Search (139 results, page 1 of 7)

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Source

https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN

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Content

Master thesis Master of Science (Library and Information Studies) (MSc), Universität Wien. Advisor: Christoph Steiner. Vgl.: https://www.researchgate.net/publication/371680244_Vergabe_von_DDC-Sachgruppen_mittels_eines_Schlagwort-Thesaurus. DOI: 10.25365/thesis.70030. Vgl. dazu die Präsentation unter: https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=web&cd=&ved=0CAIQw7AJahcKEwjwoZzzytz_AhUAAAAAHQAAAAAQAg&url=https%3A%2F%2Fwiki.dnb.de%2Fdownload%2Fattachments%2F252121510%2FDA3%2520Workshop-Gabler.pdf%3Fversion%3D1%26modificationDate%3D1671093170000%26api%3Dv2&psig=AOvVaw0szwENK1or3HevgvIDOfjx&ust=1687719410889597&opi=89978449.

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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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Date

22. 6.2023 18:15:06

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Abstract

How the knowledge base of disciplines grows, renews, and decays informs their distinct characteristics and epistemology. Here we track the evolution of knowledge bases of 19 disciplines for over 45 years. We introduce the notation of knowledge inheritance as the overlap in the set of references between years. We discuss two modes of knowledge inheritance of disciplines-successive and distant. To quantify the status and propensity of knowledge inheritance for disciplines, we propose two indicators: one descriptively describes knowledge base evolution, and one estimates the propensity of knowledge inheritance. When observing the continuity in knowledge bases for disciplines, we show distinct patterns for STEM and SS&H disciplines: the former inherits knowledge bases more successively, yet the latter inherits significantly from distant knowledge bases. We further discover stagnation or revival in knowledge base evolution where older knowledge base ceases to decay after 10 years (e.g., Physics and Mathematics) and are increasingly reused (e.g., Philosophy). Regarding the propensity of inheriting prior knowledge bases, we observe unanimous rises in both successive and distant knowledge inheritance. We show that knowledge inheritance could reveal disciplinary characteristics regarding the trajectory of knowledge base evolution and interesting insights into the metabolism and maturity of scholarly communication.

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Abstract

Theory is a kind of condensed human knowledge. This paper is to examine the mechanism of interdisciplinary diffusion of theoretical knowledge by tracing the diffusion of a representative theory, the Technology Acceptance Model (TAM). Design/methodology/approach Based on the full-scale dataset of Web of Science (WoS), the citations of Davis's original work about TAM were analysed and the interdisciplinary diffusion paths of TAM were delineated, a supervised machine learning method was used to extract theory incidents, and a content analysis was used to categorize the patterns of theory evolution. Findings It is found that the diffusion of a theory is intertwined with its evolution. In the process, the role that a participating discipline play is related to its knowledge distance from the original disciplines of TAM. With the distance increases, the capacity to support theory development and innovation weakens, while that to assume analytical tools for practical problems increases. During the diffusion, a theory evolves into new extensions in four theoretical construction patterns, elaboration, proliferation, competition and integration. Research limitations/implications The study does not only deepen the understanding of the trajectory of a theory but also enriches the research of knowledge diffusion and innovation. Originality/value The study elaborates the relationship between theory diffusion and theory development, reveals the roles of the participating disciplines played in theory diffusion and vice versa, interprets four patterns of theory evolution and uses text mining technique to extract theory incidents, which makes up for the shortcomings of citation analysis and content analysis used in previous studies.

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Abstract

The purpose of this article is to identify the most important factors and features in the evolution of thesauri and ontologies through a dialectic model. This model relies on a dialectic process or idea which could be discovered via a dialectic method. This method has focused on identifying the logical relationship between a beginning proposition, or an idea called a thesis, a negation of that idea called the antithesis, and the result of the conflict between the two ideas, called a synthesis. During the creation of knowl­edge organization systems (KOSs), the identification of logical relations between different ideas has been made possible through the consideration and use of the most influential methods and tools such as dictionaries, Roget's Thesaurus, thesaurus, micro-, macro- and metathesauri, ontology, lower, middle and upper level ontologies. The analysis process has adapted a historical methodology, more specifically a dialectic method and documentary method as the reasoning process. This supports our arguments and synthesizes a method for the analysis of research results. Confirmed by the research results, the principle of unity has shown to be the most important factor in the development and evolution of the structure of knowl­edge organization systems and their types. There are various types of unity when considering the analysis of logical relations. These include the principle of unity of alphabetical order, unity of science, semantic unity, structural unity and conceptual unity. The results have clearly demonstrated a movement from plurality to unity in the assembling of the complex structure of knowl­edge organization systems to increase information and knowl­edge storage and retrieval performance.

