Search (23 results, page 2 of 2)

  • × language_ss:"e"
  • × type_ss:"el"
  • × year_i:[2020 TO 2030}
  1. Suominen, O.; Koskenniemi, I.: Annif Analyzer Shootout : comparing text lemmatization methods for automated subject indexing (2022) 0.00
    1.707938E-4 = product of:
      0.0039282576 = sum of:
        0.0039282576 = product of:
          0.007856515 = sum of:
            0.007856515 = weight(_text_:1 in 658) [ClassicSimilarity], result of:
              0.007856515 = score(doc=658,freq=2.0), product of:
                0.057894554 = queryWeight, product of:
                  2.4565027 = idf(docFreq=10304, maxDocs=44218)
                  0.023567878 = queryNorm
                0.13570388 = fieldWeight in 658, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.4565027 = idf(docFreq=10304, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=658)
          0.5 = coord(1/2)
      0.04347826 = coord(1/23)
    
    Date
    1. 9.2022 15:56:25
  2. Aydin, Ö.; Karaarslan, E.: OpenAI ChatGPT generated literature review: : digital twin in healthcare (2022) 0.00
    1.3663506E-4 = product of:
      0.0031426062 = sum of:
        0.0031426062 = product of:
          0.0062852125 = sum of:
            0.0062852125 = weight(_text_:1 in 851) [ClassicSimilarity], result of:
              0.0062852125 = score(doc=851,freq=2.0), product of:
                0.057894554 = queryWeight, product of:
                  2.4565027 = idf(docFreq=10304, maxDocs=44218)
                  0.023567878 = queryNorm
                0.1085631 = fieldWeight in 851, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.4565027 = idf(docFreq=10304, maxDocs=44218)
                  0.03125 = fieldNorm(doc=851)
          0.5 = coord(1/2)
      0.04347826 = coord(1/23)
    
    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.
  3. Frey, J.; Streitmatter, D.; Götz, F.; Hellmann, S.; Arndt, N.: DBpedia Archivo (2020) 0.00
    1.19555676E-4 = product of:
      0.0027497804 = sum of:
        0.0027497804 = product of:
          0.005499561 = sum of:
            0.005499561 = weight(_text_:1 in 53) [ClassicSimilarity], result of:
              0.005499561 = score(doc=53,freq=2.0), product of:
                0.057894554 = queryWeight, product of:
                  2.4565027 = idf(docFreq=10304, maxDocs=44218)
                  0.023567878 = queryNorm
                0.09499271 = fieldWeight in 53, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.4565027 = idf(docFreq=10304, maxDocs=44218)
                  0.02734375 = fieldNorm(doc=53)
          0.5 = coord(1/2)
      0.04347826 = coord(1/23)
    
    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.