Search (10 results, page 1 of 1)

0.041441064 = product of:
  0.08288213 = sum of:
    0.08288213 = product of:
      0.24864638 = sum of:
        0.24864638 = weight(_text_:3a in 862) [ClassicSimilarity], result of:
          0.24864638 = score(doc=862,freq=2.0), product of:
            0.4424171 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.052184064 = queryNorm
            0.56201804 = fieldWeight in 862, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.046875 = fieldNorm(doc=862)
      0.33333334 = coord(1/3)
  0.5 = coord(1/2)

Source

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

0.029996406 = product of:
  0.059992813 = sum of:
    0.059992813 = product of:
      0.119985625 = sum of:
        0.119985625 = weight(_text_:22 in 3227) [ClassicSimilarity], result of:
          0.119985625 = score(doc=3227,freq=4.0), product of:
            0.1827397 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052184064 = queryNorm
            0.6565931 = fieldWeight in 3227, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.09375 = fieldNorm(doc=3227)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

7. 2.1996 22:38:41

Pages

22 S

0.019058352 = product of:
  0.038116705 = sum of:
    0.038116705 = product of:
      0.07623341 = sum of:
        0.07623341 = weight(_text_:systems in 3147) [ClassicSimilarity], result of:
          0.07623341 = score(doc=3147,freq=2.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = queryNorm
            0.47535738 = fieldWeight in 3147, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.109375 = fieldNorm(doc=3147)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

0.01767555 = product of:
  0.0353511 = sum of:
    0.0353511 = product of:
      0.0707022 = sum of:
        0.0707022 = weight(_text_:22 in 3161) [ClassicSimilarity], result of:
          0.0707022 = score(doc=3161,freq=2.0), product of:
            0.1827397 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052184064 = queryNorm
            0.38690117 = fieldWeight in 3161, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=3161)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

2. 2.1996 15:40:22

0.014147157 = product of:
  0.028294314 = sum of:
    0.028294314 = product of:
      0.056588627 = sum of:
        0.056588627 = weight(_text_:systems in 640) [ClassicSimilarity], result of:
          0.056588627 = score(doc=640,freq=6.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = queryNorm
            0.35286134 = fieldWeight in 640, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.046875 = fieldNorm(doc=640)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Involving users in early phases of software development has become a common strategy as it enables developers to consider user needs from the beginning. Once a system is in production, new opportunities to observe, evaluate and learn from users emerge as more information becomes available. Gathering information from users to continuously evaluate their behavior is a common practice for commercial software, while the Cranfield paradigm remains the preferred option for Information Retrieval (IR) and recommendation systems in the academic world. Here we introduce the Infrastructures for Living Labs STELLA project which aims to create an evaluation infrastructure allowing experimental systems to run along production web-based academic search systems with real users. STELLA combines user interactions and log files analyses to enable large-scale A/B experiments for academic search.

0.010890487 = product of:
  0.021780973 = sum of:
    0.021780973 = product of:
      0.043561947 = sum of:
        0.043561947 = weight(_text_:systems in 6235) [ClassicSimilarity], result of:
          0.043561947 = score(doc=6235,freq=2.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = queryNorm
            0.2716328 = fieldWeight in 6235, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.0625 = fieldNorm(doc=6235)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Almost everybody seems to be talking about document delivery and digital libraries. Library networks are starting joint ventures with journal subscription agencies and offering electronic tables of contents. Integrated systems for image management and document management are being implemented. Academic networks and Internet are being used at an exponential rate. At the same time budgets for the acquisition of books and journals are shrinking and alternatives for the delivery of information are being discussed. Are there alternatives and what will be their impact?

0.010890487 = product of:
  0.021780973 = sum of:
    0.021780973 = product of:
      0.043561947 = sum of:
        0.043561947 = weight(_text_:systems in 3751) [ClassicSimilarity], result of:
          0.043561947 = score(doc=3751,freq=2.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = queryNorm
            0.2716328 = fieldWeight in 3751, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.0625 = fieldNorm(doc=3751)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Source

Vortrag, anläßlich der Tagung 'EOCONSID '95: 2nd Meeting on Knowledge Organization in Information and Documentation Systems', Madrid, Nov. 16-17, 1995

0.009625921 = product of:
  0.019251842 = sum of:
    0.019251842 = product of:
      0.038503684 = sum of:
        0.038503684 = weight(_text_:systems in 5787) [ClassicSimilarity], result of:
          0.038503684 = score(doc=5787,freq=4.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = queryNorm
            0.24009174 = fieldWeight in 5787, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5787)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This study considers the expressiveness (that is the expressive power or expressivity) of different types of Knowledge Organization Systems (KOS) and discusses its potential to be machine-processable in the context of the Semantic Web. For this purpose, the theoretical foundations of KOS are reviewed based on conceptualizations introduced by the Functional Requirements for Subject Authority Data (FRSAD) and the Simple Knowledge Organization System (SKOS); natural language processing techniques are also implemented. Applying a comparative analysis, the dataset comprises a thesaurus (Eurovoc), a subject headings system (LCSH) and a classification scheme (DDC). These are compared with an ontology (CIDOC-CRM) by focusing on how they define and handle concepts and relations. It was observed that LCSH and DDC focus on the formalism of character strings (nomens) rather than on the modelling of semantics; their definition of what constitutes a concept is quite fuzzy, and they comprise a large number of complex concepts. By contrast, thesauri have a coherent definition of what constitutes a concept, and apply a systematic approach to the modelling of relations. Ontologies explicitly define diverse types of relations, and are by their nature machine-processable. The paper concludes that the potential of both the expressiveness and machine processability of each KOS is extensively regulated by its structural rules. It is harder to represent subject headings and classification schemes as semantic networks with nodes and arcs, while thesauri are more suitable for such a representation. In addition, a paradigm shift is revealed which focuses on the modelling of relations between concepts, rather than the concepts themselves.

0.008837775 = product of:
  0.01767555 = sum of:
    0.01767555 = product of:
      0.0353511 = sum of:
        0.0353511 = weight(_text_:22 in 1171) [ClassicSimilarity], result of:
          0.0353511 = score(doc=1171,freq=2.0), product of:
            0.1827397 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052184064 = queryNorm
            0.19345059 = fieldWeight in 1171, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1171)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

23.11.2023 19:07:22

0.007700737 = product of:
  0.015401474 = sum of:
    0.015401474 = product of:
      0.030802948 = sum of:
        0.030802948 = weight(_text_:systems in 851) [ClassicSimilarity], result of:
          0.030802948 = score(doc=851,freq=4.0), product of:
            0.16037072 = queryWeight, product of:
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.052184064 = queryNorm
            0.19207339 = fieldWeight in 851, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0731742 = idf(docFreq=5561, maxDocs=44218)
              0.03125 = fieldNorm(doc=851)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.