Search (91 results, page 1 of 5)

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Abstract

With the terrific growth of data volume and data being produced every second on millions of devices across the globe, there is a desperate need to manage the unstructured data available on web pages efficiently. Semantic Web or also known as Web of Trust structures the scattered data on the Internet according to the needs of the user. It is an extension of the World Wide Web (WWW) which focuses on manipulating web data on behalf of Humans. Due to the ability of the Semantic Web to integrate data from disparate sources and hence makes it more user-friendly, it is an emerging trend. Tim Berners-Lee first introduced the term Semantic Web and since then it has come a long way to become a more intelligent and intuitive web. Data Visualization plays an essential role in explaining complex concepts in a universal manner through pictorial representation, and the Semantic Web helps in broadening the potential of Data Visualization and thus making it an appropriate combination. The objective of this chapter is to provide fundamental insights concerning the semantic web technologies and in addition to that it also elucidates the issues as well as the solutions regarding the semantic web. The purpose of this chapter is to highlight the semantic web architecture in detail while also comparing it with the traditional search system. It classifies the semantic web architecture into three major pillars i.e. RDF, Ontology, and XML. Moreover, it describes different semantic web tools used in the framework and technology. It attempts to illustrate different approaches of the semantic web search engines. Besides stating numerous challenges faced by the semantic web it also illustrates the solutions.

Theme

Semantic Web

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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

In 2021, sharing content is easier than ever. Our lingua franca is visual: memes, infographics, TikToks. Our references cross borders and platforms, shared and remixed a hundred different ways in minutes. Digital culture is collective by default and has us together all around the world. But as the internet reaches its "dirty 30s," what happens when pieces of digital culture that have been saved, screenshotted, and reposted for years need to retire? Let's dig into the story of one of these artifacts: The Lenna image. The Lenna image may be relatively unknown in pop culture today, but in the engineering world, it remains an icon. I first encountered the image in an undergrad class, then grad school, and then all over the sites and software I use every day as a tech worker like Github, OpenCV, Stack Overflow, and Quora. To understand where the image is today, you have to understand how it got here. So, I decided to scrape Google scholar, search, and reverse image search results to track down thousands of instances of the image across the internet (see more in the methods section).
Lena Forsén, the real human behind the Lenna image, was first published in Playboy in 1972. Soon after, USC engineers searching for a suitable test image for their image processing research sought inspiration from the magazine. They deemed Lenna the right fit and scanned the image into digital, RGB existence. From here, the story of the image follows the story of the internet. Lenna was one of the first inhabitants of ARPANet, the internet's predecessor, and then the world wide web. While the image's reach was limited to a few research papers in the '70s and '80s, in 1991, Lenna was featured on the cover of an engineering journal alongside another popular test image, Peppers. This caught the attention of Playboy, which threatened a copyright infringement lawsuit. Engineers who had grown attached to Lenna fought back. Ultimately, they prevailed, and as a Playboy VP reflected on the drama: "We decided we should exploit this because it is a phenomenon." The Playboy controversy canonized Lenna in engineering folklore and prompted an explosion of conversation about the image. Image hits on the internet rose to a peak number in 1995.
But despite this progress, almost 2 years later, the use of Lenna continues. The image appears on the internet in 30+ different languages in the last decade, including 10+ languages in 2021. The image's spread across digital geographies has mirrored this geographical growth, moving from mostly .org domains before 1990 to over 100 different domains today, notably .com and .edu, along with others. Within the .edu world, the Lenna image continues to appear in homework questions, class slides and to be hosted on educational and research sites, ensuring that it is passed down to new generations of engineers. Whether it's due to institutional negligence or defiance, it seems that for now, the image is here to stay.

Content

"Having known Lenna for almost a decade, I have struggled to understand what the story of the image means for what tech culture is and what it is becoming. To me, the crux of the Lenna story is how little power we have over our data and how it is used and abused. This threat seems disproportionately higher for women who are often overrepresented in internet content, but underrepresented in internet company leadership and decision making. Given this reality, engineering and product decisions will continue to consciously (and unconsciously) exclude our needs and concerns. While social norms are changing towards non-consensual data collection and data exploitation, digital norms seem to be moving in the opposite direction. Advancements in machine learning algorithms and data storage capabilities are only making data misuse easier. Whether the outcome is revenge porn or targeted ads, surveillance or discriminatory AI, if we want a world where our data can retire when it's outlived its time, or when it's directly harming our lives, we must create the tools and policies that empower data subjects to have a say in what happens to their data. including allowing their data to die."

