Statistical Relational Learning with Formal Ontologies
Abstract
We propose a learning approach for integrating formal knowledge into statistical inference by exploiting ontologies as a semantically rich and fully formal representation of prior knowledge. The logical constraints deduced from ontologies can be utilized to enhance and control the learning task by enforcing description logic satisfiability in a latent multi-relational graphical model. To demonstrate the feasibility of our approach we provide experiments using real world social network data in form of a $\mathcal{SHOIN}(D)$ ontology. The results illustrate two main practical advancements: First, entities and entity relationships can be analyzed via the latent model structure. Second, enforcing the ontological constraints guarantees that the learned model does not predict inconsistent relations. In our experiments, this leads to an improved predictive performance.
Cite
Text
Rettinger et al. "Statistical Relational Learning with Formal Ontologies." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009. doi:10.1007/978-3-642-04174-7_19Markdown
[Rettinger et al. "Statistical Relational Learning with Formal Ontologies." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009.](https://mlanthology.org/ecmlpkdd/2009/rettinger2009ecmlpkdd-statistical/) doi:10.1007/978-3-642-04174-7_19BibTeX
@inproceedings{rettinger2009ecmlpkdd-statistical,
title = {{Statistical Relational Learning with Formal Ontologies}},
author = {Rettinger, Achim and Nickles, Matthias and Tresp, Volker},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2009},
pages = {286-301},
doi = {10.1007/978-3-642-04174-7_19},
url = {https://mlanthology.org/ecmlpkdd/2009/rettinger2009ecmlpkdd-statistical/}
}