Search (5 results, page 1 of 1)

0.05748579 = product of:
  0.11497158 = sum of:
    0.073112294 = weight(_text_:digital in 39) [ClassicSimilarity], result of:
      0.073112294 = score(doc=39,freq=4.0), product of:
        0.19770671 = queryWeight, product of:
          3.944552 = idf(docFreq=2326, maxDocs=44218)
          0.050121464 = queryNorm
        0.36980176 = fieldWeight in 39, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.944552 = idf(docFreq=2326, maxDocs=44218)
          0.046875 = fieldNorm(doc=39)
    0.041859288 = product of:
      0.083718576 = sum of:
        0.083718576 = weight(_text_:project in 39) [ClassicSimilarity], result of:
          0.083718576 = score(doc=39,freq=4.0), product of:
            0.21156175 = queryWeight, product of:
              4.220981 = idf(docFreq=1764, maxDocs=44218)
              0.050121464 = queryNorm
            0.39571697 = fieldWeight in 39, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.220981 = idf(docFreq=1764, maxDocs=44218)
              0.046875 = fieldNorm(doc=39)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Digital cultural heritage resources are widely available on the web through the digital libraries of heritage institutions. To address the difficulties of discoverability in cultural heritage, the common practice is metadata aggregation, where centralized efforts like Europeana facilitate discoverability by collecting the resources' metadata. We present the results of the linked data aggregation task conducted within the Europeana Common Culture project, which attempted an innovative approach to aggregation based on linked data made available by cultural heritage institutions. This task ran for one year with participation of eleven organizations, involving the three member roles of the Europeana network: data providers, intermediary aggregators, and the central aggregation hub, Europeana. We report on the challenges that were faced by data providers, the standards and specifications applied, and the resulting aggregated metadata.

0.014923984 = product of:
  0.059695937 = sum of:
    0.059695937 = weight(_text_:digital in 851) [ClassicSimilarity], result of:
      0.059695937 = score(doc=851,freq=6.0), product of:
        0.19770671 = queryWeight, product of:
          3.944552 = idf(docFreq=2326, maxDocs=44218)
          0.050121464 = queryNorm
        0.30194187 = fieldWeight in 851, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.944552 = idf(docFreq=2326, maxDocs=44218)
          0.03125 = fieldNorm(doc=851)
  0.25 = coord(1/4)

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.

0.014799493 = product of:
  0.059197973 = sum of:
    0.059197973 = product of:
      0.11839595 = sum of:
        0.11839595 = weight(_text_:project in 5407) [ClassicSimilarity], result of:
          0.11839595 = score(doc=5407,freq=2.0), product of:
            0.21156175 = queryWeight, product of:
              4.220981 = idf(docFreq=1764, maxDocs=44218)
              0.050121464 = queryNorm
            0.5596283 = fieldWeight in 5407, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.220981 = idf(docFreq=1764, maxDocs=44218)
              0.09375 = fieldNorm(doc=5407)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

0.013535989 = product of:
  0.054143958 = sum of:
    0.054143958 = weight(_text_:library in 5416) [ClassicSimilarity], result of:
      0.054143958 = score(doc=5416,freq=4.0), product of:
        0.1317883 = queryWeight, product of:
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.050121464 = queryNorm
        0.4108404 = fieldWeight in 5416, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.078125 = fieldNorm(doc=5416)
  0.25 = coord(1/4)

0.009866329 = product of:
  0.039465316 = sum of:
    0.039465316 = product of:
      0.07893063 = sum of:
        0.07893063 = weight(_text_:project in 850) [ClassicSimilarity], result of:
          0.07893063 = score(doc=850,freq=2.0), product of:
            0.21156175 = queryWeight, product of:
              4.220981 = idf(docFreq=1764, maxDocs=44218)
              0.050121464 = queryNorm
            0.37308553 = fieldWeight in 850, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.220981 = idf(docFreq=1764, maxDocs=44218)
              0.0625 = fieldNorm(doc=850)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

This is a short project that serves as an inspiration for a forthcoming paper, which will explore the technical side of ChatGPT and the ethical issues it presents for academic researchers, which will result in a peer-reviewed publication. This demonstrates that capacities of ChatGPT as a "chatbot" that is far more advanced than many alternatives available today and may even be able to be used to draft entire academic manuscripts for researchers. ChatGPT is available via https://chat.openai.com/chat.