| Full text | |
| Author(s): |
Ruschel, Andrey
;
Gusmao, Arthur Colombini
;
Cozman, Fabio Gagliardi
Total Authors: 3
|
| Document type: | Journal article |
| Source: | INTERNATIONAL JOURNAL OF APPROXIMATE REASONING; v. 171, p. 25-pg., 2024-06-13. |
| Abstract | |
Completion of large-scale knowledge bases, such as DBPedia or Freebase, often relies on embedding models that turn symbolic relations into vector -based representations. Such embedding models are rather opaque to the human user. Research in interpretability has emphasized non -relational classifiers, such as deep neural networks, and has devoted less effort to opaque models extracted from relational structures, such as knowledge graph embeddings. We introduce techniques that produce explanations, expressed as logical rules, for predictions based on the embeddings of knowledge graphs. Algorithms build explanations out of paths in an input knowledge graph, searched through contextual and heuristic cues. (AU) | |
| FAPESP's process: | 19/07665-4 - Center for Artificial Intelligence |
| Grantee: | Fabio Gagliardi Cozman |
| Support Opportunities: | Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program |
| FAPESP's process: | 17/19007-6 - Inference and learning algorithms for probabilistic logic programming |
| Grantee: | Arthur Colombini Gusmão |
| Support Opportunities: | Scholarships in Brazil - Master |