
Inductive Logic Programming
30th International Conference, ILP 2021, Virtual Event, October 25-27, 2021, Proceedings
Springer (Publisher)
Published on 24. February 2022
Book
Paperback/Softback
X, 283 pages
978-3-030-97453-4 (ISBN)
Description
This book constitutes the refereed conference proceedings of the 30th International Conference on Inductive Logic Programming, ILP 2021, 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.
More details
Series
Edition
1st ed. 2022
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
21 s/w Abbildungen, 40 farbige Abbildungen
X, 283 p. 61 illus., 40 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 17 mm
Weight
452 gr
ISBN-13
978-3-030-97453-4 (9783030974534)
DOI
10.1007/978-3-030-97454-1
Schweitzer Classification
Other editions
Additional editions

Nikos Katzouris | Alexander Artikis
Inductive Logic Programming
30th International Conference, ILP 2021, Virtual Event, October 25-27, 2021, Proceedings
E-Book
02/2022
Springer
€69.54
Available for download
Content
Embedding Models for Knowledge Graphs Induced by Clusters of Relations and Background Knowledge.- Fanizzi Automatic Conjecturing of P-Recursions Using Lifted Inference.- Machine learning of microbial interactions using Abductive ILP and Hypothesis Frequency/Compression Estimation.- Answer-Set Programs for Reasoning about Counterfactual Interventions and Responsibility Scores for Classification.- Reyes Synthetic Datasets and Evaluation Tools for Inductive Neural Reasoning.- Using Domain-Knowledge to Assist Lead Discovery in Early-Stage Drug Design.- Non-Parametric Learning of Embeddings for Relational Data using Gaifman Locality Theorem.- Ontology Graph Embeddings and ILP for Financial Forecasting.- Transfer learning for boosted relational dependency networks through genetic algorithm.- Online Learning of Logic Based Neural Network Structures.- Programmatic policy extraction by iterative local search.- Mapping across relational domains for transfer learning with word embeddings-based similarity.- A First Step Towards Even More Sparse Encodings of Probability Distributions.- Feature Learning by Least Generalization.- Learning Logic Programs Using Neural Networks by Exploiting Symbolic Invariance.- Learning and revising dynamic temporal theories in the full Discrete Event Calculus.- Human-like rule learning from images using one-shot hypothesis derivation.- Generative Clausal Networks: Relational Decision Trees as Probabilistic Circuits.- A Simulated Annealing Meta-heuristic for Concept Learning in Description Logics.