As Large Language Models (LLMs) and Agents accelerate their entry into enterprises, how to enable AI to understand real business and participate in business execution has become a key issue for the intelligent transformation of enterprises. By describing business objects, relationships, and rules, Ontology establishes a cognitive foundation for AI regarding enterprise business, attracting increasing attention from enterprises and technology vendors.
However, there are still obvious differences in the understanding of Ontology in the Chinese market: some focus on knowledge organization, some emphasize unified data semantics, and some vendors have begun to incorporate business logic and actions into it. Behind the same concept correspond different construction goals, product boundaries, and business values.
Against this backdrop, iFenxi released the "2026 iFenxi China Ontology Platform Market Insight Report," systematically sorting out the Ontology practice paths and capability evolution directions in the Chinese market. iFenxi believes that the value of the Ontology platform needs to be further extended from semantic expression to business computing, action execution, and continuous governance. A complete platform must not only help enterprises establish a unified business cognition but also support the continuous updating of the Ontology as the business runs and rules change, and promote the closed-loop of data, judgment, and action.
Scan the QR code at the end of the article to get the "2026 iFenxi China Ontology Platform Market Insight Report"
Core Insights
As Palantir attracts attention from the capital market and enterprise users, Ontology has become a hot concept in China's AI market. The practices of vendors in the Chinese market have not completely copied the Palantir path, but explored based on different technical routes and application scenarios. The Chinese market has not yet formed a unified definition of Ontology, and the product capability boundaries of Ontology platforms are also in the exploration stage.
The Chinese market has formed four types of practice paths around Ontology. The knowledge engineering path focuses on organizing documents and expert experience into structured knowledge; the semantic layer path focuses on unifying business terms and metric calibers, and establishing mappings between semantics and data; the business ontology path revolves around business objects such as customers, orders, and equipment, describing their attributes, relationships, and states; the executable ontology path further manages business rules, Functions, and Actions, enabling the Ontology to participate in business computing and process execution.
Palantir provides an important reference for understanding complete Ontology capabilities. Its current system comes from long-term data governance, decision-making applications, and project delivery practices, and has subsequently gradually expanded to business objects, Functions, Actions, and dynamic security. Chinese vendors can draw on its platform architecture, integrate the capability enhancements brought by large models and agent technologies, and form different product architectures and evolution paths.
The evaluation of the Ontology platform needs to focus on whether semantic expression, business computing, action execution, and continuous governance can form a closed loop. Objects, attributes, and relationships constitute semantic expression; Functions undertake business computing; Actions are responsible for action execution; and full-lifecycle governance supports the continuous iteration and stable operation of the Ontology. The four types of capabilities jointly form a closed loop from understanding business to driving action.
Dynamic Ontology capabilities include operational dynamics and evolutionary dynamics. The former refers to the linkage of object states, computing logic, and action results during the business process, completing the action execution closed loop; the latter refers to the ability of the Ontology to continuously iterate with data and business changes. AI automatic generation of Ontology can improve the efficiency of discovering objects, relationships, and rules, but there are still a large number of hallucinations at present, and manual review and verification are still required for application in production scenarios.
The Ontology platform needs to grow continuously in real scenarios. Vendors identify key objects through projects, verify computing logic and actions, and then precipitate stable experience into platform assets. The core task of FDE (Forward Deployed Engineer) is to deliver measurable business results and promote field experience into standard products and subsequent projects.
01. Multiple Ontology Practice Paths Formed in the Chinese Market, Capability Boundaries Not Yet Unified
After the significant improvement in model capabilities in 2026, enterprises' expectations for AI are shifting from knowledge Q&A to business execution and implementation. AI needs to not only understand users' questions but also know what customers, equipment, and orders exist in the enterprise, master the relationships between these objects, and complete decision-making and execution according to business rules.
