The Ontology system本体系统

The Ontology is the system at the heart of Palantir’s architecture. The Ontology is designed to represent the complex, interconnected decisions of an enterprise, not simply the data. This enables both humans and AI agents to collaborate, across operational workflows that must orchestrate with the physical world.本体是 Palantir 架构的核心系统。它的设计目标是表示企业内复杂且相互关联的决策，而不仅仅是数据。这使得人类和 AI 智能体能够在需要与物理世界协调的操作工作流中进行协作。

• An airline might model flights, aircraft, crew manifests, scheduling optimizers, and other fragmented enterprise assets into their ontology, to power day-of flight operations and longer-range planning.一家航空公司可以将航班、飞机、机组人员名单、排班优化器以及其他分散的企业资产建模到其本体中，用于支持日常航班运营和长期规划。

• A hospital system might instead model patients, nurse schedules, medical supplies, bed capacities, and other elements that often shift in real-time, and are essential to driving the patient lifecycle.一个医院系统则可以建模患者、护士排班、医疗物资、床位容量以及其他经常实时变化的要素，这些要素对于推动患者生命周期至关重要。

• In military contexts, an ontology can unify the readiness information across forward-deployed forces with the operational processes that underpin reconnaissance and target selection, providing a shared operational world for multinational teams.在军事场景中，本体可以将前沿部署部队的战备信息与支撑侦察和目标选择的作战流程统一起来，为多国团队提供一个共享的作战世界。

How the Ontology models decisions本体如何对决策建模

The Ontology models decisions through the four-fold integration of data, logic, action, and security.本体通过数据、逻辑、动作和安全四个方面的集成来对决策建模。

Data can flow from every conceivable source, such as fragmented ERP estates, homegrown systems of record, CRMs, industrial databases, geospatial repositories, real-time sensors, document stores, and essentially any other digital alcove. The Ontology unifies these disparate data sources into coherent objects, properties, and links; the semantic concepts which enable the full range of stakeholders to interact with and manipulate the information.数据可以来自各种可能的来源，例如分散的 ERP 系统、自建记录系统、CRM、工业数据库、地理空间存储库、实时传感器、文档存储库以及几乎任何其他数字角落。本体将这些不同的数据源统一为一致的对象、属性和链接；这些语义概念使所有利益相关者能够交互和操作信息。

The data objects, or "nouns", however, must be complemented by "verbs" in order to model decisions; semantics must be paired with kinetics. The Ontology is designed to model the full range of actions, from simple transactions to complex multi-step updates that must be written back to operational and edge systems in real time.然而，数据对象（“名词”）必须辅以“动词”才能对决策建模；语义必须与动力学相结合。本体旨在建模各种动作，从简单的事务到复杂的多步骤更新，这些更新必须实时写回运营系统和边缘系统。

The logic that powers each action can be modular and evolve over time, reflecting the diversity of calculation and reasoning that drives decision-making. The logic underlying a given action (or enhancing a particular object) could be a simple business rule, a conventional machine learning model, an LLM-driven function, or a complex multi-step orchestration that involves several compute engines.驱动每个动作的逻辑可以是模块化的，并随时间演变，反映了驱动决策的计算和推理的多样性。某个动作背后的逻辑（或增强特定对象的逻辑）可以是简单的业务规则、传统的机器学习模型、LLM 驱动的函数，或者涉及多个计算引擎的复杂多步骤编排。

To illustrate the vital role of security (and how it is woven into data, logic, and actions), we can use the example of a notional medical manufacturing company that is leveraging the Ontology.为了说明安全的关键作用（以及它如何融入数据、逻辑和动作），我们可以以一家假设的医疗制造公司为例，该公司正在利用本体。

Ontology example: Medical manufacturing本体示例：医疗制造

Imagine a medical manufacturer that must manage a complex web of vendor interactions, production lines, logistics activities, and customer lifecycles.想象一家医疗制造商，它必须管理由供应商交互、生产线、物流活动和客户生命周期组成的复杂网络。

Their ontology models the manufacturing plants, work orders, customer details, inbound packages, outbound shipments, and other key semantic concepts that integrated together hundreds of underlying data sources.他们的本体对制造工厂、工单、客户详细信息、入库包裹、出库货物以及其他关键的语义概念进行了建模，这些概念整合了数百个底层数据源。

For the supply chain analysts, production engineers, warehouse associates, and other team members interacting with the Ontology, different scopes of access are relevant.对于与本体的供应链分析师、生产工程师、仓库人员和其他团队成员，适用不同的访问范围。

• The production teams might require access to see global telemetry pertaining to machines and the lifecycle of finished goods;生产团队可能需要访问全局遥测数据，涉及机器和成品生命周期；
• Warehouse associates might have more granular restrictions based on a team member’s regional location;仓库人员可能根据团队成员的所在地拥有更细粒度的限制；
• Supply chain analysts may have even more granular permissions, which apply row/column-level restrictions to sensitive data elements based on particular user.供应链分析师可能拥有更细粒度的权限，根据特定用户对敏感数据元素应用行/列级限制。

