NLP interacts with knowledge graphs by enabling the extraction, organization, and utilization of structured information from unstructured text. Knowledge graphs represent entities (like people, places, or concepts) and their relationships in a graph format, while NLP techniques process natural language to identify these entities and connections. For example, named entity recognition (NER) in NLP can identify “Paris” as a city in a sentence, and relation extraction can determine that “Paris is the capital of France.” This data can then be structured into a knowledge graph, creating nodes for “Paris” and “France” linked by a “capital_of” edge. Tools like spaCy or Stanford CoreNLP often handle the NLP side, while graph databases like Neo4j or frameworks like RDF triplestores manage the knowledge graph storage.
Knowledge graphs also enhance NLP tasks by providing contextual and relational data. For instance, a question-answering system can use a knowledge graph to resolve ambiguities. If a user asks, “Who founded Microsoft?” the system might retrieve “Paul Allen” and “Bill Gates” from the graph, even if the input text only mentions “two founders.” Similarly, entity linking—a process that maps ambiguous terms in text to unique graph nodes—relies on knowledge graphs. For example, “Apple” in a sentence could refer to the company or the fruit; a knowledge graph with entity metadata (e.g., “Apple Inc.” vs. “apple fruit”) helps disambiguate this. Libraries like Wikidata or DBpedia serve as large-scale public knowledge graphs for such purposes.
The interaction is bidirectional: NLP populates knowledge graphs with structured data, and knowledge graphs improve NLP model performance. For example, training a language model like BERT on text enhanced with knowledge graph embeddings (e.g., entity types or relationships) can improve its understanding of context. Conversely, as new data is processed by NLP pipelines (e.g., news articles or research papers), the extracted information updates the knowledge graph. A practical use case is in healthcare: NLP extracts drug-disease relationships from clinical notes, which are added to a medical knowledge graph. Later, the graph can help NLP models answer complex queries like “Which drugs interact with ibuprofen?” by traversing connected nodes. This cycle of extraction and application makes NLP and knowledge graphs mutually reinforcing tools.