Research map / Learning theory and algorithms / Combinatorial and search optimization
Efficient Concept Induction for Description Logics
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ECII's materialization and set-based candidate evaluation instead of repeated reasoner checks | Concept induction · ADE20k I–IV | runtime (sec): 577.3 -> 1.966; 983.4 -> 20.8; 4,500 -> 51; 4,500 -> 116 | Helped |
| Use ECII rather than DL-Learner | Concept induction · Trains and Poker | accuracy (α3): 1.000 -> 1.000 | No clear effect |
| Use ECII rather than DL-Learner | Concept induction · Forte and Moral Reasoner | accuracy (α3): 0.965 -> 0.642; 1.000 -> 0.785 | Hurt |
| Use ECII rather than DL-Learner | Concept induction · ADE20k III | accuracy (α3): 0.375 -> 0.937 | Helped |
| Keep common types instead of deleting them (ablation) | ECII · Forte | approximation accuracy (α2): 0.733 -> 1.000 | Helped |
| Keep common types instead of deleting them (ablation) | ECII · ADE20k I | approximation accuracy (α2): 0.744 -> 1.000 | Helped |
| Keep common types instead of deleting them (ablation) | ECII · ADE20k II | approximation accuracy (α2): 0.846 -> 0.900 | Helped |
| Keep common types instead of deleting them (ablation) | ECII · ADE20k IV | approximation accuracy (α2): 0.660 -> 0.608 | Hurt |
Closest papers by meaning
- Stable Model Semantics for Description Logic Terminologies (AAAI 2024 · similarity 0.86)
- Least General Generalizations in Description Logic: Verification and Existence (AAAI 2020 · similarity 0.85)
- INDUCTION: Finite-Structure Concept Synthesis in First-Order Logic (ICML 2026 · similarity 0.85)
- Extending Description Logics with Generic Concepts – the Case of Terminologies (AAAI 2026 · similarity 0.85)
- Automated Rule Base Completion as Bayesian Concept Induction (AAAI 2019 · similarity 0.85)
- Efficient Axiomatization of OWL 2 EL Ontologies from Data by Means of Formal Concept Analysis (AAAI 2024 · similarity 0.82)
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