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Abstract

Conclusion There is a reason why Google Scholar and Web of Science/Scopus are kings of the hills in their various arenas. They have strong brand recogniton, a head start in development and a mass of eyeballs and users that leads to an almost virtious cycle of improvement. Competing against such well established competitors is not easy even when one has deep pockets (Microsoft) or a killer idea (scite). It will be interesting to see how the landscape will look like in 2030. Stay tuned for part II where I review each particular index.

Date

17.11.2020 12:22:59

Object

Web of Science

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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

Literature review articles are essential to summarize the related work in the selected field. However, covering all related studies takes too much time and effort. This study questions how Artificial Intelligence can be used in this process. We used ChatGPT to create a literature review article to show the stage of the OpenAI ChatGPT artificial intelligence application. As the subject, the applications of Digital Twin in the health field were chosen. Abstracts of the last three years (2020, 2021 and 2022) papers were obtained from the keyword "Digital twin in healthcare" search results on Google Scholar and paraphrased by ChatGPT. Later on, we asked ChatGPT questions. The results are promising; however, the paraphrased parts had significant matches when checked with the Ithenticate tool. This article is the first attempt to show the compilation and expression of knowledge will be accelerated with the help of artificial intelligence. We are still at the beginning of such advances. The future academic publishing process will require less human effort, which in turn will allow academics to focus on their studies. In future studies, we will monitor citations to this study to evaluate the academic validity of the content produced by the ChatGPT. 1. Introduction OpenAI ChatGPT (ChatGPT, 2022) is a chatbot based on the OpenAI GPT-3 language model. It is designed to generate human-like text responses to user input in a conversational context. OpenAI ChatGPT is trained on a large dataset of human conversations and can be used to create responses to a wide range of topics and prompts. The chatbot can be used for customer service, content creation, and language translation tasks, creating replies in multiple languages. OpenAI ChatGPT is available through the OpenAI API, which allows developers to access and integrate the chatbot into their applications and systems. OpenAI ChatGPT is a variant of the GPT (Generative Pre-trained Transformer) language model developed by OpenAI. It is designed to generate human-like text, allowing it to engage in conversation with users naturally and intuitively. OpenAI ChatGPT is trained on a large dataset of human conversations, allowing it to understand and respond to a wide range of topics and contexts. It can be used in various applications, such as chatbots, customer service agents, and language translation systems. OpenAI ChatGPT is a state-of-the-art language model able to generate coherent and natural text that can be indistinguishable from text written by a human. As an artificial intelligence, ChatGPT may need help to change academic writing practices. However, it can provide information and guidance on ways to improve people's academic writing skills.

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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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Abstract

Verschiedene Stakeholder fordern eine bessere Verfügbarkeit von Forschungsdaten. Der Erfolg dieser Initiativen hängt wesentlich von einer guten Auffindbarkeit der publizierten Datensätze ab, weshalb Dataset Retrieval an Bedeutung gewinnt. Dataset Retrieval ist eine Sonderform von Information Retrieval, die sich mit dem Auffinden von Datensätzen befasst. Dieser Beitrag fasst aktuelle Forschungsergebnisse über das Informationsverhalten von Datensuchenden zusammen. Anschließend werden beispielhaft zwei Suchdienste verschiedener Ausrichtung vorgestellt und verglichen. Um darzulegen, wie diese Dienste ineinandergreifen, werden inhaltliche Überschneidungen von Datenbeständen genutzt, um den Metadatenaustausch zu analysieren.

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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

We are proud to announce DBpedia Archivo (https://archivo.dbpedia.org) an augmented ontology archive and interface to implement FAIRer ontologies. Each ontology is rated with 4 stars measuring basic FAIR features. We discovered 890 ontologies reaching on average 1.95 out of 4 stars. Many of them have no or unclear licenses and have issues w.r.t. retrieval and parsing.

Content

# Community action on individual ontologies We would like to call on all ontology maintainers and consumers to help us increase the average star rating of the web of ontologies by fixing and improving its ontologies. You can easily check an ontology at https://archivo.dbpedia.org/info. If you are an ontology maintainer just release a patched version - archivo will automatically pick it up 8 hours later. If you are a user of an ontology and want your consumed data to become FAIRer, please inform the ontology maintainer about the issues found with Archivo. The star rating is very basic and only requires fixing small things. However, theimpact on technical and legal usability can be immense.
# 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.
# How does Archivo work? Each week Archivo runs several discovery algorithms to scan for new ontologies. Once discovered Archivo checks them every 8 hours. When changes are detected, Archivo downloads and rates and archives the latest snapshot persistently on the DBpedia Databus. # Archivo's mission Archivo's mission is to improve FAIRness (findability, accessibility, interoperability, and reusability) of all available ontologies on the Semantic Web. Archivo is not a guideline, it is fully automated, machine-readable and enforces interoperability with its star rating. - Ontology developers can implement against Archivo until they reach more stars. The stars and tests are designed to guarantee the interoperability and fitness of the ontology. - Ontology users can better find, access and re-use ontologies. Snapshots are persisted in case the original is not reachable anymore adding a layer of reliability to the decentral web of ontologies.

