{"slug": "on-measuring-semantic-preservation-in-legal-ontology-learning", "title": "On Measuring Semantic Preservation in Legal Ontology Learning", "summary": "A new arXiv paper (2608.12326v1) proposes an evaluation methodology that measures semantic loss in ontology learning by comparing LLM task performance on source documents versus transformed representations, and demonstrates it on legal merger agreement analysis across six language models and three ontology learning methods. The results reveal systematic semantic loss that varies significantly with reasoning complexity and model-method interactions, providing guidance for selecting optimal configurations in legal knowledge systems.", "body_md": "arXiv:2608.12326v1 Announce Type: new\nAbstract: Ontology learning transforms unstructured text into structured representations for automated reasoning. Yet structuring information risks losing it, and current evaluation methodologies cannot detect such loss, focusing on structural correctness while failing to measure whether meaning survives transformation. We propose an evaluation methodology that addresses this: comparing LLM task performance on source documents against performance on transformed representations, with the difference quantifying semantic loss. We demonstrate this approach on legal merger agreement analysis, a domain chosen for its complex language and precise semantic requirements, comparing direct LLM application against three ontology learning methods across six language models. The results reveal systematic semantic loss with significant variation based on reasoning complexity and model-method interactions. Our contributions are: (1) an evaluation framework for measuring semantic preservation in ontology learning, and (2) empirical evidence that semantic loss varies dramatically with model-method pairing, providing guidance for selecting optimal configurations in legal knowledge systems.", "url": "https://wpnews.pro/news/on-measuring-semantic-preservation-in-legal-ontology-learning", "canonical_source": "https://arxiv.org/abs/2608.12326", "published_at": "2026-08-14 04:00:00+00:00", "updated_at": "2026-08-14 04:12:18.347498+00:00", "lang": "en", "topics": ["artificial-intelligence", "natural-language-processing", "ai-research"], "entities": ["arXiv"], "alternates": {"html": "https://wpnews.pro/news/on-measuring-semantic-preservation-in-legal-ontology-learning", "markdown": "https://wpnews.pro/news/on-measuring-semantic-preservation-in-legal-ontology-learning.md", "text": "https://wpnews.pro/news/on-measuring-semantic-preservation-in-legal-ontology-learning.txt", "jsonld": "https://wpnews.pro/news/on-measuring-semantic-preservation-in-legal-ontology-learning.jsonld"}}