AIBullisharXiv – CS AI · Jun 237/10
🧠NOEM³A is a lightweight neuro-symbolic framework that enhances compact language models with intent ontologies to improve natural language understanding for mobile agents. By injecting structured symbolic knowledge into both input prompts and output decoding, the method achieves better performance on dialogue understanding tasks while maintaining privacy and low-latency requirements suitable for on-device deployment.
🧠 Llama
AIBullisharXiv – CS AI · Mar 167/10
🧠Researchers have developed a new methodology that leverages Large Language Models to automate the creation of Ontological Knowledge Bases, addressing traditional challenges of manual development. The approach demonstrates significant improvements in scalability, consistency, and efficiency through automated knowledge acquisition and continuous refinement cycles.
AIBullisharXiv – CS AI · Mar 67/10
🧠Researchers propose a new 'Memory-as-Ontology' paradigm for AI agents that treats memory as the foundation of digital existence rather than just a functional tool. The approach introduces Animesis, a Constitutional Memory Architecture designed for persistent digital citizens whose identities must survive across model transitions and extended lifecycles.
AINeutralarXiv – CS AI · Jun 256/10
🧠Researchers have developed a framework for evaluating fuzzy quantification queries over OWL ontologies and knowledge graphs, enabling retrieval of individuals matching Type I or Type II fuzzy quantified expressions. The system is agnostic to quantifier types and data sources, with Q2S2 released as an open implementation for future research.
AINeutralarXiv – CS AI · Jun 195/10
🧠TOTEN is a new tokenization framework for Brazilian Portuguese that uses formal ontologies to semantically preserve physical quantities, units, and technical notation instead of fragmenting them like standard statistical methods. The system significantly outperforms existing baselines in numerical reconstruction and dimensional equivalence, achieving 0.775-0.904 accuracy compared to 0.627-0.703 for competing approaches.
GeneralNeutralarXiv – CS AI · Jun 106/10
📰A comprehensive survey examines semantic modeling approaches for Building Energy Management (BEM), analyzing 60 semantic models and 20+ ontology-based use cases to address data interoperability challenges. The research identifies significant gaps in how current ontologies represent abstract operational concepts like performance indicators and control logic, highlighting the need for more integrated semantic frameworks to enable autonomous, context-aware building systems.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers propose a formal temporal modeling framework using the LRMoo ontology to represent how legal norms evolve over time, enabling precise point-in-time reconstruction of legal texts. The approach treats legal amendments as event-centric chains of versioned works, addressing a critical gap in automated legal processing that could improve AI reliability in legal applications.
GeneralNeutralarXiv – CS AI · Jun 16/10
📰Researchers propose MAECO-Lite, a lightweight ontology for dynamic malware analysis that improves upon existing standards like MAEC and STIX by clearly separating enduring artifacts from runtime events. The modular framework demonstrates significantly better performance in machine learning-based threat intelligence processing while maintaining semantic precision.
AINeutralarXiv – CS AI · May 296/10
🧠Researchers propose an ontology-driven framework called CCAI (Contextual Collaboration AI Ontology) to document and trace human-AI interactions, converting ephemeral prompt-response exchanges into structured, queryable collaboration records. The framework addresses transparency and accountability gaps in AI-assisted workflows by explicitly modeling tasks, agent roles, resources, and constraints within a machine-interpretable vocabulary.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers propose embedding the Robotic Service Ontology (RoSO) into the Structural Model of General Intelligence (SMGI) to enable dynamic governance of robotic services during runtime reconfigurations. The framework addresses how service semantics can remain valid and admissible when systems are rebound, recomposed, or redeployed, moving beyond static ontology conformance to formally governed runtime change.
AIBullisharXiv – CS AI · Apr 76/10
🧠Researchers developed a lightweight framework that uses ontological definitions to provide modular and explainable control over Large Language Model outputs in conversational systems. The method fine-tunes LLMs to generate content according to specific constraints like English proficiency level and content polarity, consistently outperforming pre-trained baselines across seven state-of-the-art models.
AIBullisharXiv – CS AI · Mar 276/10
🧠Researchers have developed UniAI-GraphRAG, an enhanced framework that improves upon existing GraphRAG systems for complex reasoning and multi-hop queries. The framework introduces three key innovations including ontology-guided extraction, multi-dimensional clustering, and dual-channel fusion, showing superior performance over mainstream solutions like LightRAG on benchmark tests.
AIBullisharXiv – CS AI · Mar 36/107
🧠Researchers have developed QIME, a new framework for creating interpretable medical text embeddings that uses ontology-grounded questions to represent biomedical text. Unlike black-box AI models, QIME provides clinically meaningful explanations while achieving performance close to traditional dense embeddings in medical text analysis tasks.
AINeutralarXiv – CS AI · Mar 94/10
🧠A new academic paper analyzes the ontological nature of Large Language Models like ChatGPT, concluding they are not autonomous agents but rather 'linguistic automatons' or 'libraries-that-talk' that lack true agency. The research argues that LLMs fail to meet key conditions for autonomous agency including individuality, normativity, and interactional asymmetry, while still enabling new forms of human-machine interaction.
🧠 ChatGPT