Existing enterprise data and knowledge are usually scattered in different systems. Data platforms store business data, knowledge bases manage documents, and rules are distributed in business systems and personnel experience. Enterprises need a unified mechanism to associate business objects, data meanings, and business rules, and provide clear boundaries for AI to call enterprise resources.
Therefore, the Ontology platform has attracted attention. It can organize business objects and their relationships in the enterprise, further connect to real data, carry business rules and actions, and form a unified business expression of data, knowledge, and business processes.
Palantir's market performance has further driven the attention of Chinese enterprises and vendors to Ontology. The Chinese market has not formed a unified product form. Different vendors enter from the directions of knowledge engineering, semantic layer, business object modeling, and business actions, and gradually expand their capabilities in combination with their own customers and projects.
1.1 Four Types of Ontology Practice Paths Correspond to Different Construction Goals
The exploration of Ontology in the Chinese market does not start from the same demand. Some enterprises hope to transform documents and expert experience into machine-understandable knowledge; some hope to unify business terms and data calibers; and some hope to organize data around real business objects and further support business computing and process execution. Different construction goals lead to obvious differences in the formed Ontology forms and platform capabilities.
To more clearly present current market practices, iFenxi summarizes the relevant practices into four paths based on the construction objects, core goals, and usage methods of Ontology: the knowledge engineering path, the semantic layer path, the business ontology path, and the executable ontology path.
The knowledge engineering path mainly deals with documents, terms, and expert knowledge; the semantic layer path mainly connects business semantics with underlying data; the business ontology path revolves around business objects such as customers, orders, and equipment, describing their attributes, relationships, and states; the executable ontology path further manages business rules, Functions, and Actions, enabling the Ontology to participate in business computing and process execution.
Figure 1: Four Types of Ontology Practice Paths and Capability Boundaries in the Chinese Market
The main differences among the four paths lie in what content the Ontology needs to organize and what application scenarios it ultimately needs to support. The knowledge engineering path focuses on the extraction and organization of knowledge; the semantic layer path focuses on data meanings and metric calibers; the business ontology path focuses on real business objects and their contexts; and the executable ontology path focuses on rule execution and business actions.
1.2 Knowledge Engineering Path and Semantic Layer Path Start from Knowledge and Data Respectively
The knowledge engineering path mainly extracts concepts and their relationships from institutional documents, technical documents, and expert experience. The construction focus usually falls on ontology modeling, entity extraction, and knowledge fusion. The governed knowledge can enter the knowledge graph or become a knowledge source for enterprise Agents. The main value is to improve knowledge quality and cross-document understanding efficiency.
The semantic layer path mainly faces scenarios such as enterprise intelligent data querying. The platform unifies business terms and metric calibers, and then maps semantics to underlying data. After users ask questions through natural language, the system can generate queries based on the semantic model. Some vendors have already realized the transition from decision-making to action, combining data analysis with action mechanisms.
1.3 Business Ontology Path and Executable Ontology Path: Driving Ontology into Business Operations
The business ontology path starts from the real business objects of the enterprise. The platform takes objects such as customers, orders, and equipment as the core, uniformly describes object attributes, relationships, and states, and then organizes data from different systems into continuous business contexts. This lays the foundation for Agents to understand business scenarios and reason and recall.
The executable ontology path further implements the management of business rules and Functions on top of the business ontology. The platform runs Functions according to object states, forms business judgments, and then calls Actions to trigger processes or external systems. After the action is completed, the execution results can return to the Ontology and update the object states.
02. China's Ontology Capabilities Move from Model Construction to Dynamic Operation
The four Ontology practice paths in China have different construction goals, but face similar problems in the implementation process: how to quickly construct the Ontology, how to keep it synchronized with real data, and how to adapt to business changes.
Early Ontology construction mainly relied on domain experts for manual modeling. As data scales expand and business changes accelerate, relying solely on manual work can no longer afford the continuous construction and maintenance costs. The current market begins to construct the Ontology through the joint participation of data structures, business documents, application scenarios, and AI, and gradually forms dynamic operation and continuous evolution capabilities.