As these different teams build AI-powered agents, they must have security scopes that either inherit from a human user, or from the permissions structure of a defined project. This becomes much more complex when factoring in the action and logic primitives that are connected into the Ontology, and are essential to conducting workflows.随着这些不同团队构建基于 AI 的智能体，他们必须拥有从人类用户或已定义项目的权限结构继承的安全范围。当考虑到连接到本体并对执行工作流至关重要的动作和逻辑原语时，这变得更加复杂。

The ability to trigger a purchase order might have granular permissions, while the ability to run a scenario to gauge the impact of a proposed reallocation might be more permissible; the underlying optimizers, or abilities to call LLMs, which manifest into functions which are interactively orchestrated via actions, might have altogether different security scopes. The Ontology’s security system has to reconcile all of these granular policies, at the time of interaction, across tens of thousands of humans and agents.触发采购订单的能力可能具有细粒度权限，而运行场景以评估提议的重新分配的影响的能力可能更宽松；底层的优化器，或调用 LLM 的能力（这些能力表现为通过动作交互编排的函数）可能具有完全不同的安全范围。本体的安全系统必须在交互时，跨越数万个人类和智能体，协调所有这些细粒度策略。

The Ontology Language, Ontology Engine, and Ontology Toolchain本体语言、本体引擎和本体工具链

The Ontology is not a "semantic layer"; the fourfold integration and operationalization of data, logic, action, and security cannot be accomplished with a thin semantic layer or a monolithic design.本体不是一个“语义层”；数据、逻辑、动作和安全的四重集成和可操作化无法通过薄薄的语义层或单一设计来完成。

Rather, the Ontology is a multimodal system consisting of dozens of underlying components, which can conceptually be grouped into a Language, an Engine, and Toolchain.相反，本体是一个由数十个底层组件组成的多模态系统，这些组件在概念上可以分组为语言、引擎和工具链。

• The Language models the semantic objects, links, and properties; along with the kinetic actions and automations; and the literal pieces of logic that define how those actions operate, and how they interact with other systems.语言对语义对象、链接和属性进行建模；以及动态的动作和自动化；还有定义这些动作如何运作以及与其它系统交互的具体逻辑片段。

• The Engine substantiates every component of the Language. It provides the modular read architecture that enables high-scale SQL queries, real-time subscription to state changes, and every materialization needed by mixed Human + AI teams. In equal measure, it provides a scalable write architecture which enables atomic and durable transactional updates, high-scale batch mutations, high-scale streams, and mechanisms like Change Data Capture for extremely low-latency mirroring with other operational systems.引擎实现语言的每个组件。它提供模块化的读取架构，支持高规模 SQL 查询、实时订阅状态变更，以及人类+AI 混合团队所需的所有物化。同时，它还提供可扩展的写入架构，支持原子且持久的事务性更新、高规模批量变更、高规模流，以及变更数据捕获等机制，用于与其它运营系统实现极低延迟的镜像。

• The Toolchain encompasses the entire expressivity of the Language and the power of the Engine, enabling developers to use the Ontology as a backend. Rich, AI-enabled applications for wildfire response, naval logistics, automotive assembly, and countless other use-cases all build upon the Ontology SDK (OSDK), and a rich collection of DevOps tooling designed for the scaled governance of production use cases.工具链涵盖了语言的全部表达能力和引擎的全部功能，使开发人员能够将本体用作后端。用于野火响应、海军物流、汽车装配以及无数其他用例的丰富 AI 应用都构建在本体 SDK（OSDK）之上，以及一组丰富的 DevOps 工具，用于生产用例的规模化治理。

A digital representation of your world你世界的数字表示

The Ontology serves as the dynamic, compounding core of the cybernetic enterprise.本体作为控制论企业动态且不断累积的核心。

Every data integration helps build a full-fidelity representation of the operational world, shared by humans and AI-enabled agents.每次数据集成都有助于构建一个由人类和 AI 智能体共享的、高保真的运营世界表示。

Every piece of logic, whether a simple business rule or a multi-step orchestration, can be connected to every action, within a decision graph that connects together traditionally fragmented processes.每一块逻辑，无论是简单的业务规则还是多步骤编排，都可以连接到每个动作，形成一个连接传统上分散的流程的决策图。

Every piece of feedback gathered within a workflow can be securely incorporated into continuous learning loops, and used to power the journey from augmentation to automation.工作流中收集的每条反馈都可以安全地纳入持续学习循环，并用于推动从增强到自动化的进程。

Battle-tested security and audit systems ensure that every activity can be precisely governed, across the entire fleet of human and machine workers. The Ontology reflects the ambition of Palantir’s customers, and its constant evolution is driven by their most important missions.经过实战检验的安全和审计系统确保每项活动都能在整个人类和机器工人群体中得到精确治理。本体反映了 Palantir 客户的雄心，其不断演进由他们最重要的使命驱动。