Piano Teaching Knowledge Graph Construction Based on Cross-Media Data Analysis and Semantic Network.

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  • Author(s): Li H;Li H
  • Source:
    Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 Jun 16; Vol. 2022, pp. 5499593. Date of Electronic Publication: 2022 Jun 16 (Print Publication: 2022).
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Hindawi Pub. Corp Country of Publication: United States NLM ID: 101279357 Publication Model: eCollection Cited Medium: Internet ISSN: 1687-5273 (Electronic) NLM ISO Abbreviation: Comput Intell Neurosci Subsets: MEDLINE
    • Publication Information:
      Original Publication: New York, NY : Hindawi Pub. Corp.
    • Subject Terms:
    • Abstract:
      With the rapid development of information technology and mobile Internet, digital image, text, audio, video, and other cross-media data are growing explosively, which has changed people's way of life and work. In view of the issues of negative studying effectivity and challenging attention of college students in the modern-day piano instructing process, this paper puts forward the application of knowledge Atlas technology in piano teaching and constructs a multimodal knowledge Atlas of piano teaching based on deep neural network, so as to make piano teaching more intelligent and improve students' learning efficiency and learning interest. How to realize the semantic association understanding of cross-media data is the core problem of cross-media semantic analysis. First, this paper introduces the basic rules of ontology construction and the basic method of establishing general knowledge graph are introduced. Then, taking the piano teaching content as an example, natural language sentences can be expressed and stored with cross-media data using semantic network. The mathematical understanding is extracted in accordance to the herbal language processing technology, and the entities are fused in accordance to the frequent semantic similarity detection between extraordinary entities, so as to decrease the redundancy and repetition fee of entities and the complexity of the graph. The fused new knowledge is processed according to the quality evaluation rules, the qualified part is added to the knowledge base, and then the above steps are iterated to update the database. The great overall performance of piano instructing understanding graph mannequin primarily based on semantic network is validated through experiments.
      Competing Interests: The author declares that they have no conflicts of interest or personal relationships that could have appeared to influence the work reported in this paper.
      (Copyright © 2022 Han Li.)
    • References:
      IEEE J Biomed Health Inform. 2015 Jan;19(1):210-8. (PMID: 25029520)
    • Publication Date:
      Date Created: 20220627 Date Completed: 20220628 Latest Revision: 20220716
    • Publication Date:
      20240105
    • Accession Number:
      PMC9225848
    • Accession Number:
      10.1155/2022/5499593
    • Accession Number:
      35755737