Search (3 results, page 1 of 1)

0.014540519 = product of:
  0.072702594 = sum of:
    0.072702594 = weight(_text_:thesaurus in 723) [ClassicSimilarity], result of:
      0.072702594 = score(doc=723,freq=2.0), product of:
        0.23732872 = queryWeight, product of:
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.051357865 = queryNorm
        0.30633712 = fieldWeight in 723, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.046875 = fieldNorm(doc=723)
  0.2 = coord(1/5)

Abstract

Manual subject indexing in libraries is a time-consuming and costly process and the quality of the assigned subjects is affected by the cataloger's knowledge on the specific topics contained in the book. Trying to solve these issues, we exploited the opportunities arising from artificial intelligence to develop Kratt: a prototype of an automatic subject indexing tool. Kratt is able to subject index a book independent of its extent and genre with a set of keywords present in the Estonian Subject Thesaurus. It takes Kratt approximately one minute to subject index a book, outperforming humans 10-15 times. Although the resulting keywords were not considered satisfactory by the catalogers, the ratings of a small sample of regular library users showed more promise. We also argue that the results can be enhanced by including a bigger corpus for training the model and applying more careful preprocessing techniques.

0.012117098 = product of:
  0.06058549 = sum of:
    0.06058549 = weight(_text_:thesaurus in 341) [ClassicSimilarity], result of:
      0.06058549 = score(doc=341,freq=2.0), product of:
        0.23732872 = queryWeight, product of:
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.051357865 = queryNorm
        0.2552809 = fieldWeight in 341, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.0390625 = fieldNorm(doc=341)
  0.2 = coord(1/5)

Abstract

Purpose Based on the highlights of The Metropolitan Museum of Art's collection, the purpose of this paper is to examine the similarities and differences between the subject keywords tags assigned by the museum and those produced by three computer vision systems. Design/methodology/approach This paper uses computer vision tools to generate the data and the Getty Research Institute's Art and Architecture Thesaurus (AAT) to compare the subject keyword tags. Findings This paper finds that there are clear opportunities to use computer vision technologies to automatically generate tags that expand the terms used by the museum. This brings a new perspective to the collection that is different from the traditional art historical one. However, the study also surfaces challenges about the accuracy and lack of context within the computer vision results. Practical implications This finding has important implications on how these machine-generated tags complement the current taxonomies and vocabularies inputted in the collection database. In consequence, the museum needs to consider the selection process for choosing which computer vision system to apply to their collection. Furthermore, they also need to think critically about the kind of tags they wish to use, such as colors, materials or objects. Originality/value The study results add to the rapidly evolving field of computer vision within the art information context and provide recommendations of aspects to consider before selecting and implementing these technologies.

0.012117098 = product of:
  0.06058549 = sum of:
    0.06058549 = weight(_text_:thesaurus in 1024) [ClassicSimilarity], result of:
      0.06058549 = score(doc=1024,freq=2.0), product of:
        0.23732872 = queryWeight, product of:
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.051357865 = queryNorm
        0.2552809 = fieldWeight in 1024, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6210785 = idf(docFreq=1182, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1024)
  0.2 = coord(1/5)

Abstract

There are several ways to employ machine learning for automating subject indexing. One popular strategy is to utilize a supervised learning algorithm to train a model on a set of documents that have been manually indexed by subject matter using a standard vocabulary. The resulting model can then predict the subject of new and previously unseen documents by identifying patterns learned from the training data. To do this, the first step is to gather a large dataset of documents and manually assign each document a set of subject keywords/descriptors from a controlled vocabulary (e.g., from Agrovoc). Next, the dataset (obtained from Agris) can be divided into - i) a training dataset, and ii) a test dataset. The training dataset is used to train the model, while the test dataset is used to evaluate the model's performance. Machine learning can be a powerful tool for automating the process of subject indexing. This research is an attempt to apply Annif (http://annif. org/), an open-source AI/ML framework, to autogenerate subject keywords/descriptors for documentary resources in the domain of agriculture. The training dataset is obtained from Agris, which applies the Agrovoc thesaurus as a vocabulary tool (https://www.fao.org/agris/download).