Ontologies Are So Back: Why AI Agents Are Reviving the Semantic Web
https://www.latent.space/p/ontologies-agentic-systems📌 【AI Engineer World’s Fair 焦點】為何 AI Agent 時代正讓「本體論 (Ontologies)」重新回歸？
TL;DR：LLM 擅長機率推理，但 Agent 需要本體論提供「邏輯護欄」來實現規模化運作。
🎣 從機率推理到邏輯護欄的轉向
在 AI Engineer World’s Fair 上，UC Berkeley 教授 Frank Coyle 指出，雖然大型語言模型 (LLM) 在提供機率推理 (probabilistic reasoning) 方面非常有效，但若要讓 Agentic systems (代理系統) 真正發揮作用，它們需要「邏輯護欄 (logical guardrails)」，也就是本體論 (ontologies)。
🧩 本體論就是「資料的圖譜化」
本體論的概念可以追溯至亞里斯多德，在人工智慧發展史上也扮演重要角色。從技術角度來看，本體論可以被簡單定義為「資料即圖譜 (data as graphs)」，它是對特定知識領域中類別 (classes)、屬性 (properties) 與關係 (relationships) 的資料結構描述。
📊 Neo4j 提出的三種本體論應用層次
圖譜資料庫領導廠商 Neo4j 的 CEO Emil Eifrem 提到，為了讓 Agent 能夠在大規模場景下運行，需要建立一個更聰明的「共享基底 (shared substrate)」，這包含三種不同類型的本體論：
- 面向業務的本體論 (Business-facing ontology)：描述組織中的核心概念。
- 技術本體論 (Technical ontology)：包含企業生態系統中所有資料來源與資料資產的元數據 (metadata)。
- 執行軌跡 (Execution traces)：來自 Agent 運行時 (runtime) 的訊號。
💡 利用既有的 Web 本體論與 LLM 訓練資料
Frank Coyle 特別強調，Web 本體論對於構建代理系統非常有益。開發者可以利用已有的成熟技術，例如：
- 既有的 Web 本體論：Schema.org、FOAF、Dublin Core。
- 增強技術 (augmenting technologies)：RDFS (Resource Description Framework Schema) 與 OWL (Web Ontology Language)。
這裡有一個對工程師極大的優勢：由於這些既有的本體論已經存在於 LLM 的訓練資料中，開發者可以直接透過 Prompt (提示詞) 要求模型遵循這些結構，這比從零開始定義要有效得多。
🎯 實務啟示
當開發者試圖將 Agent 從單純的對話機器人轉向複雜的自動化代理時，不應僅依賴 LLM 的機率預測。引入結構化的本體論，可以為 Agent 提供必要的邏輯框架，讓其在處理複雜的企業資料與業務邏輯時，具備更強的可靠性與可擴展性。
🔗 來源
- 標題：Ontologies Are So Back: Why AI Agents Are Reviving the Semantic Web
- 作者／機構：Latent Space
- 連結：https://www.latent.space/p/ontologies-agentic-systems
#AI #AIagents #Ontology #LLM #GraphDatabase #Neo4j #SemanticWeb #MachineLearning #DataStructure #ArtificialIntelligence
原始資料 Latent Space · 收集於 2026-07-31
摘要原文
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. 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.” 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 t
由 tencent/hy3:free 自動生成