROM AUTHOR]