지식 그래프

A knowledge graph is a structured representation of real-world entities and the relationships between them. Unlike a standard database that stores data in rigid rows and columns, a knowledge graph organizes information as a network of nodes (representing objects, people, or concepts) and edges (representing the connections or interactions between those nodes). This structure mimics how humans organize information, allowing artificial intelligence (AI) systems to understand context, infer new facts, and reason about data in a more semantic and interconnected way.

Understanding the Structure

At the core of a knowledge graph are three main components that form "triples" (Subject-Predicate-Object):

• Nodes (Entities): These are the distinct data points, such as "London," "Python," or "Ultralytics YOLO26." In computer vision tasks, these might represent detected objects like a "Car" or a "Pedestrian."
• Edges (Relationships): These distinct lines connect nodes and define how they relate. For instance, an edge might label the relationship between "London" and "UK" as "is_capital_of."
• Attributes (Properties): Additional details describing a node, such as the population of a city or the confidence score of an object detection.

This web-like structure enables systems to perform semantic search, where the engine understands the user's intent rather than just matching keywords. For example, knowing that "Jaguar" is both an animal and a car brand allows the system to differentiate results based on context.

Integration with Machine Learning

Knowledge graphs are increasingly vital for enhancing machine learning (ML) models. While deep learning models excel at statistical pattern recognition, they often lack factual grounding. Integrating a knowledge graph allows models to access a verified "world view."

• Retrieval-Augmented Generation (RAG):Generative models can sometimes produce plausible but incorrect information. By grounding Large Language Models (LLMs) with a knowledge graph, AI agents can query a verified source of truth before generating a response. This significantly reduces hallucinations in LLMs and improves factual accuracy for enterprise applications.
• Recommendation Systems:In AI in retail, graphs map complex relationships between users and products. If a customer purchases a camera, the graph understands the functional link to "SD Cards" or "Tripods," enabling smarter suggestions than simple collaborative filtering.

코드 예제: 그래프에 대한 엔티티 추출

Computer vision models act as excellent entry points for populating knowledge graphs by identifying physical entities in the real world. The following Python snippet demonstrates how to use the Ultralytics YOLO26 model to detect objects in an image. These detected classes can act as nodes, which can then be linked in a graph database (like Neo4j or Amazon Neptune).

from ultralytics import YOLO

# Load the latest YOLO26 model (released Jan 2026)
model = YOLO("yolo26n.pt")

# Run inference on an image to find entities
results = model("https://ultralytics.com/images/bus.jpg")

# Extract unique detected object names to serve as graph nodes
# e.g., {'bus', 'person'}
detected_entities = {results[0].names[int(c)] for c in results[0].boxes.cls}

print(f"Graph Nodes: {detected_entities}")

실제 애플리케이션

1. 의료 분야에서의 신약 개발: 의료 인공지능 연구진은 지식 그래프를 활용해 생물학적 상호작용을 모델링합니다. 유니프로트( UniProt)와 같은 데이터베이스의 유전자, 단백질, 화합물 등의 개체를 연결함으로써 알고리즘은 잠재적 약물 표적과 부작용을 예측하여 신규 치료법 개발을 가속화합니다.
2. 공급망 디지털 트윈: 물류 기업들은 지식 그래프를 활용해 운영의 '디지털 트윈'을 구축합니다. 노드는 공급업체, 창고, 재고를 나타내고, 에지는 운송 경로와 의존성을 나타냅니다. 이러한 구조는 빅데이터 분석을 용이하게 하여 관리자가 지연을 예측하고 경로를 동적으로 최적화할 수 있게 합니다.

Knowledge Graphs vs. Relational Databases

It is important to distinguish a knowledge graph from a traditional Relational Database (RDBMS). A relational database stores data in rigid tables linked by foreign keys, which is efficient for structured, transactional data (like bank ledgers). However, querying complex relationships (e.g., "Find friends of friends who like sci-fi") requires expensive "join" operations.

In contrast, a knowledge graph (often stored in a Graph Database) treats the relationship as a first-class citizen. Traversing connections is instantaneous, making graphs superior for tasks involving highly interconnected data, such as fraud detection rings or social network analysis. While RDBMS excels at storage and retrieval of specific records, knowledge graphs excel at discovering patterns and hidden insights within the connections themselves.

Future Outlook with Multi-Modal AI

The future of knowledge graphs lies in multi-modal learning. As models like Ultralytics YOLO26 continue to advance in object detection and pose estimation, they will automatically feed visual context into graphs. This creates systems that not only "read" text but "see" the world, linking visual concepts to linguistic definitions. Using the Ultralytics Platform, developers can train these specialized vision models to recognize custom entities, effectively building the sensory organs for the next generation of knowledge-aware AI systems.