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Abstract

Es handelt sich um eine bibliometrische Untersuchung der Entwicklung der Open-Access-Verfügbarkeit wissenschaftlicher Zeitschriftenartikel in Deutschland, die im Zeitraum 2010-18 erschienen und im Web of Science indexiert sind. Ein besonderes Augenmerk der Analyse lag auf der Frage, ob und inwiefern sich die Open-Access-Profile der Universitäten und außeruniversitären Wissenschaftseinrichtungen in Deutschland voneinander unterscheiden.

Content

This study investigates the development of open access (OA) to journal articles from authors affiliated with German universities and non-university research institutions in the period 2010-2018. Beyond determining the overall share of openly available articles, a systematic classification of distinct categories of OA publishing allowed us to identify different patterns of adoption of OA. Taking into account the particularities of the German research landscape, variations in terms of productivity, OA uptake and approaches to OA are examined at the meso-level and possible explanations are discussed. The development of the OA uptake is analysed for the different research sectors in Germany (universities, non-university research institutes of the Helmholtz Association, Fraunhofer Society, Max Planck Society, Leibniz Association, and government research agencies). Combining several data sources (incl. Web of Science, Unpaywall, an authority file of standardised German affiliation information, the ISSN-Gold-OA 3.0 list, and OpenDOAR), the study confirms the growth of the OA share mirroring the international trend reported in related studies. We found that 45% of all considered articles during the observed period were openly available at the time of analysis. Our findings show that subject-specific repositories are the most prevalent type of OA. However, the percentages for publication in fully OA journals and OA via institutional repositories show similarly steep increases. Enabling data-driven decision-making regarding the implementation of OA in Germany at the institutional level, the results of this study furthermore can serve as a baseline to assess the impact recent transformative agreements with major publishers will likely have on scholarly communication.

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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

While thousands of ontologies exist on the web, a unified sys-tem for handling online ontologies - in particular with respect to discov-ery, versioning, access, quality-control, mappings - has not yet surfacedand users of ontologies struggle with many challenges. In this paper, wepresent an online ontology interface and augmented archive called DB-pedia Archivo, that discovers, crawls, versions and archives ontologies onthe DBpedia Databus. Based on this versioned crawl, different features,quality measures and, if possible, fixes are deployed to handle and sta-bilize the changes in the found ontologies at web-scale. A comparison toexisting approaches and ontology repositories is given.

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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

Nur noch Informationsethik, Informationskompetenz und Information Assessment? Doch gerade die Abschottung von anderen Disziplinen verstärkt die Isolation des "kleinen Faches" Informationswissenschaft in der Scientific Community. So bleiben ihr als letzte "eigenständige" Forschungsrandgebiete nur die, die Wolf Rauch als Keynote Speaker bereits in seinem einführenden, historisch-genetischen Vortrag zur Lage der Informationswissenschaft auf der ISI 2021 benannt hat: "Wenn die universitäre Informationswissenschaft (zumindest in Europa) wohl kaum eine Chance hat, im Bereich der Entwicklung von Systemen und Anwendungen wieder an die Spitze der Entwicklung vorzustoßen, bleiben ihr doch Gebiete, in denen ihr Beitrag in der kommenden Entwicklungsphase dringend erforderlich sein wird: Informationsethik, Informationskompetenz, Information Assessment" (Wolf Rauch: Was aus der Informationswissenschaft geworden ist; in: Thomas Schmidt; Christian Wolff (Eds): Information between Data and Knowledge. Schriften zur Informationswissenschaft 74, Regensburg, 2021, Seiten 20-22 - siehe auch die Rezeption des Beitrages von Rauch durch Johannes Elia Panskus, Was aus der Informationswissenschaft geworden ist. Sie ist in der Realität angekommen, in: Open Password, 17. März 2021). Das ist alles? Ernüchternd.

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Theme

Semantisches Umfeld in Indexierung u. Retrieval

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Content

"1. Use a range of different media 2. Access paywalled media content 3. Use an advertising and tracking blocker 4. Use alternatives to Google Search 5. Use alternatives to YouTube 6. Use alternatives to Facebook and Twitter 7. Caution with Wikipedia 8. Web browser, email, and internet access 9. Access books and scientific papers 10. Access deleted web content"