arXiv Machine Learning By Caleb Princewill Nwokocha

Rule Extraction in Machine Learning: Chat Incremental Pattern Constructor

Read the original on arXiv Machine Learning →

arXiv:2208. 00335v5 Announce Type: replace Abstract: Rule extraction is a central problem in interpretable machine learning because it seeks to convert opaque predictive behavior into human-readable symbolic structure.

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arXiv Computation and Language
Aug 25

Beyond Factual Knowledge: Benchmarking and Learning Step-Level Procedural Rule Reasoning in Large Language Models

arXiv:2608.22753v1 Announce Type: new Abstract: Large language models (LLMs) excel at text understanding and generation, yet still struggle to reliably understand and apply externally provided proced...

By Bohan Yu, Pengfei Cao, Chen Han, Chenxi Zhou, Zhiheng Zhang, Zhiyang Xie, Wenhao Teng, Xiangwen Liao, Jun Zhao, Kang Liu
arXiv AI
Aug 25

SLogic: Subgraph-Informed Logical Rule Learning for Knowledge Graph Completion

SLogic introduces a subgraph-informed approach to logical rule learning for knowledge graph completion, assigning query-dependent scores to rules instead of a single global weight. The framework uses a context-aware scoring function that evaluates the importance of a rule based on the local subgraph around the query’s head entity, aligning with the specificity principle of commonsense reasoning. Experiments on benchmark datasets demonstrate that SLogic performs competitively with other rule-based methods while producing human-readable, query-specific explanations.

By Trung Hoang Le, Tran Cao Son, Ishtiaq Ahmed, Huiping Cao