# Unifying Ontology Construction and Semantic Alignment for Deterministic Enterprise Reasoning at Scale

arXiv: https://arxiv.org/abs/2604.09608 · HTML: https://mlsanity.com/paper/2604.09608/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 11 Mar 2026.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)

## Builds on (most cited first, 10 of 10)

- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [Group Sequence Policy Optimization](https://mlsanity.com/paper/2507.18071/) (2025)
- [AFlow: Automating Agentic Workflow Generation](https://mlsanity.com/paper/2410.10762/) (2024)
- [Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs](https://mlsanity.com/paper/2410.18451/) (2024)
- [Retrieval-Augmented Generation of Ontologies from Relational Databases](https://mlsanity.com/paper/2506.01232/) (2025)
- [Node Classification via Semantic-Structural Attention-Enhanced Graph Convolutional Networks](https://mlsanity.com/paper/2403.16033/) (2024)
- [Construct, Align, and Reason: Large Ontology Models for Enterprise Knowledge Management](https://mlsanity.com/paper/2602.00029/) (2026)
- [LLM-Driven Ontology Construction for Enterprise Knowledge Graphs](https://mlsanity.com/paper/2602.01276/) (2026)
- [Generative Ontology: When Structured Knowledge Learns to Create](https://mlsanity.com/paper/2602.05636/) (2026)
- [Development of Ontological Knowledge Bases by Leveraging Large Language Models](https://mlsanity.com/paper/2601.10436/) (2026)

## Built on by (most cited first, 1 of 1)

- [From Business Events to Auditable Decisions: Ontology-Governed Graph Simulation for Enterprise AI](https://mlsanity.com/paper/2604.08603/) (2026)

## Abstract (arXiv)

While enterprises amass vast quantities of data, much of it remains chaotic and effectively dormant, preventing decision-making based on comprehensive information. Existing neuro-symbolic approaches rely on disjoint pipelines and struggle with error propagation. We introduce the large ontology model (LOM), a unified framework that seamlessly integrates ontology construction, semantic alignment, and logical reasoning into a single end-to-end architecture. LOM employs a construct-align-reason (CAR) pipeline, leveraging its unified architecture across all three stages: it first autonomously constructs a domain-specific ontological universe from raw data, then aligns neural generation with this structural reality using a graph-aware encoder and reinforcement learning, and finally executes deterministic reasoning over the constructed topology, node attributes and relation types. We evaluate LOM on a comprehensive benchmark constructed from diverse real-world enterprise datasets. Experimental results demonstrate that LOM-4B achieves 88.8% accuracy in ontology completion and 94% in complex graph reasoning tasks, significantly outperforming state-of-the-art LLMs. These findings validate that autonomous logical construction is essential for achieving deterministic, enterprise-grade intelligence.