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arXiv:2611.02842 · v1

NIR: Approximate Neural Instance Retrieval for ALCIQ

N'Dah Jean KouagouPritilata SahaCaglar DemirAxel-Cyrille Ngonga Ngomo
cs.LG
首次提交 2026-11-02 17:44
版本更新 2026-11-02 17:44
雷达观测 2026-11-04 23:42
arXiv ↗PDF ↗

摘要

Instance retrieval is the main bottleneck of concept-learning systems based on refinement operators. We present NIR, a family of neural instance retrievers for ALCIQ that output graded membership scores from knowledge graph embeddings. We compare tokenization-based encoders (GRU, LSTM, Transformer) with a Compositional encoder that mirrors DL operators via primitive sub-networks. On eight benchmarks, all variants approximate Pellet closely on complete knowledge bases, while the Transformer model is most robust under incompleteness. The source code of our approach is at https://github.com/dice-group/owlapy.

作者备注：17 pages, 3 figures, 7 tables

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v1 · 2026-11-02 17:44 · 最新观测版本

NIR: Approximate Neural Instance Retrieval for ALCIQ

Instance retrieval is the main bottleneck of concept-learning systems based on refinement operators. We present NIR, a family of neural instance retrievers for ALCIQ that output graded membership scores from knowledge graph embeddings. We compare tokenization-based encoders (GRU, LSTM, Transformer) with a Compositional encoder that mirrors DL operators via primitive sub-networks. On eight benchmarks, all variants approximate Pellet closely on complete knowledge bases, while the Transformer model is most robust under incompleteness. The source code of our approach is at https://github.com/dice-group/owlapy.

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