WordNet

WordNet organises English around meaning rather than spelling. Words that share a sense are gathered into a synset (a set of synonyms), and those synsets are wired together by relationships: "is a kind of" (so a robin is a kind of bird), "is a part of," and "is the opposite of," among others. Because a single word can carry several senses, WordNet keeps each one separate, which is what lets software tell a river bank from a savings bank. The database covers well over 100,000 synsets across nouns, verbs, adjectives, and adverbs.

Getting started is genuinely easy. WordNet is small, runs entirely on your own machine with no API, rate limits, or network calls, and comes bundled inside most language toolkits, NLTK among them, with libraries in nearly every programming language. You can also download the raw database files straight from Princeton. Parallel efforts extend the same structure to dozens of other languages through the Open Multilingual WordNet, so the approach is not limited to English.

For RAG and search, WordNet belongs in the pre-processing and retrieval layers rather than serving as a source you answer from. It is well suited to expanding a query with synonyms and related terms so retrieval catches documents that phrase things differently, to word-sense disambiguation (deciding which meaning of an ambiguous word is intended), and to measuring how closely two words relate in meaning. Applied before your embeddings run, those techniques can noticeably lift recall on sparse or keyword-heavy queries.

Two things to keep in mind. The licence is genuinely permissive and fine for commercial use, so that is not a concern. The real limits are in the data itself: WordNet is hand-built, has not been actively expanded in years, and is thin on proper nouns, technical jargon, and current slang. For factual, entity-level knowledge, reach instead for Wikidata or ConceptNet, and treat WordNet as dependable lexical scaffolding beside them.