Search (23 results, page 1 of 2)

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Abstract

Purpose This paper offers a definition of the core of information science, which encompasses most research in the field. The definition provides a unique identity for information science and positions it in the disciplinary universe. Design/methodology/approach After motivating the objective, a definition of the core and an explanation of its key aspects are provided. The definition is related to other definitions of information science before controversial discourse aspects are briefly addressed: discipline vs. field, science vs. humanities, library vs. information science and application vs. theory. Interdisciplinarity as an often-assumed foundation of information science is challenged. Findings Information science is concerned with how information is manifested across space and time. Information is manifested to facilitate and support the representation, access, documentation and preservation of ideas, activities, or practices, and to enable different types of interactions. Research and professional practice encompass the infrastructures - institutions and technology -and phenomena and practices around manifested information across space and time as its core contribution to the scholarly landscape. Information science collaborates with other disciplines to work on complex information problems that need multi- and interdisciplinary approaches to address them. Originality/value The paper argues that new information problems may change the core of the field, but throughout its existence, the discipline has remained quite stable in its central focus, yet proved to be highly adaptive to the tremendous changes in the forms, practices, institutions and technologies around and for manifested information.

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Abstract

Human brain size nearly quadrupled in the six million years since Homo last shared a common ancestor with chimpanzees, but human brains are thought to have decreased in volume since the end of the last Ice Age. The timing and reason for this decrease is enigmatic. Here we use change-point analysis to estimate the timing of changes in the rate of hominin brain evolution. We find that hominin brains experienced positive rate changes at 2.1 and 1.5 million years ago, coincident with the early evolution of Homo and technological innovations evident in the archeological record. But we also find that human brain size reduction was surprisingly recent, occurring in the last 3,000 years. Our dating does not support hypotheses concerning brain size reduction as a by-product of body size reduction, a result of a shift to an agricultural diet, or a consequence of self-domestication. We suggest our analysis supports the hypothesis that the recent decrease in brain size may instead result from the externalization of knowledge and advantages of group-level decision-making due in part to the advent of social systems of distributed cognition and the storage and sharing of information. Humans live in social groups in which multiple brains contribute to the emergence of collective intelligence. Although difficult to study in the deep history of Homo, the impacts of group size, social organization, collective intelligence and other potential selective forces on brain evolution can be elucidated using ants as models. The remarkable ecological diversity of ants and their species richness encompasses forms convergent in aspects of human sociality, including large group size, agrarian life histories, division of labor, and collective cognition. Ants provide a wide range of social systems to generate and test hypotheses concerning brain size enlargement or reduction and aid in interpreting patterns of brain evolution identified in humans. Although humans and ants represent very different routes in social and cognitive evolution, the insights ants offer can broadly inform us of the selective forces that influence brain size.

Source

Frontiers in ecology and evolution, 22 October 2021 [https://www.frontiersin.org/articles/10.3389/fevo.2021.742639/full]

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Abstract

Due to the advances in information retrieval in the past decades, search engines have become extremely efficient at acquiring useful sources in response to a user's query. However, for more prolonged and complex information seeking tasks, these search engines are not as well suited. During complex information seeking tasks, various stages may occur, which imply varying support needs for users. However, the implications of theoretical information seeking models for concrete search user interfaces (SUI) design are unclear, both at the level of the individual features and of the whole interface. Guidelines and design patterns for concrete SUIs, on the other hand, provide recommendations for feature design, but these are separated from their role in the information seeking process. This chapter addresses the question of how to design SUIs with enhanced support for the macro-level process, first by reviewing previous research. Subsequently, we outline a framework for complex task support, which explicitly connects the temporal development of complex tasks with different levels of support by SUI features. This is followed by a discussion of concrete system examples which include elements of the three dimensions of our framework in an exploratory search and sensemaking context. Moreover, we discuss the connection of navigation with the search-oriented framework. In our final discussion and conclusion, we provide recommendations for designing more holistic SUIs which potentially evolve along with a user's information seeking process.

Source

Understanding and improving information search [Vgl. unter: https://www.researchgate.net/publication/341747751_Designing_Multistage_Search_Systems_to_Support_the_Information_Seeking_Process]

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Abstract

We present an overview of the NTCIR Math Tasks organized during NTCIR-10, 11, and 12. These tasks are primarily dedicated to techniques for searching mathematical content with formula expressions. In this chapter, we first summarize the task design and introduce test collections generated in the tasks. We also describe the features and main challenges of mathematical information retrieval systems and discuss future perspectives in the field.

