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Bisociative Literature-Based Discovery Methods with Tutorials in Python /

This monograph introduces the field of bisociative literature-based discovery (LBD) by first explaining the underlying LBD principles and techniques, followed by the presentation of bisociative LBD techniques and applications developed by the authors. LBD is a process of uncovering new knowledge by...

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Ngā kaituhi matua: Lavrač, Nada (Author), Cestnik, Bojan (Author), Kastrin, Andrej (Author)
Kaituhi rangatōpū: SpringerLink (Online service)
Hōputu: Tāhiko īPukapuka
Reo:Ingarihi
I whakaputaina: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Putanga:1st ed. 2025.
Ngā marau:
Data mining.
Quantitative research.
Machine learning.
Artificial intelligence > Data processing.
Information storage and retrieval systems.
Data Mining and Knowledge Discovery.
Data Analysis and Big Data.
Machine Learning.
Data Science.
Information Storage and Retrieval.
Urunga tuihono:https://doi.org/10.1007/978-3-031-96863-1
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Rārangi ihirangi:
  • 1. Introduction
  • 2. History, Resources and Tools
  • 3. Background Technologies
  • 4. Benchmark Data and Reusable Python Code
  • 5. Text Mining for Closed Discovery
  • 6. Outlier-based Closed Discovery
  • 7. Semantic and Outlier-based Open Discovery
  • 8. Network-based Closed Discovery
  • 9. Embedding-based Closed Discovery
  • 10. Research Trends and Lessons Learned.

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