@article{1444702,
      recid = {1444702},
      author = {Katzouris, Nikos, and Artikis, Alexander,},
      title = {Inductive logic programming : 30th International  Conference, ILP 2021, Virtual event, October 25-27, 2021,  Proceedings /. ILP (Conference)},
      pages = {1 online resource (x, 283 pages) :},
      note = {Includes author index.},
      abstract = {This book constitutes the refereed conference proceedings  of the 30th International Conference on Inductive Logic  Programming, ILP 2032, held in October 2021. Due to  COVID-19 pandemic the conference was held virtually. The 16  papers and 3 short papers presented were carefully reviewed  and selected from 19 submissions. Inductive Logic  Programming (ILP) is a subfield of machine learning, which  originally relied on logic programming as a uniform  representation language for expressing examples, background  knowledge and hypotheses. Due to its strong representation  formalism, based on first-order logic, ILP provides an  excellent means for multi-relational learning and data  mining, and more generally for learning from structured  data.},
      url = {http://library.usi.edu/record/1444702},
      doi = {https://doi.org/10.1007/978-3-030-97454-1},
}