Inductive Logic Programming: 30th International Conference, Ilp 2021, Virtual Event, October 25-27, 2021, Proceedings

ISBN-13: 9783030974534 / Angielski / Miękka / 2022 / 296 str.

Katzouris, Nikos

Opis

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.

Spis treści

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.