How do knowledge graphs improve IR?

Knowledge graphs improve information retrieval (IR) by structuring data as interconnected entities and their relationships, enabling systems to understand context and infer meaning beyond keyword matching. Traditional IR systems rely on lexical analysis, which struggles with ambiguous terms, synonyms, or complex queries requiring domain-specific knowledge. Knowledge graphs address these limitations by explicitly modeling real-world entities (e.g., people, places, concepts) and their semantic connections. For example, a search for “Apple” can be disambiguated using linked data: if the user’s query context includes terms like “iPhone” or “Cupertino,” the system can prioritize the tech company over the fruit. This structured approach allows IR systems to interpret intent and deliver more relevant results.

A key advantage is the ability to resolve entity relationships during search. For instance, a query like “scientists who worked on AI and were educated in Europe” requires connecting multiple entities (scientists, institutions, research fields) across layers of data. A knowledge graph can traverse these relationships efficiently, identifying individuals like Yann LeCun (associated with Meta AI and educated in France) without relying solely on text patterns. This also enables query expansion: if a user searches for “Python,” the system can differentiate between the programming language and the animal by analyzing adjacent terms (e.g., “code” vs. “habitat”) and leveraging precomputed entity attributes. Such capabilities reduce ambiguity and improve recall, especially in domains like healthcare or technical documentation where precision is critical.

Finally, knowledge graphs enhance IR by enabling dynamic aggregation of information. Developers can use graph traversal algorithms to surface indirect connections, such as recommending articles authored by colleagues of a researcher mentioned in a query. For example, a search for “machine learning conferences in 2023” could return not only event dates but also related papers presented there, linked via author affiliations. This structured data layer also simplifies integration with other tools—like recommendation engines or chatbots—by providing a unified representation of entities and their attributes. By organizing data into a graph, IR systems move beyond keyword-based indexing, offering users contextual, interconnected answers that better align with real-world knowledge.