Knowledge graph technology vs large language models in recruitment

When most people think of AI, they picture large language models (LLMs) like ChatGPT or Gemini, which excel at generating human-like text, answering questions, and performing creative tasks. However, AI is a much broader concept encompassing a wide range of technologies. Other AI techniques often focus on narrower, task-specific goals, such as optimizing supply chains, improving diagnostic accuracy in healthcare, or enabling real-time translation in communication tools.

 

Knowledge graphs technology vs large language models

Knowledge graph technology is a structured framework that organizes information into a network of entities and their relationships, creating a semantic map of interconnected data points. Unlike generative AI and large language models (LLMs), which predict sequences of words based on patterns in large datasets, knowledge graphs prioritize accuracy, clarity, and contextual relationships within a predefined schema.

While LLMs generate responses dynamically and probabilistically, knowledge graphs are deterministic, offering precise answers by navigating defined nodes and edges. This makes knowledge graphs better suited for applications where the accuracy of relationships and explicit facts are critical, such as database management, semantic search or recommendation systems.

 

The power of knowledge graphs in specialist recruiting

Knowledge graphs excel in specialist domains with complex and uncommon terminology because they enable precise organization and retrieval of niche information. When recruiting for industries like energy, resources and technology, knowledge graphs provide an intuitive way to represent intricate data relationships and specialist terms.

For instance, in engineering recruitment, a knowledge graph is better placed to understand the contextual relationship between a Process Engineering role in mining versus that same role title in the energy sector. Similarly, a knowledge graph is then much better placed to understand the differences in the skills requirements of the two types of process engineers based on their relationship to the industry in which they work or perhaps even the employers who they have worked for.

This precision and clarity make knowledge graphs indispensable for domains where generative AI might struggle with ambiguity or misinterpretation due to the rarity or specificity of terms – which is why Hiremii’s matching processes use knowledge graph technology and are thus, not very chatty.