2.1 Ontology Construction is Moving from Expert-Led to Multi-Source Collaboration
Traditional Ontology construction mainly relies on domain experts. The team first determines objects and relationships, and then defines attributes and constraints. This method has a long construction cycle and severely depends on expert resources and organizational collaboration.
More and more vendors are beginning to use data structures and business documents to assist in Ontology modeling. Ontology modeling tools can read database metadata, identify potential objects, and provide field mapping suggestions; for unstructured data such as documents, Ontology modeling tools can first generate model drafts, and then domain experts confirm the boundaries and meanings.
Another way is to construct the Ontology starting from scenarios. The team first determines the application scenarios and business pain points, and then reversely searches for related objects, computing logic, and Actions. This approach can narrow the initial construction scope and verify the value of the Ontology for real business scenarios earlier.
Ontology construction in production environments often requires a combination of multiple methods. Data structures provide object candidates and field mappings, business materials provide rule basis, and domain experts are responsible for explaining boundaries.
Multi-source collaboration improves the efficiency of initial Ontology construction, but the completion of the model does not mean the end of construction. When the Ontology connects to real-time data and enters business processes, object states need to be continuously updated, and models and rules will be continuously adjusted with business changes, thus generating dynamic Ontology capabilities.
2.2 Dynamic Ontology Capabilities Focus on Both Business Operation and Continuous Evolution
According to iFenxi's research, there are currently two main understandings of "dynamic Ontology" in the market. The first emphasizes that the Ontology can participate in business operations, and the second emphasizes that the Ontology model can be continuously adjusted with business changes. The two understandings correspond to operational dynamics and evolutionary dynamics respectively, each explaining a part of the dynamic Ontology. Only by combining the two capabilities can a true dynamic Ontology be formed.
Operational dynamics emphasize that the Ontology needs to have a complete composition. In addition to objects, attributes, and relationships, the Ontology also needs to manage business rules, Functions, and Actions. Objects, attributes, and relationships help the system understand the enterprise's business world; Functions are responsible for completing business computing and judgments; Actions are responsible for triggering processes or calling external systems.
For example, in a customer risk management scenario, the Ontology first needs to describe the relationships between customers, accounts, and transactions. When customer risks change, Functions can recalculate risk levels based on business rules, and Actions then trigger manual reviews, restrict transactions, or send warnings. Through this process, the Ontology can extend from describing business objects to supporting business judgments and action execution.
Evolutionary dynamics emphasize that the Ontology model can be continuously adjusted with business changes. Enterprises will continuously add new business objects, modify data calibers, and adjust business rules. For example, banks change customer risk assessment standards, manufacturing enterprises add new equipment types, and supply chain enterprises adjust inventory warning rules. The objects, relationships, mappings, and business logic in the Ontology also need to change synchronously.
Adjustments to the Ontology model may affect existing data, Functions, Actions, and business applications. In project implementation, it is necessary to identify which contents will be affected, and support modification, testing, review, and release. Operational dynamics and evolutionary dynamics focus on two different levels of the Ontology.
Operational dynamics solve whether the Ontology can participate in business computing and actions under the current version, while evolutionary dynamics solve whether the Ontology can adapt to business changes in the long term through version iteration.
The existence of any single capability alone can only cover a part of the dynamic Ontology. A true dynamic Ontology needs to have both complete operational capabilities and continuous evolutionary capabilities.
Figure 2: Dynamic Ontology is Composed of Operational Dynamics and Evolutionary Dynamics
2.3 AI-Automated Ontology Generation Enters the Production Quality Verification Stage
As the objects, relationships, and business rules contained in the Ontology continue to increase, relying on manual work to complete initial construction and continuous updates will bring high costs. AI begins to be used to assist in identifying objects, generating relationships and rule suggestions, and helping teams discover the impact of business changes on the Ontology model.