Series

¬The Information retrieval series, vol 43

Source

Evaluating information retrieval and access tasks. Eds.: Sakai, T., Oard, D., Kando, N. [https://doi.org/10.1007/978-981-15-5554-1_12]

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Date

17.11.2020 12:22:59

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Abstract

Cataloging practices, as exemplified by the five user tasks of the IFLA Library Reference Model, can support information literacy practices. The six frames of the Framework for Information Literacy for Higher Education are used as lenses to examine the user tasks. Two themes emerge from this examination: context matters, and catalogers must tailor bibliographic descriptions to meet users' expectations and information needs. Catalogers need to solicit feedback from various user communities to reform cataloging practices to remain current and viable. Such conversations will enrich the catalog and enhance (reclaim?) its position as a primary tool for research and learning. Supplemental data for this article is available online at https://doi.org/10.1080/01639374.2021.1939828.

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Abstract

The initial dimensions extracted by latent semantic analysis (LSA) of a document-term matrixhave been shown to mainly display marginal effects, which are irrelevant for informationretrieval. To improve the performance of LSA, usually the elements of the raw document-term matrix are weighted and the weighting exponent of singular values can be adjusted.An alternative information retrieval technique that ignores the marginal effects is correspon-dence analysis (CA). In this paper, the information retrieval performance of LSA and CA isempirically compared. Moreover, it is explored whether the two weightings also improve theperformance of CA. The results for four empirical datasets show that CA always performsbetter than LSA. Weighting the elements of the raw data matrix can improve CA; however,it is data dependent and the improvement is small. Adjusting the singular value weightingexponent often improves the performance of CA; however, the extent of the improvementdepends on the dataset and the number of dimensions. (PDF) Improving information retrieval through correspondence analysis instead of latent semantic analysis.

Source

Journal of intelligent information systems [https://doi.org/10.1007/s10844-023-00815-y]

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Abstract

Wikipedia categories, a classification scheme built for organizing and describing Wikpedia articles, are being applied in computer science research. This paper adopts a systematic literature review approach, in order to identify different approaches and uses of Wikipedia categories in information retrieval research. Several types of work are identified, depending on the intrinsic study of the categories structure, or its use as a tool for the processing and analysis of other documentary corpus different to Wikipedia. Information retrieval is identified as one of the major areas of use, in particular its application in the refinement and improvement of search expressions, and the construction of textual corpus. However, the set of available works shows that in many cases research approaches applied and results obtained can be integrated into a comprehensive and inclusive concept of information retrieval.

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Abstract

COVID-19 is an unprecedented global health crisis that will have immeasurable consequences for our economic and social well-being. Tedros Adhanom Ghebreyesus, the director general of the World Health Organization, stated "We're not just fighting an epidemic; we're fighting an infodemic". Currently, there is no robust scientific basis to the existing definitions of false information used in the fight against the COVID-19infodemic. The purpose of this paper is to demonstrate how the use of a novel taxonomy and related model (based upon a conceptual framework that synthesizes insights from information science, philosophy, media studies and politics) can produce new scientific definitions of mis-, dis- and malinformation. We undertake our analysis from the viewpoint of information systems research. The conceptual approach to defining mis-,dis- and malinformation can be applied to a wide range of empirical examples and, if applied properly, may prove useful in fighting the COVID-19 infodemic. In sum, our research suggests that: (i) analyzing all types of information is important in the battle against the COVID-19 infodemic; (ii) a scientific approach is required so that different methods are not used by different studies; (iii) "misinformation", as an umbrella term, can be confusing and should be dropped from use; (iv) clear, scientific definitions of information types will be needed going forward; (v) malinformation is an overlooked phenomenon involving reconfigurations of the truth.

