An Efficient Parallelized Ontology Network-Based Semantic Similarity Measure for Big Biomedical Document Clustering
Semantic mining is always a challenge for big biomedical text data. Ontology has been widely proved and used to extract semantic information. However, the process of ontology-based semantic similarity calculation is so complex that it cannot measure the similarity for big text data. To solve this pr...
Guardado en:
Autores principales: | Meijing Li, Tianjie Chen, Keun Ho Ryu, Cheng Hao Jin |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Hindawi Limited
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/81dfa9c1b4484390b03555bc8184cf6a |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Social license for the use of big data in the COVID-19 era
por: James A. Shaw, et al.
Publicado: (2020) -
COVID-19 information retrieval with deep-learning based semantic search, question answering, and abstractive summarization
por: Andre Esteva, et al.
Publicado: (2021) -
Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies
por: Ning Shang, et al.
Publicado: (2021) -
The ontology of fast food facts: conceptualization of nutritional fast food data for consumers and semantic web applications
por: Muhammad Amith, et al.
Publicado: (2021) -
Interaction of Diet/Lifestyle Intervention and TCF7L2 Genotype on Glycemic Control and Adiposity among Overweight or Obese Adults: Big Data from Seven Randomized Controlled Trials Worldwide
por: Tao Huang, et al.
Publicado: (2021)