Research map / Continual learning / Online and replay-based
Tracking Logical Difference in Large-Scale Ontologies: A Forgetting-Based Approach
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use new forgetting prototype instead of LETHE | forgetting for logical difference · BioPortal, forget 50% | success rate: 50.5% -> 90.1% | Helped |
| use new prototype instead of FAME 1.0 | forgetting for logical difference · BioPortal, forget 50% | success rate: 66.7% -> 90.1% | Helped |
| use new prototype instead of LETHE | forgetting for logical difference · BioPortal, forget 50% | average time: 127.1s -> 2.4s | Helped |
| use new prototype instead of FAME 1.0 | forgetting for logical difference · BioPortal, forget 50% | average time: 2.0s -> 2.4s | No clear effect |
| introduce definer names conservatively (Ackermann and purify rules first) | forgetting method · SNOMED CT version pairs | speed vs LETHE | Helped |
Closest papers by meaning
- A Practical Approach to Forgetting in Description Logics with Nominals (AAAI 2020 · similarity 0.88)
- Minimal Macro-Based Rewritings of Formal Languages: Theory and Applications in Ontology Engineering (and Beyond) (AAAI 2024 · similarity 0.84)
- End-to-End Ontology Learning with Large Language Models (NeurIPS 2024 · similarity 0.83)
- Stable Model Semantics for Description Logic Terminologies (AAAI 2024 · similarity 0.83)
- Efficient Axiomatization of OWL 2 EL Ontologies from Data by Means of Formal Concept Analysis (AAAI 2024 · similarity 0.83)
- A Unified View on Forgetting and Strong Equivalence Notions in Answer Set Programming (AAAI 2024 · similarity 0.82)
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