Daily Digest — Oct 2

LLM-Assisted Discovery of Typed Semantic Links for Ontology Network Construction

• An automated framework for ontology network construction combines domain-adapted DistilBERT embeddings, clustering-based pre-filtering, and GPT-4o-driven relationship generation to reduce 800,000 raw concept pairs to 95,000 high-quality candidates across 33 ontologies in ReproduceMeON.

Ontology-Grounded, Reasoner-Verified Benchmarks for Evaluating LLM Reasoning in Scientific AI

• A novel automated pipeline generates ontology-grounded multiple-choice question benchmarks from OWL 2 ontologies to evaluate logical reasoning in scientific AI applications. • The method produces formally verified distractors by perturbing class definition axioms and using an OWL reasoner to check entailment across datasets including 112 Pizza, 2491 PMDco, and 15216 DOID questions.

From Literature to Hypotheses: An AI Co-Scientist System for Biomarker-Guided Drug Combination Hypothesis Generation

• CoDHy introduces an interactive AI co-scientist system that generates biomarker-guided drug combination hypotheses for oncology applications. • The system builds task-specific knowledge graphs from curated databases and biomedical literature, integrating graph embeddings with agent-based reasoning to validate and rank drug combinations.

Build2SPARQL: A Large-Scale Text-to-SPARQL Benchmark Dataset for Building Knowledge Graph Querying

• Build2SPARQL provides 6,136 executable SPARQL queries and 30,680 natural-language questions derived from 201 building knowledge graphs using ontologies like Brick and ASHRAE 223P.

Protecting De-identified Documents from Search-based Linkage Attacks

• A novel method protects de-identified documents from search-based linkage attacks by identifying and rewriting N-grams present in fewer than k documents. • The approach employs an inverted index for efficient N-gram analysis followed by an LLM-based rewriter to reformulate sensitive spans.

ESIA: An Energy-Based Spatiotemporal Interaction-Aware Framework for Pedestrian Intention Prediction

• The ESIA (Energy-based Spatiotemporal Interaction-Aware framework) proposes a novel Conditional Random Field (CRF)-based paradigm for pedestrian intention prediction, treating pedestrians and the environment as spatiotemporal nodes in a unified graph.

Physics-Grounded Multi-Agent Architecture for Traceable, Risk-Aware Human-AI Decision Support in Manufacturing

• The Multi-Agent Knowledge Analysis (MAKA) architecture separates intent routing, quantitative analysis, knowledge retrieval, and verification to support risk-aware human-AI decision-making in manufacturing.

TusoAI: Agentic Optimization for Scientific Methods

• TusoAI, an agentic AI system, autonomously develops and optimizes computational methods for scientific tasks by integrating domain knowledge into a knowledge tree and performing iterative, domain-specific optimization.