思想领袖
AI Agents的便携式商业本体学
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“肿瘤学”一词现在已变了。 为什么？Agents，以及给它们提供动力的LLMs，需要相关的商业环境。
在你买到本体学之前，请供应商问：“当本体学把我们的业务弄错了时，我们的人怎么纠正它呢？答案会告诉你很多你所买的生意模式它还指出了更大的利害关系：你有能力随着业务的改变而改进这个模式，并且如果改变平台，保持投资的实用性。
它有助于区分本体学和知识图，尽管两者经常重叠。
本体论：什么是存在的事物，什么是关系的意思，什么规则适用。 设备，组件，团队，角色，流程。 例如，肿瘤学可以定义“设备”类型和“组件”与“组件”的关系。 然后，知识图可以断言，“P-101泵含有S-7封条”：两个具体实体及其关系。
想象一下，你告诉agent, “ 计算同一商店的销售增长，但告诉我一个侧面，我们在那里展示所有商店，另一个则排斥不到2年的商店；然后把这两边分成国内、国际和综合。 ”
这个要求取决于细节 agent不能猜测：
- 当商店开业时，有什么商店？
- 他们在哪里？
- “同一商店销售”的内部定义
一项政策可能将商店排除在外，直到它们开业一年；要求的两年制的新增组是一个进一步的过滤器。 即便如此，你是否只要一个商店符合条件，就比较日历月，或者仅仅在前一个财政年度满期后才列入日历月？
肿瘤学需要将这些商店与管理计算的政策联系起来。 找到相关事实有帮助；适用正确的规则是使答案有用的原因。
agent可以在回答一个问题之前使用图中的关系来检索相关的上下文。 难怪它们有用当知识域很容易变得非常复杂时
但是提取也可以编码错误。 团队可能为同一个实体使用不同名称，产生重复。 消息来源可能不同意或过时。 即使是干净的来源也可能被LLM误读。 使这些错误看起来具有权威性并不使这些错误变得不那么危险。
当企业模式变成一个对其采取行动的系统时，这些风险就会增加。 错误的关系会触发错误的行动，而不仅仅是错误的答案。
肿瘤产品的风险
首先，警惕不灵活的模式。 以制造“包括电池在内的物品”为名，一个供应商可以让自己的实体成为头等公民，同时限制你所能做的事情。 如果自定义类型失去搜索，验证，或动作能力，那么定制是不够的。
第二，寻找从发现到治理的道路。 一个工具应该让您从 LLM 所看到的图中拖出一个图，然后用明确的限制来验证它。 你应该能够升级例外的人并保存探索性的知识不把它作为核准的商业数据。
最大的风险是将业务流程与无法离开的平台挂钩。 你的本体学代表你的事业：它的知识，过程和关系。 即使你改变了工具，对课程设置和商业逻辑的投资也应该继续有用。 为什么卖家要锁上钥匙？
图形嵌入帮助
随着知识图的发展非常大，可以值得选择一个支持图表嵌入的工具或销售商。 这些是语义嵌入法的亲近表亲，使许多RAG的应用产生动力。 有两个有益的好处需要考虑。
首先，通过一个紧密相连的图表，几个跳动可以产生过多的实体进行处理。 通过学习实体和关系的数字表述，嵌入提供了寻找图中相关部分的另一种途径。 对于正确的任务，这可以比走图更有效率。
第二，嵌入模型可以对候选人缺失的关系进行排序。 同样的学习可以揭示一个实体类型典型的属性和关系上的规律，帮助构建一个更加完整的图表。
逐步改进的理由
拥有自己的命运，避免锁定，模拟你的实际业务，而不是一个卖主的漫画，没有一支前方部署的工程师队伍，你怎么开始呢？我认为现代AI会在这里发光，如果工具支持从发现到核查的路径。
首先使用自动发现和提取。 从商业文档，政策，电子邮件，或Slack消息中可以构建一个域的初始视图。 关键是，在核实之前，你不能相信它如此准确。
核查是第二部分。 您的工具必须给您一个平滑的路径，从被提取的候选人到声称以源头为基础，以及到一个负责任的人或权威系统验证的知识。 保留每项索赔的来源。
新鲜度是正在进行的工作。 处理麻烦机票的程序，或“新泽西有多少商店”的答案，今天可能与三年前不同。 但更新今天的知识绝不能抹去昨天的现实。 当一个事实被记录下来时，以及何时适用时，可以有所不同；一个有用的本体学需要既支持当前的答案，又支持准确的历史观点。 仍然有人必须保持这种知识。
范围可能很大。 从一个较小的元件开始，绘制一个有用的域，并构建一个全面的图片同时创造价值沿途。
客户支持是一个有用的切片：入境呼叫，电子邮件，一个售票系统，自动解析，一个可以响应客户的支援团队，以及一个将真实bug升级到工程的路径。 它把知识与行动联系起来，并在一个可管理的领域暴露同样的风险和回报。
ServiceNow的共同事务数据模型说明了这一制约因素。 其CSDM 5号白皮书警告，“如果没有在规定的表格中提供这一数据，你可能得不到ServiceNow AI平台的全部价值。” 警告本身就造成了设计和业务上的制约：倾向于供应商的模式或接受风险。 如果这意味着你把圆圈塞进他们的方洞里呢？
现在假设一个肿瘤学平台也提供摄入，转化，行动，以及商业规则。 您可以导出实体、 属性和关系。 但您也可以移植转换和动作管道，执行您数据库中的更改，调用 Webhooks，并创建其他副作用？紧密结合可以让企业本身陷入困境。
为什么现在有灵活性
软件正在吞噬世界，AI正在吞噬软件。 由于团队既要使用AI来构建软件，又要为软件提供动力，因此更多的业务逻辑可以被捆绑在这些平台上。 每个有用的自动化也可以成为另一种移徙依赖。 让卖家站在你和敏捷之间很危险。