AI can help enterprises complete a large amount of basic work in Ontology construction. The Ontology modeling tools of some vendors have begun to use AI to extract business rules. For example, AI can organize risk judgment conditions from credit systems, or extract fault handling rules from equipment operation and maintenance manuals, and form Function drafts. However, there may be inconsistent calibers or unclear expressions in business documents, and AI may also miss important conditions.
Therefore, the current generation results still need to be reviewed by domain experts and can only be used in actual business after passing the tests, especially in high-risk scenarios such as finance and industry.
When evaluating the capability of AI-automated Ontology generation, we cannot just look at the modeling speed. We also need to see what content AI can generate, whether it can explain where these contents come from in terms of data and documents, and whether it can discover duplicate objects, relationship conflicts, and rule errors. The generation results must also be convenient for subsequent modification and maintenance. Only when efficiency, accuracy, and maintenance costs are verified simultaneously does AI-generated Ontology have production application value.
The Chinese market has formed multiple construction methods such as expert modeling, data assistance, and AI collaboration. Some products have also begun to support Ontology operation and continuous evolution. AI can reduce the cost of initial Ontology construction and continuous updates, but the value of Ontology modeling tools also depends on whether the model can connect to real data, carry business computing, drive business actions, and be updated in the long term with business changes. Palantir's long-term practice provides a relatively complete reference for understanding this direction.
03. Palantir Ontology Forms a Complete Ontology Capability Architecture Through Long-Term Practice
To understand Palantir Ontology, we need to look at not only what capabilities it currently has but also the formation path of these capabilities. Currently, Ontology has integrated semantic elements, business computing, Actions, and dynamic security. This system comes from long-term data governance, decision-making applications, and project delivery. Palantir can serve as a capability reference, but Chinese vendors do not need to completely copy Palantir's construction path.
Figure 3: Core Architecture of Palantir Ontology
3.1 Palantir Ontology Gradually Forms from Data Analysis and Business Applications
In the early stage, Palantir first solved the problems of multi-source data connection, analysis, and collaboration. As projects deepened, simple data tables and analysis results were difficult to meet customers' business demands. The platform gradually organized data with business objects such as personnel, equipment, and events, and established relationships between objects.
When these objects are further continuously called by applications and processes, Ontology gradually adds business rules, computing logic, and action boundaries. AIP further connects large models with Ontology, enabling models to call data and Actions within the enterprise context and permission scope. It can be seen that Palantir's Ontology capabilities are gradually expanded and formed in real business problems.
3.2 Semantic Elements and Kinetic Elements Jointly Constitute Palantir Ontology
Palantir officially divides Ontology into semantic elements and kinetic elements. Semantic elements include objects, attributes, and relationships, used to describe the enterprise business world; kinetic elements include Functions, Actions, and dynamic security, used to calculate business states and drive business changes.
Functions are the business computing and processing logic running around Ontology objects. They can read objects, execute judgments, and return results. Actions stipulate how objects are created or modified, and can also trigger external systems. Only when the two are combined can the Ontology truly enter the executable business process.
Dynamic security calculates access and execution permissions when users and Agents initiate interactions. The range of objects seen by different users may be different, and the executable Actions may also be different. After semantic elements, kinetic elements, and dynamic security are combined, analysis results can enter business processes in a controlled environment.
3.3 Palantir Ontology Connects Data, Models, and Applications
In the Palantir product system, Ontology is built on data assets and provides a unified business interface for analysis tools and operational applications. Developers can call objects through the Ontology API, and business personnel can also use low-code tools to build workflows.
AIP further supports large models and Agents to call objects, Functions, and Actions under permission control. Therefore, Ontology becomes an important foundation for enterprise AI context, business logic, and action execution.
