Summary of Simple and Interpretable Probabilistic Classifiers For Knowledge Graphs, by Christian Riefolo and Nicola Fanizzi and Claudia D’amato
Simple and Interpretable Probabilistic Classifiers for Knowledge Graphs
by Christian Riefolo, Nicola Fanizzi, Claudia d’Amato
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to learning probabilistic classifiers from incomplete data in Knowledge Graphs expressed in Description Logics. The authors propose an inductive method based on simple belief networks, extending Naive Bayes classifiers by connecting them to a lower layer of mixture Bernoullis. They demonstrate how these models can be converted into probabilistic axioms, enhancing interpretability and allowing for expert knowledge initialization. An empirical evaluation tests the effectiveness of the models on random classification problems with various ontologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how computers can learn from incomplete information in large networks of data. The authors created a new way to do this using simple “believe” networks, which are like teams of mini-classifiers working together. They show that these networks can be turned into clear rules and even use expert knowledge to get started. To test their idea, they tried it on many different problems with different types of data and saw how well it worked. |
Keywords
» Artificial intelligence » Classification » Naive bayes
