Knowledge graphs enhance machine learning models by providing structured, interconnected data that adds context and relationships to raw inputs. They act as a source of domain-specific knowledge, helping models make sense of ambiguous or sparse data. For example, in natural language processing (NLP), a knowledge graph can link entities like “Paris” to concepts such as “capital of France” or “Eiffel Tower location,” enabling models to disambiguate meanings and improve tasks like entity recognition or question answering. By explicitly encoding relationships (e.g., “is_a,” “located_in”), knowledge graphs reduce the burden on models to infer connections from scratch.
A key benefit is improved feature engineering. Knowledge graphs allow models to incorporate relational features that aren’t present in raw data. For instance, a recommendation system could use a graph connecting users, products, and attributes (e.g., “User A bought Product B, which belongs to Category C”). Graph embeddings—vector representations of nodes and edges—can be generated and fed into ML models as input features. These embeddings capture semantic relationships, such as similarity between products, which a model might miss when processing transactional data alone. In healthcare, a knowledge graph linking symptoms, diseases, and treatments could help a diagnostic model prioritize likely conditions based on patient data and medical research.
Knowledge graphs also address data sparsity and enable semi-supervised learning. For example, in fraud detection, a graph of transaction histories, account linkages, and geographic patterns can help identify suspicious clusters even with limited labeled fraud examples. The graph structure allows models to propagate signals—like detecting that accounts connected to a flagged user are higher risk. Similarly, in social network analysis, a knowledge graph of user interactions can predict missing relationships (e.g., “User X likely knows User Y”) by leveraging existing connections. By integrating external knowledge, these graphs help models generalize better, especially in scenarios where training data is scarce or noisy.