04. Palantir Ensures Continuous Ontology Operation Through Full-Lifecycle Management
After Ontology enters the core processes of the enterprise, it will be used by multiple types of applications. Changes in data structures, adjustments in business logic, or updates in permissions may all affect downstream applications. Therefore, Palantir integrates Ontology management throughout the entire process of construction, connection, operation, and governance.
4.1 Ontology Capabilities Run Through Construction, Connection, and Operation
Palantir does not have an "Ontology platform" as a separate product line. Ontology capabilities are distributed in tools such as Ontology Manager, Pipeline Builder, and AIP, connected by a unified object system. The lifecycle of Ontology management can be summarized in four links. The construction link defines objects and actions; the connection link maps data or models to objects; the operation link enters business processes through Functions and Actions; the governance link is responsible for permissions, versions, and operation monitoring.
4.2 Change Governance Ensures Stability in Production Environments
After the Ontology enters the production environment, every change may affect existing systems, so it needs to be tested in an isolated environment first. The team identifies potential conflicts through proposals and reviews, and then decides whether to merge them. What is more worth learning from Palantir is the full-lifecycle management method. Enterprises need to consider data connection and operation mechanisms during modeling, as well as version and permission management after the model goes online.
4.3 Operation and Maintenance Governance Focuses on Permissions, Usage, and Execution Results
After the Ontology goes online, the platform needs to continuously monitor resource usage. On the one hand, it identifies the access and dependency relationships of objects; on the other hand, it tracks the execution performance of Functions and Actions. The Action Log saves action submissions and data modification records, providing a basis for auditing and review. Permission governance needs to cover model resources and object instances. Project permissions control the viewing and editing of resources, and object security policies further constrain instance access. Dynamic security also calculates permissions based on the interaction context, keeping the calls of Agents in the enterprise environment controllable.
Palantir's practice shows that Ontology capabilities need to cover the entire process of construction, operation, and governance. Most Chinese vendors will develop independent Ontology platforms to cover the full-lifecycle management of the Ontology.
05. China's Ontology Platform Evaluation Should Focus on Capability Closed-Loop and Actual Effects
Palantir provides a relatively complete system reference. The Chinese market still needs to form an independent definition and evaluation system for vendor Ontology platforms. This system needs to not only identify what capabilities the platform has but also verify whether the relevant capabilities can operate collaboratively in a real environment. The product architecture does not necessarily have to copy Palantir's technical framework.
5.1 Definition of Ontology Platform
iFenxi believes that the Ontology platform is a software platform used to construct, operate, and govern enterprise business Ontology. It maps scattered data and knowledge into unified business objects and their relationships, manages rules, Functions, Actions, and permissions within the same business expression system. Enterprise users can carry out Ontology querying, computing, decision-making, and process execution based on this platform.
The Ontology platform covers links such as Ontology design, data connection, release and operation, monitoring and maintenance, and version iteration. The platform needs to not only let the Ontology continuously feed back real business states but also let business logic and actions run continuously under permission constraints, thereby supporting the long-term evolution of the Ontology.
Specifically, the Ontology platform needs three core characteristics.
First, it can construct a complete Ontology. The platform needs to manage objects, attributes, and relationships, and should also cover business rules, Functions, Actions, and corresponding permissions, so that semantic expression can further support business operation.
Second, it can connect real data and business actions. The platform needs to continuously map enterprise data into Ontology objects, and connect business processes or external systems through Functions and Actions, so that computing results can enter actual operations.
Third, it can manage the full lifecycle of the Ontology. The platform needs to provide release, version, and lineage management, and also cover permission control, operation monitoring, and change governance, ensuring the continuous update and stable operation of the Ontology.
5.2 Six Key Capabilities of the Ontology Platform Revolve Around the Ontology
From the complete process of construction, evolution to operation governance, the Ontology platform can be summarized into six key capabilities. Through these six capabilities, the completeness of the Ontology platform is evaluated, and at the same time, combined with actual implementation scenarios and real cases to judge whether different functions achieve equivalent results.
