Knowledge graphs improve information retrieval (IR) by organizing and representing data in a structured way, enabling systems to understand relationships between entities. Instead of treating documents as isolated pieces of information, knowledge graphs connect concepts, such as people, places, and events, with contextual relationships. This enables more accurate and relevant search results.
For example, when a user queries a knowledge graph-based system with a phrase like "president of the United States," the system can quickly pull up the most relevant result by recognizing the connection between "president" and "United States" from the graph. This eliminates ambiguity and improves result precision.
In IR, knowledge graphs provide additional context to improve search relevance, particularly in complex queries. They allow systems to go beyond keyword matching by recognizing entities and their interrelations, thus providing deeper insights and better results for users. This can be particularly useful in applications like search engines and recommendation systems.