结论
活下来是令人惊奇的时刻！难怪人们正在探索本体学为agents提供背景。 将这种背景扩大到进程和一体化可以产生真正的价值。 回到开场白上来您的人事更正应该能更好地反映您的业务。 这种积累起来的理解太宝贵了，当你改变平台时，它不会被困住。 画成一角也太可怕了。
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
Thought Leaders
Portable Business Ontologies for AI Agents
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The term “ontology” is now en vogue. Why? Agents, and the LLMs that power them, need relevant business context.
Before you buy into an ontology, ask the vendor: “When the ontology gets our business wrong, how do our people correct it—and what still depends on you?” The answer will tell you a great deal about whose model of the business you are buying into. It also points to the larger stakes: your ability to improve that model as the business changes, and to keep that investment useful if you change platforms.
It helps to distinguish an ontology from a knowledge graph, even though the two often overlap.
Ontology: What kinds of things exist, what relationships mean, and what rules apply. Equipment, components, teams, roles, processes. For example, an ontology can define an “Equipment” type and a “Contains” relationship to a “Component”. A knowledge graph can then assert, “Pump P-101 contains Seal S-7”: two specific entities and their relationship.
Imagine you tell an agent, “Compute same-store sales growth, but show me a side-by-side where we show all stores, then another where we exclude stores open less than 2 years; then break both sides of that up into domestic, international, and combined.”
That ask depends on details an agent cannot guess:
- what stores exist, when they opened
- where they are
- the internal definition of “same-store sales”
A policy might exclude stores until they have been open a year; the requested two-year group adds a further filter. Even then, do you compare calendar months as soon as a store qualifies, or include it only after a full prior fiscal year?
The ontology needs to connect those stores to the policy that governs the calculation. Finding related facts helps; applying the right rule is what makes the answer useful.
An agent can use relationships in the graph to retrieve relevant context before answering a question. And no wonder they are useful, when knowledge domains can easily get very complex.