In this evaluation system, the dynamic Ontology belongs to comprehensive judgment. The capability of logic operation and action closed loop reflects operational dynamics, and the capability of continuous Ontology evolution reflects evolutionary dynamics. Only when both capabilities are possessed can a complete dynamic Ontology capability be formed.
Figure 4: Framework of Six Key Capabilities of the Ontology Platform
First, complete Ontology construction capability. The platform needs to uniformly manage objects, attributes, and relationships, and incorporate business rules, Functions, and Actions into the Ontology. This capability focuses on what Ontology resources the platform can manage and whether clear associations can be established between different resources.
Second, data connection and mapping capability. The platform needs to connect multi-source data in the enterprise and establish stable mappings between data resources and Ontology objects. When data in the source system changes, the object states in the Ontology can be updated synchronously.
Third, logic operation and action closed-loop capability. The platform needs to run rules and Functions, complete judgments based on the current state of business objects, and trigger processes or call external systems through Actions. The action results also need to return to the Ontology to update the states of related objects. This capability reflects the operational dynamics of the Ontology.
Fourth, continuous Ontology evolution capability. When business processes, data structures, or management rules change, the platform needs to support the adjustment of object models and business rules, and provide impact analysis, testing, and version release mechanisms. This capability reflects the evolutionary dynamics of the Ontology.
Fifth, AI-assisted construction and maintenance capability. The platform can use AI to identify objects, relationships, and rules from data structures and business materials, and assist in generating Ontology drafts. During the Ontology operation, AI can also identify changes and propose adjustment suggestions. Relevant results need to retain the generation basis and be subject to manual review.
Sixth, full-lifecycle governance capability. The platform needs to continuously govern Ontology resources, covering permission control, lineage tracking, and change approval, while supporting operation monitoring and auditing. Governance capabilities run through the entire process of Ontology construction, release, and operation, ensuring the stable use of the Ontology in the production environment.
5.3 Platform Evaluation Needs to Combine Product Demonstrations and Case Verifications
Product demonstrations need to revolve around a real business object. Vendors should demonstrate how objects connect to data, how rules and Functions run, how Actions are controlled by permissions, and how execution results return to the business system. Only when these functions operate collaboratively in the same process can it prove that the platform has a complete closed loop.
When evaluating the dynamic Ontology, operation and evolution need to be verified separately. The operation side focuses on whether the object state is updated in time after data changes and whether Functions are triggered according to conditions; the evolution side focuses on whether model changes can be isolated for testing, whether dependency conflicts can be discovered, and whether stable versions can be restored.
The focus of case verification is to judge whether the platform capabilities have entered the production process. Cases focus on what business pain points the Ontology platform has solved after going online and what incremental business value it has brought.
Thus, the Ontology platform evaluation forms two interrelated dimensions: one is platform capability, observing complete construction, dynamic evolution, and operation governance; the other is implementation verification, observing which processes it enters, what results it produces, and whether project experience can be reused. This report focuses on establishing the platform capability framework. Subsequently, iFenxi's evaluation of Chinese Ontology platform vendors will be carried out combining the two dimensions.
06. China's Ontology Platform Capabilities Need to Grow Continuously in Scenarios
6.1 High-Value Scenarios Determine the Starting Point of Construction
The maturity of the Ontology platform ultimately needs to be verified by scenarios. The Ontology platform is more suitable for scenarios with complex object relationships and continuously changing business rules. According to iFenxi's research, the Ontology platform is particularly suitable for scenarios involving cross-system collaboration and long-chain complex decision-making. If the demand is only knowledge retrieval or single-metric querying, enterprise users should first judge whether the AI knowledge base can meet it.
Enterprises need to first clarify business problems and responsible departments, and then set verifiable goals. Goals can be reflected in shortened decision-making cycles, reduced abnormal losses, or improved process efficiency. Clear delivery boundaries help control the initial scope and also facilitate judging whether the platform creates incremental value.
