Summary of Combining Machine Learning and Ontology: a Systematic Literature Review, by Sarah Ghidalia et al.
Combining Machine Learning and Ontology: A Systematic Literature Review
by Sarah Ghidalia, Ouassila Labbani Narsis, Aurélie Bertaux, Christophe Nicolle
First submitted to arxiv on: 15 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 Machine learning educators can expect to learn about a systematic literature review that explores the combination of inductive and deductive reasoning in artificial intelligence systems. The study analyzed 128 articles that integrate machine learning and ontologies, identifying three main categories: learning-enhanced ontologies, semantic data mining, and learning and reasoning systems. These hybrid approaches utilize various machine learning algorithms, including those used for inductive reasoning. By examining these studies, researchers can gain insights into the diverse techniques used to combine deductive and inductive reasoning in AI systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence is getting smarter by combining two ways of thinking: machine learning (like a detective figuring things out) and ontologies (like a dictionary that explains what words mean). Researchers looked at 128 studies that mixed these two ideas together. They found three main ways to do this: making ontologies more intelligent, finding patterns in data, and creating systems that learn and reason. This is important because it helps us create better AI systems that can make decisions and solve problems. |
Keywords
* Artificial intelligence * Machine learning
