Daily Digest — Sep 29

APOLO: Automatic Prompt Optimization for Ontology Learning

• APOLO introduces an automatic prompt optimization framework for ontology learning using large language models, validated on the biomedical DOID and plant PO ontologies. • The methodology utilizes a multi-agent system to generate text-ontology training pairs and employs GEPA, a greedy evolutionary prompt optimizer built on DSPy, to train greedy and autoregressive learner architectures.

5W1H+Which: Context-Valid Semantic Indexing with Progressive Ontology Binding

• Researchers introduce 5W1H+Which, a semantic indexing framework that decouples source-grounded content extraction from versioned ontology binding to preserve query flexibility and support formal reasoning.

Scalable GNN-based Knowledge Graph Representation Learning with Efficient Message Passing

• Researchers introduce an extended Relational Sparse Matrix Multiplication framework that supports expressive composition functions such as 2x2 block-diagonal matrix multiplication, Givens rotation, and circular correlation for knowledge graph representation learning.

LLM-Guided Ontology-Driven Knowledge Graph Construction from Unstructured Text

• Schema-guided prompting significantly improves entity and relation extraction quality when building ontology-driven knowledge graphs from a private corpus of 80 power-grid incident reports.

Extracting ontology-compliant knowledge from scientific text describing irradiated materials using large language models

• The Eolas pipeline uses large language models to automatically transform scientific documents into knowledge graphs aligned with a specified ontology, significantly reducing extraction time from 30-90 minutes to a few minutes.

Knowledge Graph-Enhanced Zero-Shot Topic Classification: A Multi-Strategy Comparative Study

• A zero-shot multi-label topic classification framework augmented with per-article knowledge graphs is proposed and evaluated across fifteen LLMs and eight multi-label datasets. • Keyword-enhanced classification emerges as the top-performing base method, with six LLMs outperforming a sentence-encoder baseline without graph augmentation.

LLM-Assisted Ontology Engineering and Construction of a French Legal Knowledge Graph

• A two-stage LLM-assisted workflow is presented for constructing a French legal knowledge graph from maintenance regulations using GPT-4.1 and mistral-large-2512. • The methodology involves open extraction of entities and triples, label normalization via embedding-based fusion, and induction of candidate object properties, followed by closed extraction guided by the resulting ontology.

Initial Evaluation of Potential Bias in Coverage of Humans in Wikidata

• Women account for 28.71% (CI +/-0.04) of all humans in Wikidata with a stated gender. • Western Europe and North America represent approximately 53% of citizenship statements among humans in Wikidata. • Rural birthplaces are observed in 2.48% of classified Wikidata entries, significantly lower than the 27.4% global baseline.