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Abstract

Purpose Information scientists may find value in studying cultural information evolution and information diffusion through memetics. Information studies in memetics have often found datafication in memetics research difficult. Meanwhile, current memetic scholarship elsewhere is abundant in data due to their focus on Internet artifacts. This paper offers a way to close the datafication gap for information researchers by associating information data with "differences" between memetic documents. Design/methodology/approach This work offers a joint theory and methodology invested in information-oriented memetics. This methodology of differences is developed from a content analysis of difference on a collection of images with visual similarities. Findings The authors find that this kind of analysis provides a heuristic method for quantitatively bounding where one meme ends and another begins. The authors also find that this approach helps describe the dynamics of memetic media in such a way that the authors can datafy information or cultural evolution more clearly. Originality/value Here the authors offer an approach for studying cultural information evolution through the study of memes. In doing so, the authors forward a methodology of difference which leverages content analysis in order to outline how it functions practically. The authors propose a quantitative methodology to assess differences between versions of document contents in order to examine what a particular meme is. The authors also move toward showing the information structure which defines a meme.

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Abstract

Data Science (DS) has emerged from the shadows of its parents-statistics and computer science-into an independent field since its origin nearly six decades ago. Its evolution and education have taken many sharp turns. We present an impressionistic study of the evolution of DS anchored to Kuhn's four stages of paradigm shifts. First, we construct the landscape of DS based on curriculum analysis of the 32 iSchools across the world offering graduate-level DS programs. Second, we paint the "field" as it emerges from the word frequency patterns, ranking, and clustering of course titles based on text mining. Third, we map the curriculum to the landscape of DS and project the same onto the Edison Data Science Framework (2017) and ACM Data Science Knowledge Areas (2021). Our study shows that the DS programs of iSchools align well with the field and correspond to the Knowledge Areas and skillsets. iSchool's DS curriculums exhibit a bias toward "data visualization" along with machine learning, data mining, natural language processing, and artificial intelligence; go light on statistics; slanted toward ontologies and health informatics; and surprisingly minimal thrust toward eScience/research data management, which we believe would add a distinctive iSchool flavor to the DS.

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Abstract

In this study, we propose a way to link the scholarly contents of scientific papers by constructing a knowledge graph based on the semantic organization of argumentation units and relations in scientific papers. We carried out an argumentation graph data model aimed at linking multiple discourses, and also developed a semantic annotation platform for scientific papers and an argumentation graph visualization system. A construction experiment was performed using 12 articles. The final argumentation graph has 1,262 nodes and 1,628 edges, including 1,628 intra-article relations and 190 inter-article relations. Knowledge evolution representation, strategic reading, and automatic abstracting use cases are presented to demonstrate the application of the argumentation graph. In contrast to existing knowledge graphs used in academic fields, the argumentation graph better supports the organization and representation of scientific paper content and can be used as data infrastructure in scientific knowledge retrieval, reorganization, reasoning, and evolution. Moreover, it supports automatic abstract and strategic reading.

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Abstract

Von der biologischen Evolution inspiriert, machen Informatiker große Fortschritte im Bereich der künstlichen Intelligenz. Dabei stellt es sich als hilfreich heraus, sich nicht auf ein einziges bestimmtes Ziel zu versteifen, sondern Algorithmen eine vielfältige und freie Entwicklung zu ermöglichen. Manchmal findet man die besten Lösungen, indem man viele verschiedene - und unter Umständen abwegige - Ideen verfolgt.

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Abstract

We demonstrate that if consciousness is relevant for the temporal evolution of a system's states--that is, if it is dynamically relevant--then AI systems cannot be conscious. That is because AI systems run on CPUs, GPUs, TPUs or other processors which have been designed and verified to adhere to computational dynamics that systematically preclude or suppress deviations. The design and verification preclude or suppress, in particular, potential consciousness-related dynamical effects, so that if consciousness is dynamically relevant, AI systems cannot be conscious.

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Abstract

Characterizing the leadership in research is important to revealing the interaction pattern and organizational structure through research collaboration. This research defines the leadership role based on the corresponding author's affiliation, and presents the first quantitative research on the factors and evolution of 5 proximity dimensions (geographical, cognitive, institutional, social, and economic) of research leadership. The data to capture research leadership consist of a set of multi-institution articles in the fields of "Life Sciences & Biomedicine," "Technology," "Physical Sciences," "Social Sciences," and "Humanities & Arts" during 2013-2017 from the Web of Science Core Citation Database. A Tobit regression-based gravity model indicates that the mass of research leadership of both the leading and participating institutions and the geographical, cognitive, institutional, social, and economic proximities are important factors for the flow of research leadership among Chinese institutions. In general, the effect of these proximities for research leadership flow has been declining recently. The outcome of this research sheds light on the leadership evolution and flow among Chinese institutions, and thus can provide evidence and support for grant allocation policies to facilitate scientific research and collaborations.