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Abstract

I wonder if you would consider an English perspective on the exchange between Bernd Jörs and Hermann Huemer. In my career in the independent education sector I can recall many discussions and Government reports about cross-curricular issues such as logical reasoning and critical thinking, In the IB system this led to the inclusion in the Diploma of "Theory of Knowledge." In the UK we had "key skills" and "critical thinking." One such key skill is what we now call "information literacy." "In his parody of Information literacy, Dr Jörs seems to have confused a necessary condition for a sufficient condition. The fact that information competence may be necessary for serious academic study does not of course make it sufficient. When that is understood the joke about the megalomaniac rather loses its force. (We had better pass over the rant which follows, the sneer at "earth sciences" and the German prejudice towards Austrians)."

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Abstract

We consider use of ontological background knowledge in intelligent information systems and analyze directions of their reduction in compliance with specifics of particular user task. Such reduction is aimed at simplification of knowledge processing without loss of significant information. We propose methods of generation of task thesauri based on domain ontology that contain such subset of ontological concepts and relations that can be used in task solving. Combinatorial optimization is used for minimization of task thesaurus. In this approach, semantic similarity estimates are used for determination of concept significance for user task. Some practical examples of optimized thesauri application for semantic retrieval and competence analysis demonstrate efficiency of proposed approach.

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Abstract

The content of master's programs accredited by the American Library Association was examined to assess the status of knowledge organization (KO) as a subject in current training. Data collected show that KO remains very visible in a majority of programs, mainly in the form of required and electives courses focusing on descriptive cataloging, classification, and metadata. Observed tendencies include, however, the recent elimination of the required KO course in several programs, the reality that one third of KO electives listed in course catalogs have not been scheduled in the past three years, and the fact that two-thirds of those teaching KO specialize in other areas of information science.

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Abstract

Persistent identifiers (PID's) are essential for getting access and referring to library, archive and museum (LAM) collection objects in a sustainable and unambiguous way, both internally and externally. Heritage institutions need a universal policy for the use of PID's in order to have an efficient digital infrastructure at their disposal and to achieve optimal interoperability, leading to open data, open collections and efficient resource management. Here the discussion is limited to PID's that institutions can assign to objects they own or administer themselves. PID's for people, subjects etc. can be used by heritage institutions, but are generally managed by other parties. The first part of this article consists of a general theoretical description of persistent identifiers. First of all, I discuss the questions of what persistent identifiers are and what they are not, and what is needed to administer and use them. The most commonly used existing PID systems are briefly characterized. Then I discuss the types of objects PID's can be assigned to. This section concludes with an overview of the requirements that apply if PIDs should also be used for linked data. The second part examines current infrastructural practices, and existing PID systems and their advantages and shortcomings. Based on these practical issues and the pros and cons of existing PID systems a list of requirements for PID systems is presented which is used to address a number of practical considerations. This section concludes with a number of recommendations.

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Abstract

Conceptual models are artifacts representing conceptualizations of particular domains. Hence, multi-domain model catalogs serve as empirical sources of knowledge and insights about specific domains, about the use of a modeling language's constructs, as well as about the patterns and anti-patterns recurrent in the models of that language crosscutting different domains. However, to support domain and language learning, model reuse, knowledge discovery for humans, and reliable automated processing and analysis by machines, these catalogs must be built following generally accepted quality requirements for scientific data management. Especially, all scientific (meta)data-including models-should be created using the FAIR principles (Findability, Accessibility, Interoperability, and Reusability). In this paper, we report on the construction of a FAIR model catalog for Ontology-Driven Conceptual Modeling research, a trending paradigm lying at the intersection of conceptual modeling and ontology engineering in which the Unified Foundational Ontology (UFO) and OntoUML emerged among the most adopted technologies. In this initial release, the catalog includes over a hundred models, developed in a variety of contexts and domains. The paper also discusses the research implications for (ontology-driven) conceptual modeling of such a resource.

