Knowledge graphs enhance natural language processing (NLP) by providing structured, interconnected data that helps models better understand context, relationships, and real-world knowledge. A knowledge graph organizes information as entities (e.g., people, places, concepts) and their relationships (e.g., “is a,” “located in”), creating a network of facts. This structure allows NLP systems to access explicit semantic connections that are often missing in raw text. For example, when processing a sentence like “Paris is the capital of France,” a knowledge graph can confirm that “Paris” refers to a city linked to “France” via a “capital_of” relationship, reducing ambiguity and improving accuracy.
One key application of knowledge graphs in NLP is entity disambiguation and linking. For instance, the word “Apple” could refer to a company or a fruit. By mapping text mentions to entries in a knowledge graph (e.g., Wikidata or DBpedia), NLP systems can resolve such ambiguities. A search engine using a knowledge graph might distinguish between queries about “Apple stock prices” (company) and “apple nutrition facts” (fruit) by checking the graph’s relationships. Similarly, in medical NLP, a symptom like “headache” can be linked to related conditions (e.g., migraines) or treatments through a domain-specific knowledge graph, improving diagnostic support tools. This structured approach reduces errors caused by relying solely on statistical patterns in text data.
Knowledge graphs also improve semantic reasoning and question answering. For example, if a user asks, “Which movies did Christopher Nolan direct?” an NLP system can traverse a knowledge graph to retrieve films linked to Nolan via a “directed_by” relationship. Without the graph, the model might struggle if the answer isn’t explicitly stated in its training data. Additionally, chatbots use knowledge graphs to maintain context in multi-turn conversations. If a user says, “Book a flight to Paris,” then later asks, “What’s the weather there?” the graph’s “Paris→France→capital” relationships help the bot infer “there” refers to Paris. By integrating external knowledge, models become less dependent on memorized patterns, leading to more reliable and explainable outputs in tasks like summarization or recommendation systems.