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Inductive Reasoning about Ontologies Using Conceptual Spaces

AAAI 2017 · Large language models › Retrieval and knowledge · approach: Knowledge graph reasoning with LLMs · 10 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Bayesian likelihood-ratio induction over conceptual-space features instead of nearest-neighbour similaritysimilarity-based reasoning · SUMO ABox, |X|>=50F1: 0.379 -> 0.757Helped
same changesimilarity-based reasoning · SUMO ABox, |X|>=50Precision: 0.249 -> 0.816Helped
same changesimilarity-based reasoning · SUMO ABox, |X|<50F1: 0.773 -> 0.835Helped
same changesimilarity-based reasoning · SUMO ABox, |X|<10F1: 0.874 -> 0.930Helped
same changesimilarity-based reasoning · SUMO ABox, |X|<5F1: 1.0 -> 1.0No clear effect
same change (variant 1)similarity-based reasoning · SUMO TBox, |X|>=50F1: 0.491 -> 0.613Helped
same change (variant 1)WordNet hyponym baseline · SUMO TBox, |X|>=50F1: 0.274 -> 0.613Helped
same change (variant 1)WordNet hyponym baseline · SUMO TBox, |X|>=50Precision: 1.0 -> 0.714Hurt
use instance-based Gaussian concept representation (variant 2) instead of point concept representation (variant 1)BR · SUMO TBox, |X|>=50F1: 0.613 -> 0.795Helped
Bayesian (normal-inverse-chi2) estimates instead of maximum likelihoodconceptual-space induction · toy example (Figure 1)confidenceHelped

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