本体论强势回归：AI代理为何复兴语义网
本文报道Frank Coyle在AI Engineer World's Fair的演讲，认为本体论为AI代理提供逻辑护栏，Neo4j也在使用本体论，神经符号AI将神经网络与符号AI结合。
中文处理结果
近期 AI Engineer World's Fair 上观看次数最多的视频之一，是加州大学伯克利分校计算机科学教授 Frank Coyle 的 20 分钟演讲，他目前教授生成式 AI 和 LLM。凭借数十年的经验，Coyle 向当今的 AI 工程师重新介绍了本体论的概念与实践。
Coyle 认为，虽然 LLM 在提供概率推理方面非常有效，但要让代理系统真正有效，它们需要“逻辑护栏”——他指的正是本体论。
加州大学伯克利分校教授 Frank Coyle 在 AIEWF 2026 上发言。
在计算机科学中，本体论是“对数据结构——即知识领域中的类、属性和关系——的描述”（牛津语义技术公司的精确定义）。Coyle 本人将本体论简单定义为“作为图形的数据”。他补充说，本体论这一概念可以追溯到亚里士多德，并贯穿于人工智能的历史。
以图数据库系统闻名的 Neo4j 公司，也在其代理产品中使用本体论。
在 AIEWF 的主题演讲中，Neo4j 首席执行官 Emil Eifrem 谈到了三种不同类型的本体论，以实现“更智能的共享基底”来大规模运行代理。第一种是面向业务的本体论，描述组织中的关键概念；第二种是技术本体论，Eifrem 将其描述为“企业生态系统中所有数据源和数据资产的元数据”；最后是执行轨迹，即“来自代理的运行时信号”。
Neo4j 基于本体论的语义层
旧事重提：语义网
Frank Coyle 认为，网络本体论在构建代理系统时尤其有用。他提到了 Schema.org、FOAF、Dublin Core 以及 Web 开发者熟悉的其他本体论——至少是那些有一定资历的开发者。他还提到了 RDFS（资源描述框架模式）和 OWL（Web 本体语言）等“增强技术”。
使用这些既有本体论的一个好处是，它们已经存在于 LLM 的训练数据中，因此开发者可以直接通过提示词调用它们——比从头开始重新发明本体论要好得多。
“这些东西一直在我们已做的事情底层，”Coyle 说，“所以要利用这些已经存在的东西。”
作为例子，他展示了一个 Claude 代理循环，该循环在工具运行后使用本体论来帮助验证 LLM 的推理。
Coyle 将概率代理与本体论的融合称为“神经符号 AI”。
“听起来很花哨，”他说，“但它实际上是将神经网络与符号 AI 绑定在一起——基于规则的系统属于这一类，我们正在构建的知识图谱也是如此。”
他重申，神经符号 AI 代表“一种让 LLM 保持在护栏内的方法”。
本体论的优点与缺点
原始正文
Ontologies Are So Back: Why AI Agents Are Reviving the Semantic Web
One of the most watched videos from the recent AI Engineer World’s Fair is a 20-minute talk by Frank Coyle, a professor of computer science who currently teaches generative AI and LLMs at UC Berkeley. Drawing on his decades of experience, Coyle re-introduced the concept and practice of ontologies to today’s AI engineers.
Coyle argued that while LLMs are very effective at providing probabilistic reasoning, for agentic systems to be truly effective they need “logical guardrails” — by which he means ontologies.
UC Berkeley professor Frank Coyle speaking at AIEWF 2026.
In computer science, an ontology is “a description of data structure – of classes, properties, and relationships in a domain of knowledge” (as nicely defined by Oxford Semantic Technologies). Coyle himself defined an ontology as simply “data as graphs.” He added that ontologies as a concept go right back to Aristotle, and have been used throughout the history of Artificial Intelligence.
The company Neo4j, known for its graph database systems, is also using ontologies in its agentic products.
In a keynote session at AIEWF, Neo4j CEO Emil Eifrem talked about three different types of ontologies to enable a “smarter shared substrate” to run agents at scale. The first is a business-facing ontology, describing the key concepts in an organization; next is a technical ontology, which Eifrem described as “all the metadata of all the data sources and data assets in your enterprise ecosystem”; and finally execution traces, which are “the runtime signals out of your agent.”