But extraction can also encode mistakes. Teams may use different names for the same entity, creating duplicates. Sources can disagree or become outdated. Even a clean source can be misread by an LLM. Making those mistakes look authoritative does not make them less dangerous.
Those risks grow when a model of the business becomes a system that acts on it. A wrong relationship can then trigger a wrong action, not just a wrong answer.
Risks of Ontology Products
First, be wary of inflexible models. In the name of making something “batteries included,” a vendor may make its own entities first-class citizens while limiting what yours can do. Customization is not enough if custom types lose search, validation, or action capabilities.
Second, look for a path from discovery to governance. A tool should let you bootstrap a graph from what an LLM sees, then validate it against explicit constraints. You should be able to escalate exceptions to people and preserve exploratory knowledge without treating it as approved business data.
The largest risk is tying business processes to a platform you cannot leave. Your ontology represents your business: its knowledge, processes, and relationships. That investment in curation and business logic should stay useful even if you change tools. Why should a vendor get to put that under lock and key?
Where Graph Embeddings Help
As knowledge graphs grow very large, it can be worth choosing a tool or vendor that supports graph embeddings. These are close cousins of the semantic embeddings that power many RAG applications. There are two useful benefits to consider.
First, a few hops through a densely connected graph can yield too many entities to process. By learning numerical representations of entities and relationships, embeddings offer another way to find relevant parts of the graph. For the right task, this can be more efficient than walking the graph.
Second, embedding models can rank candidate missing relationships. The same learning can reveal patterns in the attributes and relationships typical of an entity type, helping to build a more complete graph.
The Case for Progressive Improvement
To own your own destiny, avoid lock-in, and model your actual business instead of a vendor-dictated caricature of it, how do you begin without an army of forward-deployed engineers? This is where I think modern AI shines, if the tooling supports a path from discovery to verification.
First, use automated discovery and extraction. From business documents, policies, email, or Slack messages, you can construct an initial view of a domain. The critical part is that you cannot trust it as accurate until it is verified.
Verification is the second part. Your tools must give you a smooth path from extracted candidates to claims grounded in sources, and to knowledge validated by a responsible person or authoritative system. Preserve each claim’s source.
Freshness is ongoing work. The process for handling a trouble ticket, or the answer to “how many stores are there in New Jersey”, may not be the same today as it was three years ago. But updating today’s knowledge must not erase yesterday’s reality. When a fact was recorded and when it applied can differ; a useful ontology needs to support both current answers and an accurate historical view. Someone still must keep that knowledge current.
The scope can be massive. Start with a smaller corpus, map a useful domain, and build toward a comprehensive picture while creating value along the way.
Customer support is one useful slice: inbound calls, emails, a ticketing system, automated resolutions, a support team that can respond to customers, and a path to escalate true bugs to engineering. It connects knowledge to action and exposes the same risks and rewards in a manageable domain.
ServiceNow’s Common Service Data Model illustrates the constraint. Its CSDM 5 white paper warns, “Without this data in prescribed tables, you may not receive the full value of the ServiceNow AI Platform.” The warning itself creates a design and operational constraint: lean into the vendor’s model or accept a risk. What if that means cramming your round peg into their square hole?
Now suppose an ontology platform also supplies ingestion, transformations, actions, and business rules. You may be able to export entities, attributes, and relationships. But can you also port transformations and action pipelines that execute changes in your databases, call webhooks, and create other side effects? Tightly coupled integrations can leave the business itself locked in.
Why Flexibility Matters Now
Software is eating the world, and AI is eating software. As teams use AI both to build software and to power it, more business logic can become tied to these platforms. Each useful automation can also become another migration dependency. It’s dangerous to let a vendor stand between you and agility.
Conclusions
It’s an amazing time to be alive! Little wonder people are exploring ontologies to provide context to agents. Extending that context into processes and integrations can create real value. Come back to that opening question. Your people’s corrections should make the model a better reflection of your business. That accumulated understanding is too valuable to be stranded when you change platforms. It’s also a terrible time to be painted into a corner.
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