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Abstract

Terminology has evolved from static and prescriptive theories to dynamic and cognitive approaches. Thanks to these approaches, there have been significant advances in the design and elaboration of terminological resources. This has resulted in the creation of tools such as terminological knowledge bases, which are able to show how concepts are interrelated through different semantic or conceptual relations. Of these relations, hyponymy is the most relevant to terminology work because it deals with concept categorization and term hierarchies. This doctoral thesis presents an enhancement of the semantic structure of EcoLexicon, a terminological knowledge base on environmental science. The aim of this research was to improve the description, categorization, and representation of hyponymy in environmental terminology. Therefore, we created HypoLexicon, a new stand-alone module for EcoLexicon in the form of a hyponymy-based terminological resource. This resource contains twelve terminological entries from four specialized domains (Biology, Chemistry, Civil Engineering, and Geology), which consist of 309 concepts and 465 terms associated with those concepts. This research was mainly based on the theoretical premises of Frame-based Terminology. This theory was combined with Cognitive Linguistics, for conceptual description and representation; Corpus Linguistics, for the extraction and processing of linguistic and terminological information; and Ontology, related to hyponymy and relevant for concept categorization. HypoLexicon was constructed from the following materials: (i) the EcoLexicon English Corpus; (ii) other specialized terminological resources, including EcoLexicon; (iii) Sketch Engine; and (iv) Lexonomy. This thesis explains the methodologies applied for corpus extraction and compilation, corpus analysis, the creation of conceptual hierarchies, and the design of the terminological template. The results of the creation of HypoLexicon are discussed by highlighting the information in the hyponymy-based terminological entries: (i) parent concept (hypernym); (ii) child concepts (hyponyms, with various hyponymy levels); (iii) terminological definitions; (iv) conceptual categories; (v) hyponymy subtypes; and (vi) hyponymic contexts. Furthermore, the features and the navigation within HypoLexicon are described from the user interface and the admin interface. In conclusion, this doctoral thesis lays the groundwork for developing a terminological resource that includes definitional, relational, ontological and contextual information about specialized hypernyms and hyponyms. All of this information on specialized knowledge is simple to follow thanks to the hierarchical structure of the terminological template used in HypoLexicon. Therefore, not only does it enhance knowledge representation, but it also facilitates its acquisition.

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Abstract

This study aims to introduce librarians to ChatGPT and challenge them to think about how it fits into their work and what learning they will need to do in order to stay relevant in the realm of artificial intelligence. Design/methodology/approach Popular and scientific media sources were monitored over the course of two months to gather current discussions about the uses of and opinions about ChatGPT. This was analyzed in light of historical developments in education and libraries. Additional sources of information on the topic were described and discussed so that the issue is made relevant to librarians and libraries. Findings The potential risks and benefits of ChatGPT are highly relevant for librarians but also currently not fully understood. We are in a very early stage of understanding and using this technology but it does appear to have the possibility of becoming disruptive to libraries as well as many other aspects of life. Originality/value ChatGPT-3 has only been publicly available since the end of November 2022. We are just now starting to take a deeper dive into what this technology means for libraries. This paper is one of the early ones that provide librarians with some direction in terms of where to focus their interest and attention in learning about it.

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Content

# Community action on all ontologies (quality, FAIRness, conformity) Archivo is extensible and allows contributions to give consumers a central place to encode their requirements. We envision fostering adherence to standards and strengthening incentives for publishers to build a better (FAIRer) web of ontologies. 1. SHACL (https://www.w3.org/TR/shacl/, co-edited by DBpedia's CTO D. Kontokostas) enables easy testing of ontologies. Archivo offers free SHACL continuous integration testing for ontologies. Anyone can implement their SHACL tests and add them to the SHACL library on Github. We believe that there are many synergies, i.e. SHACL tests for your ontology are helpful for others as well. 2. We are looking for ontology experts to join DBpedia and discuss further validation (e.g. stars) to increase FAIRness and quality of ontologies. We are forming a steering committee and also a PC for the upcoming Vocarnival at SEMANTiCS 2021. Please message hellmann@informatik.uni-leipzig.de <mailto:hellmann@informatik.uni-leipzig.de>if you would like to join. We would like to extend the Archivo platform with relevant visualisations, tests, editing aides, mapping management tools and quality checks.

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Abstract

The paper examines the development of facet analysis as a methodology and the role it plays in building classifications and other knowledge-organization tools. The use of categorical analysis in areas other than library and information science is also considered. The suitability of the faceted approach for humanities documentation is explored through a critical description of the FATKS (Facet Analytical Theory in Managing Knowledge Structure for Humanities) project carried out at University College London. This research focused on building a conceptual model for the subject of religion together with a relational database and search-and-browse interfaces that would support some degree of automatic classification. The paper concludes with a discussion of the differences between the conceptual model and the vocabulary used to populate it, and how, in the case of religion, the choice of terminology can create an apparent bias in the system.

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Source

Journal of information science. 54(2023) no.x, S.xx-xx