Neo4j’s ontology-based semantic layer
What’s Old is New Again: the Semantic Web
Frank Coyle thinks web ontologies are especially useful in building agentic systems. He mentioned Schema.org, FOAF, Dublin Core and other ontologies familiar to web developers — or at least, those of a certain vintage. He also mentioned “augmenting technologies” like RDFS (Resource Description Framework Schema) and OWL (Web Ontology Language).
One benefit of using these established ontologies is that they’re already in the training data of LLMs, so developers can just prompt for them — much better than reinventing ontologies from first principles.
“This stuff has been out there underlying a lot of what we already do,” Coyle said. “So take advantage of these things that already exist.”
As an example, he showed a Claude agent loop which used an ontology to help validate the LLM’s reasoning after the tool had run.
Coyle calls the convergence of probabilistic agents with ontologies “neurosymbolic AI.”
“Sounds pretty fancy,” he said, “but it’s really neural networks tied into symbolic AI — rule-based systems come under that category, as do the knowledge graphs that we’re assembling.”
He reiterated that neurosymbolic AI represents “a way to keep the LLM on its guardrails.”
The Pros and Cons of Ontologies
Someone who has been steeped in ontologies for many years and is now combining that with AI engineering is Kingsley Idehen, who runs a company called OpenLink Software. He’s been building an “agent engineering stack” that uses Semantic Web technologies — including an “agent with RDF memory.”
Kingsley Idehen’s agent-rdf-memory system.
I asked Idehen to explain to me the benefits of ontologies.
“The beauty of LLMs is that they are powerful processors of language,” he replied. “The beauty of an ontology is that it defines the types of entities and relationships through which language acquires computable context. Together, they bring the expressive power of language to computing’s UI/UX stack.”
That makes a lot of sense, but if you’ve been a web developer for a while you’ll know the challenges of ontologies: maintenance and keeping them up-to-date. It’s why the 1990s and 2000s vision for a “Semantic Web” — which was based on ontologies — never took off.
Current AI developer Prasenjit Sarkar offered a potential solution for the maintenance problem on X, arguing that “when an agent maintains the ontology as part of its own operation, updating definitions when it encounters edge cases, the maintenance problem changes character.” It’s still a hard problem though, he added.
Despite these issues, the structured nature of ontologies does appear to be a good match with the occasionally wayward tendencies of probabilistic LLMs. You get the power of LLMs, but ontologies will keep them in check.
Plus, as Neo4j’s Eifrem explained, ontologies allow us to move from “a world of thick agents with manually wired data sources” to a new world of “thin agents on a smarter shared ontology-based semantic layer.”
Neo4j’s “thin agents” concept, which relies on ontologies.
Loops and Guardrails
Back to Coyle’s presentation. He also had a great point about loop engineering, which he noted “has been around forever” in computer science. The problem, of course, is that loops can break or otherwise “go off the rails.”
Again, this is where an ontology system can act as a guardrail to a probabilistic LLM. One of Coyle’s slides referred to it as “a bounded set of rules around an unbounded loop.”
Near the end of his presentation, Coyle demonstrated how to use OWL as a check on agents. One slide showed that while language can be slippery, “an OWL axiom is a rule a machine enforces.”
He also showed how “you can have a reasoner built on ontology, to check [and] keep the LLM on track — have guardrails to keep it honest.”
Semantic Vibes
Perhaps ontologies are starting to resonate with AI engineers because a central concern at this time is quality control for loop engineering. We saw this debate play out at AIEWF, with many conference speakers not willing to go all-in on fully automated “software factories” just yet. One of the key learnings from the event was that there need to be guardrails and humans in the loop.
Also it’s fascinating to see traditional web technologies make a resurgence in the field of AI engineering, especially after the 2025 trend of “vibe coding” made it seem like anyone could create software. Of course, since then the penny has dropped: we need to maintain that software and make sure it doesn’t break! So in 2026, we’re seeing a return to software engineering discipline — including now a revival of web ontologies as a way to keep probabilistic LLMs honest.