6.2 Scenario Implementation Forms a Platform Growth Cycle
The construction of the Ontology platform can start from key business decisions. The team identifies relevant objects and data, defines business logic and Actions, then connects to applications and enters real processes. The project needs to set result indicators synchronously to verify the impact of the Ontology on the business.
Figure 5: Scenario Implementation and Ontology Platform Growth Flywheel
Actual operation will expose problems such as missing objects, logic conflicts, and permission boundaries. The team adjusts the Ontology based on feedback and then enters the next round of verification. After verified object templates, connectors, and action components enter the platform, subsequent projects can be expanded on the basis of reuse.
To judge whether the platform is growing in the scenario, three results can be observed: first, whether the project enters the core process and improves business indicators; second, whether field experience enters standard products; third, whether subsequent projects shorten the delivery cycle. Only by forming this cycle can the number of projects help improve platform capabilities.
6.3 FDE Connects Business Sites and Platform Products
FDE (Forward Deployed Engineer) is responsible for discovering business problems and delivering results in the construction of the Ontology platform. The team needs to understand customers' decision-making and processes, as well as master data and engineering capabilities. Working on-site, FDE needs to complete object abstraction, organize business logic, and verify Actions and final results together with customers.
The Chinese software market has long had an on-site implementation model. The core of FDE lies in creating incremental business value, achieving delivery goals, and precipitating capabilities. System deployment and go-live are only basic conditions; attention must also be paid to business indicator improvements and whether project experience reversely promotes platform product upgrades.
The FDE team should record object templates, rule bases, and action interfaces in the scenario, and precipitate testing methods and review results. The product team then transforms these experiences into standard components for subsequent projects to call and expand. In this way, on-site investment can gradually be transformed into sustainable platform assets.
6.4 Project Experience Needs to be Transformed into Platform Growth
A large number of projects does not equal platform maturity. After completing multiple projects, some vendors still need to rely on manpower for repeated development. Platform growth needs to be reflected in shortened implementation cycles, increased standard capabilities, and stable delivery quality, and also needs to form industry methods that can be reused across customers.
Reusable assets include object templates, rule libraries, and connection components, as well as Actions and testing plans. After assets enter the platform, they need to undergo version management and quality checks. Subsequent projects expand on the basis of reuse, so that vendors can form economies of scale, and customers can also obtain more stable implementation results.
07. Conclusion
The Chinese Ontology platform market is moving from conceptual discussion to the stage of product capability and scenario value verification. As enterprise AI applications gradually extend from knowledge Q&A to business decision-making and action, the market focus will shift to complete Ontology construction, dynamic evolution, action closed loop, and full-lifecycle governance.
Starting from the multiple practice paths in the Chinese market, this article summarizes the common evolution of Ontology construction and dynamic operation, and proposes the definition of the Ontology platform and six capabilities with Palantir as a reference. Palantir demonstrates the capability boundaries of a mature system. Chinese vendors still need to combine the business scenarios, existing systems, and delivery conditions of local customers to form an evolution path suitable for themselves.
The long-term value of the Ontology platform depends on whether the platform can form a stable business expression, drive controlled actions, and continuously evolve with business changes. Vendors need to start from high-value scenarios, connect business sites and platform products through FDE, and then precipitate verified and effective project experience into reusable assets. Subsequently, iFenxi will continue to carry out vendor research and case verification around the Ontology platform and applications, and focus on forming two types of research results.
First, release the panoramic and practical research of Ontology platform and application vendors, presenting market participants, key industry scenarios, and typical cases, and further summarizing enterprise construction paths and key success factors.
Second, release the Ontology platform market research and vendor evaluation report, establish a two-dimensional evaluation system covering platform capabilities and scenario implementation, conduct systematic research on representative vendors, and verify capabilities through product demonstrations and production cases.
Scan the QR code at the end of the article to get the "2026 iFenxi China Ontology Platform Market Insight Report"