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Abstract

The purpose of this article is to identify the most important factors and features in the evolution of thesauri and ontologies through a dialectic model. This model relies on a dialectic process or idea which could be discovered via a dialectic method. This method has focused on identifying the logical relationship between a beginning proposition, or an idea called a thesis, a negation of that idea called the antithesis, and the result of the conflict between the two ideas, called a synthesis. During the creation of knowl­edge organization systems (KOSs), the identification of logical relations between different ideas has been made possible through the consideration and use of the most influential methods and tools such as dictionaries, Roget's Thesaurus, thesaurus, micro-, macro- and metathesauri, ontology, lower, middle and upper level ontologies. The analysis process has adapted a historical methodology, more specifically a dialectic method and documentary method as the reasoning process. This supports our arguments and synthesizes a method for the analysis of research results. Confirmed by the research results, the principle of unity has shown to be the most important factor in the development and evolution of the structure of knowl­edge organization systems and their types. There are various types of unity when considering the analysis of logical relations. These include the principle of unity of alphabetical order, unity of science, semantic unity, structural unity and conceptual unity. The results have clearly demonstrated a movement from plurality to unity in the assembling of the complex structure of knowl­edge organization systems to increase information and knowl­edge storage and retrieval performance.

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Abstract

This study considers commons-based peer production (CBPP) by examining the organizational processes of the free/libre open-source software community, Drupal. It does so by exploring the sociotechnical systems that have emerged around both Drupal's development and its face-to-face communitarian events. There has been criticism of the simplistic nature of previous research into free software; this study addresses this by linking studies of CBPP with a qualitative study of Drupal's organizational processes. It focuses on the evolution of organizational structures, identifying the intertwined dynamics of formalization and decentralization, resulting in coexisting sociotechnical systems that vary in their degrees of organicity.

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Abstract

The purpose of this paper is to introduce the concept of Dark Knowledge, an epistemology that acknowledges both alternative knowledge and ways of knowing which are cognizant of the moral and ethical positioning of each. Design/methodology/approach This is a conceptual paper that uses existing relevant literature to develop the work. The paper uses a four-stage literature search process and draws upon a range of disciplines, including philosophy, computer science and information management, to underpin the evolution of the concept. Findings As a conceptual paper, no empirical findings are presented. Instead, the paper presents an embryonic model of Dark Knowledge and identifies a number of characteristics, which may be used to explore the concept in more detail. Research limitations/implications There is a clear need to develop a body of empirical work, adding to the theoretical perspectives presented in this paper. It is anticipated that this paper will provide one of the cornerstones for future studies in this area. Originality/value The paper makes an original contribution to the study of information behaviours, practices and epistemology.

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Abstract

Within the knowledge organization systems (KOS) set, the term "ontology" is paradigmatic of the terminological ambiguity in different typologies. Contributing to this situation is the indiscriminate association of the term "ontology", both as a specific type of KOS and as a process of categorization, due to the interdisciplinary use of the term with different meanings. We present a systematization of the perspectives of different authors of ontologies, as representational artifacts, seeking to contribute to terminological clarification. Focusing the analysis on the intention, semantics and modulation of ontologies, it was possible to notice two broad perspectives regarding ontologies as artifacts that coexist in the knowledge organization systems spectrum. We have ontologies viewed, on the one hand, as an evolution in terms of complexity of traditional conceptual systems, and on the other hand, as a system that organizes ontological rather than epistemological knowledge. The focus of ontological analysis is the item to model and not the intentions that motivate the construction of the system.

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Abstract

This article presents a rare insight into the migration of municipality record-keeping databases. The migration of a database for preservation purposes poses several challenges. In particular, our findings show that relevant issues are file-format heterogeneity, collection volume, time and database structure evolution, and deviation from the governing standard. This article presents and discusses how such issues interfere with an organization's ability to undertake a migration, for preservation purposes, of records from a relational database. The case study at hand concerns six Norwegian municipality record-keeping databases covering a period from 1999 to 2012. The findings are presented with a discussion on how these issues manifest themselves as a problem for long-term preservation. The results discussed here may help an organization and Information Systems (IS) manager to establish a best practice when undertaking a migration project and enable them to avoid some of the pitfalls that were discovered during this project.

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Abstract

Cataloging principles and objectives guide the formation of cataloging rules governing the organization of information within the library catalog, as well as the function of the catalog itself. Changes in technologies wrought by the internet and the web have been the driving forces behind shifting cataloging practice and reconfigurations of cataloging rules. Modern cataloging principles and objectives started in 1841 with the creation of Panizzi's 91 Rules for the British Museum and gained momentum with Charles Cutter's Rules for Descriptive Cataloging (1904). The first Statement of International Cataloguing Principles (ICP) was adopted in 1961, holding their place through such codifications as AACR and AACR2 in the 1970s and 1980s. Revisions accelerated starting in 2003 with the three original FR models. The Library Reference Model (LRM) in 2017 acted as a catalyst for the evolution of principles and objectives culminating in the creation of Resource Description and Access (RDA) in 2013.