Tech Term Decoded: Triple (Semantic Triple)

Definition

A triple, which is also called a Semantic Triple or a subject-predicate-object expression, is a data structure that stands for a relationship between three elements in a way that a computer understands by using part-of-speech tagging. These elements include a subject, a predicate, and an object, which are related to one another in the shape of a triple. The subject is the entity that the triple is about, the predicate is the relationship between the subject and the object, and the object is the entity that the subject is related to [1]. For example;

Subject: Egegbara Kelechi

Predicate: is a

Object: Lecturer

Illustration of  a semantic triple relationship in AI [2]

Origin

In the 1960s, researchers like Sheldon Klein, M. Ross Quillian, and others at System Development Corporation explored methods for representing knowledge using semantic networks, laying the groundwork for semantic triples.

Context and Usage

Semantic triples are usually applied in the field of natural language processing and machine learning to assist computers know and comprehend the meaning of text. In the context of search engines, semantic triples can be utilized to aid in pin pointing the main themes and topic clusters of a webpage and to improve comprehension of the relationships between different pieces of information on the page [1].

Why it Matters

Triples are more than just organizing facts for the sake of it - they make AI smarter. By structuring knowledge using this process, AI systems can triangulate the exact piece of information required to answer a question. It enables AI to efficiently navigate a web of relationships – connecting ideas, filling in gaps, and even reasoning over new facts, instead of blindly searching through a mass of text.

From financial services to transport networks, the ability to structure and retrieve knowledge efficiently is a game-changer. And at the core of it all? A simple, three-part statement: the humble triple [3].

In practice

A real-life case study of a company practicing semantic triples in AI can be seen in the case of Google with its Knowledge Graph. Google's Knowledge Graph uses semantic triples (subject-predicate-object relationships) to structure information about people, places, and things. This implementation allows Google to understand the meaning behind queries rather than just matching keywords, enabling more intelligent search responses and laying the foundation for more advanced AI reasoning capabilities [4].

See Also 

Related NLP and Text Processing terms: 

• Speech Recognition: Converting spoken language into written text
• Syntax Analysis: Understanding sentence structure 
• Text Analytics: Deriving insights from text 
• Text Summarization: Condensing content automatically 
• Tokens: Individual units (words, subwords, characters) that text is divided into for processing.

References

1. Market Brew. (2025). The Benefits of Using Semantic Triples in SEO.
2. Research Gate. (n.d). A knowledge graph fragment: observed semantic triples are marked with solid arrows, while unobserved semantic triples are marked with dashed arrows.
3. Knowledge Graph Guys. (n.d). What is a Triple?
4. Peluso, K. (2024). How Do Semantic Triples Improve Search Visibility?