#knowledge graph

Ontology là gì? Lời giải cho dữ liệu “bảo hiểm” trong kỷ nguyên Agentic AI Kim Jongwook · 2026-03-24 TL;DR Ontology là công nghệ dữ liệu AI giúp máy tính hiểu khái niệm và quan hệ giống con người. Ontology không chỉ tăng độ chính xác của LLM mà chủ yếu là để giảm rủi ro và ngăn “ảo giác”. Thời Agentic AI, dữ liệu sai không còn là lỗi nhỏ mà có thể thành thảm hoạ “Garbage In, Disaster Out”. Doanh…

The Master Data Ontology Manager is a specialized solution developed by PiLog to provide a semantic framework for managing master data. It enables the creation and maintenance of ontologies — formal representations of categories, relationships, attributes, and rules that describe a specific domain of knowledge. In simple terms, it gives your data context.

Instead of merely storing data as flat records, PiLog’s Ontology Manager organizes it hierarchically and semantically — allowing machines and humans to understand not only what the data is, but also how it relates to other data.

For example: A "pump" in an equipment database isn’t just a label — it belongs to a broader category of "mechanical devices," has attributes like flow rate and pressure, and is used in contexts like oil refineries or water treatment plants. Ontology allows this rich metadata to be embedded within the data model itself.

Why Is Ontology Important in Master Data?

Traditional data models often lack the sophistication to capture contextual meaning, domain-specific logic, and interrelationships between data elements. This leads to:

Inconsistent terminology across departments

Difficulty integrating data from multiple systems

Poor data quality and limited reuse

Ineffective search and analytics capabilities

Most organisations sit on a mix of structured records, documents, emails, images, and reports, yet still analyse them in isolation. 

Hybrid extraction approaches aim to bridge that gap by combining: 

Rule-based methods for consistency 

ML and LLMs for contextual understanding 

Knowledge graphs to connect entities, relationships, and meaning over time 

The result isn’t just better with extraction accuracy, but richer organisational memory where data becomes explorable, linked, and reusable across teams. 

Explore how hybrid extraction and knowledge graphs work together to support scalable, explainable intelligence : https://linkly.link/2Vx01

The world of data is constantly evolving, demanding increasingly sophisticated ways to model complex relationships and interactions. Traditional approaches often struggle when dealing with dynamic scenarios where understanding *what* happened and *who* or *what* was involved becomes paramount – not just *when* it occurred. We’re moving beyond simple timelines to a more nuanced perspective that…

When people search your business, they expect clear facts. Search engines expect the same. If those facts live in different places with small differences, trust drops. The fix is simple. Create one page that holds your core facts. We call it an Entity Home.

Search engines need clear signals to understand what your website represents. One of the most effective ways to send these signals is through schema markup — structured data that sits behind your pages.

O Knowledge Graph é uma das inovações mais importantes do Google e de outros motores de busca para transformar a forma como a informação é organizada, compreendida e apresentada ao usuário. Mais do que um simples banco de dados, ele funciona como uma rede semântica de entidades e relacionamentos, permitindo que a pesquisa vá além das palavras-chave e passe a compreender contexto, intenção e…