What is a knowledge graph, and how is it used in IR?

A knowledge graph is a structured representation of information that organizes data as interconnected entities and their relationships. It uses nodes (entities like people, places, or concepts) and edges (relationships between them) to create a network of facts. For example, a knowledge graph about movies might link entities like “Christopher Nolan” to “Inception” with a “directed_by” relationship and “Inception” to “Leonardo DiCaprio” with a “starred_by” edge. This structure enables machines to understand context and infer connections that aren’t explicitly stated. Unlike traditional databases, knowledge graphs are flexible, allowing dynamic integration of diverse data sources (e.g., combining product catalogs with customer reviews) while maintaining semantic clarity. Tools like RDF (Resource Description Framework) or property graphs (e.g., Neo4j) are often used to model this data.

In information retrieval (IR), knowledge graphs enhance search and data discovery by adding semantic context. Traditional keyword-based IR systems match queries to documents using terms, but knowledge graphs enable systems to interpret user intent. For instance, a search for “Apple” could return results about the company, the fruit, or products like the iPhone, depending on the user’s context. By analyzing relationships in the graph (e.g., “Apple Inc. manufactures iPhone”), the system disambiguates terms and prioritizes relevant entities. Knowledge graphs also support entity-centric search, where results are grouped by connected concepts. For example, a query for “scientists who won Nobel Prizes” might traverse edges between “scientist,” “award,” and “Nobel Prize” to compile a list of laureates, their fields, and discoveries. This approach improves recall and precision by leveraging structured data instead of relying solely on text matches.

Developers use knowledge graphs in IR systems to build features like auto-suggestions, faceted search, or personalized recommendations. For example, an e-commerce platform might use a product knowledge graph to suggest related items based on shared attributes (e.g., “customers who bought this camera also purchased these lenses”). In enterprise search, a knowledge graph could link internal documents, employee profiles, and project data to answer queries like “find engineers with Python experience in the Berlin office.” APIs like Google’s Knowledge Graph Search or open-source frameworks (e.g., Apache Jena) simplify integration. By mapping relationships, knowledge graphs also enable reasoning—for instance, inferring that a search for “electric vehicles under $30k” should include models with tax incentives that effectively reduce their price. This structured approach makes IR systems more intuitive and efficient for users.