Yonyou Unveils the Large Ontology Model (LOM): Forging a Deep-Thinking Digital Core for the Enterprise

BEIJING, March 1, 2026 /PRNewswire/ — As enterprise digital transformation advances with increasing depth and precision, the ability to efficiently manage and harness massive datasets has become the core competitive barrier. Facing the significant challenges of integrating multi-source heterogeneous data and the intense demand for high-fidelity decision-making in complex business scenarios, Yonyou officially released the Large Ontology Model (LOM) on February 24th. This release provides enterprises with a true “digital brain” capable of deeply understanding their operations and executing complex logical reasoning.

I. Technological Breakthrough: LOM as the New Foundation for Enterprise Intelligence

Grounded in the Yonyou BIP Enterprise AI Ontology Agent, the LOM represents a fundamental paradigm shift from conventional two-dimensional tabular models to knowledge graph-based architectures. By using nodes and edges to capture entities and their relationships, LOM transforms siloed enterprise data into computable, reason-capable “live connections.” It upgrades enterprise knowledge from static storage into dynamic, executable intelligent assets.

Leveraging a unified construct-align-reason framework, LOM seamlessly connects bottom-layer business systems and data with top-layer ontology applications. It enables the full-lifecycle management of enterprise knowledge, from raw extraction to high-value output. During the construction phase, LOM automates multi-source ontology construction, bridging the barriers between structured databases and unstructured text, explicit and implicit knowledge, and dynamic real-time versus static historical data. Driven by a robust knowledge construction engine, it performs entity-relation extraction, link prediction, knowledge distillation, and reasoning completion, culminating in a comprehensive enterprise-scale data architecture. Simultaneously, using the BIP standard ontology as the core anchor, it builds the skeletal structure of the model’s nodes. Then, through highly efficient dynamic text-ontology alignment, it ensures continuous data streams remain semantically consistent and deeply fused with the ontology structure.

When it comes to complex logical reasoning, the LOM is exceptionally capable. It executes reliable, multi-hop reasoning over heterogeneous enterprise data. In rigorous benchmark testing covering 19 diverse graph reasoning tasks, our 4B-parameter LOM achieved state-of-the-art performance, taking the top rank with an overall accuracy of 89.47%, while hitting near-perfect accuracy (100%) on several core tasks. This unequivocally validates the cutting-edge effectiveness of our architecture.

II. Real-World Impact: LOM Supercharges Agile and Lean Enterprise Management Across All Dimensions

The ultimate test of any technology is its real-world application. Armed with formidable reasoning capabilities, LOM is deeply optimized for core enterprise workflows—procurement, production, sales, and finance. It translates raw computational power into tangible business momentum, delivering full-stack empowerment from intelligent decision-making to complex system analysis.

Here is how the Large Ontology Model empowers intelligent decision-making and complex system analysis across these four critical domains:

Procurement: Engineering an Antifragile Supply Chain

When a core supplier faces disruption, the LOM doesn’t just see a localized failure; it instantly pinpoints that node within the global graph, executing a deep traversal across Tier-2 and Tier-3 dependencies to broadcast predictive early warnings. Before a purchase order is even issued, the system autonomously validates prerequisites—credentials, QA logs, budget constraints—eliminating compliance risks caused by missing information. For mission-critical materials, the LOM runs centrality and topology analysis to identify strategic bottleneck suppliers—those “single points of failure”—allowing enterprises to engineer redundancy and backup plans ahead of time.

Production: Full-Stack Traceability and Dynamic Optimization

When a product defect occurs, the LOM performs predecessor node searches along the Bill of Materials (BOM), reverse-tracing from the finished product back to the exact raw material batch to precisely locate the point of failure. In the smart factory, it acts as a routing engine, computing the shortest path for automated material handling equipment, significantly reducing dead time between operations. By monitoring task backlogs across workstations in real-time, it identifies throughput bottlenecks, enabling managers to dynamically allocate resources and smooth out the production flow.

Sales & Marketing: High-Precision Customer Insight and Resource Allocation

Leveraging historical interaction logs and social relation graphs, the LOM runs PageRank-style centrality algorithms to identify high-influence customer nodes, focusing your finite sales bandwidth where it generates the highest ROI. In owned channel ecosystems operations, it uses common neighbor analysis starting from existing users to rapidly map and capture directly connected prospect clusters, fundamentally driving down customer acquisition costs. For high-value accounts, it constructs a full-journey behavioral funnel, uncovering the hidden relational patterns behind churn to power highly targeted retention strategies.

Finance & Risk Management: Penetrative Oversight and Autonomous Compliance

The moment funds are deployed, the LOM identifies direct transactional counterparties of the receiving account. If the connected component contains high-risk entities, it triggers an autonomous hard stop. For labyrinthine corporate equity structures, the LOM penetrates multi-hop ownership layers to expose the ultimate beneficial owner, delivering a crystal-clear risk topology for M&A and investment decisions. In three-way matching, it starts at the payment request and traverses upward to validate all predecessor documents —POs, receipts, invoices—ensuring complete transparency in financial settlements through automated alignment.

From bare-metal architecture to real-world deployment, the LOM is rigorously grounded in actual enterprise needs. Its highly efficient, lightweight 4B-parameter model design cracks the code on complex enterprise-level reasoning, massively lowering the barrier and cost of deploying enterprise AI. More than that, it builds the definitive bridge between structured databases and unstructured textual knowledge, creating a continuously evolving intelligence framework where enterprise data assets self-generate and self-optimize.

Looking ahead, Yonyou’s LOM will relentlessly push the technical frontier. We will upgrade our reinforcement learning strategies, build open evaluation benchmarks, and tackle complex challenges. We are scaling LOM’s inference capabilities in complex enterprise environments so that every company is equipped with a deep-thinking “brain.”

https://chinaxiv.org/abs/202601.00187 

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SOURCE Yonyou