aid: conceptnet name: ConceptNet description: >- ConceptNet is a freely available multilingual knowledge graph that gives computers access to common-sense knowledge. It represents over 13 million links between concepts across 100+ languages, drawing from crowd-sourced resources (Open Mind Common Sense, Wiktionary), expert-created resources (WordNet, JMDict), and games with a purpose (Verbosity, nadya.jp). The public REST API provides JSON-LD responses and receives over 50,000 daily hits. ConceptNet also powers Numberbatch, a set of multilingual word embeddings aligned across languages that outperform word2vec, GloVe, and fastText on standard benchmarks. url: https://conceptnet.io image: https://conceptnet.io/img/conceptnet-logo.png specificationVersion: '0.19' created: '2026-06-13' modified: '2026-06-13' x-source: manual x-category: Knowledge Graphs tags: - Knowledge Graph - NLP - Semantic Web - Common Sense - Multilingual - Word Embeddings - Linked Data - Open Data apis: - name: ConceptNet REST API description: >- The ConceptNet REST API exposes the full ConceptNet 5 knowledge graph via JSON-LD endpoints. Consumers can look up concept nodes by language and term, query edges by relation type, retrieve semantically related terms ranked by Numberbatch embedding similarity, compute pairwise relatedness scores between concepts, and normalize natural-language text into canonical ConceptNet URIs. No authentication or API key is required. Rate limits of 3,600 requests/hour and 120 requests/minute apply. The /related and /relatedness endpoints each count as 2 requests against the quota. humanURL: https://github.com/commonsense/conceptnet5/wiki/API baseURL: https://api.conceptnet.io tags: - Knowledge Graph - NLP - Semantic Relations - Multilingual - Common Sense properties: - type: Documentation url: https://github.com/commonsense/conceptnet5/wiki/API - type: GettingStarted url: https://github.com/commonsense/conceptnet5/wiki - type: Downloads url: https://github.com/commonsense/conceptnet5/wiki/Downloads common: - type: Website url: https://conceptnet.io - type: Documentation url: https://github.com/commonsense/conceptnet5/wiki/API - type: GettingStarted url: https://github.com/commonsense/conceptnet5/wiki - type: GitHubOrganization url: https://github.com/commonsense - type: GitHubRepository url: https://github.com/commonsense/conceptnet5 - type: License url: https://creativecommons.org/licenses/by-sa/4.0/ - type: Downloads url: https://github.com/commonsense/conceptnet5/wiki/Downloads - type: FAQ url: https://github.com/commonsense/conceptnet5/wiki/FAQ - type: Support url: https://groups.google.com/g/conceptnet-users - type: Plans url: plans/conceptnet-plans-pricing.yml - type: RateLimits url: rate-limits/conceptnet-rate-limits.yml - type: FinOps url: finops/conceptnet-finops.yml features: - name: Multilingual Knowledge Graph description: >- Covers concepts in 100+ languages with cross-language semantic links. Each concept node is identified by a URI such as /c/en/dog or /c/fr/chien, enabling cross-lingual knowledge transfer and multilingual NLP pipelines. - name: Semantic Relations description: >- Edges encode typed relations including IsA, UsedFor, CapableOf, AtLocation, Causes, HasProperty, PartOf, SimilarTo, Antonym, RelatedTo, and many more. Each edge carries a weight representing confidence from the source data. - name: JSON-LD Linked Data API description: >- All API responses use the JSON-LD format with @context, @id, and @type annotations, enabling seamless integration with RDF toolchains and semantic web applications. - name: Related Terms (Numberbatch Embeddings) description: >- The /related endpoint returns ranked semantically related concepts using ConceptNet Numberbatch word vectors — cross-lingual embeddings designed to avoid harmful stereotypes and outperform word2vec, GloVe, and fastText on analogy and similarity benchmarks. - name: Pairwise Relatedness Score description: >- The /relatedness endpoint returns a similarity score between 0 and 1 for any two concept URIs, enabling quick semantic similarity checks without building a local embedding model. - name: Complex Edge Queries description: >- The /query endpoint accepts combinations of start, end, rel, node, other, and sources parameters to slice the knowledge graph by subject, object, relation type, or data source simultaneously. - name: URI Normalization description: >- The /uri endpoint converts raw natural-language text in any supported language into a canonical ConceptNet URI, handling tokenization, lowercasing, and language-specific normalization automatically. - name: No Authentication Required description: >- The public API requires no registration, API key, or OAuth token. Any HTTP client can query api.conceptnet.io directly. useCases: - name: Semantic Similarity in NLP Pipelines description: >- Use /relatedness or /related to score or rank term similarity in question answering, text classification, and entity disambiguation tasks without training a custom embedding model. - name: Knowledge-Graph-Augmented AI description: >- Enrich LLM prompts or retrieval pipelines with structured commonsense facts by querying /c/{language}/{term} for edges describing causes, properties, and typical locations of a concept. - name: Cross-Language Information Retrieval description: >- Leverage ConceptNet's multilingual graph to expand a query in one language to synonymous concepts in another, supporting multilingual search and cross-lingual document clustering. - name: Educational Vocabulary Tools description: >- Build vocabulary-learning apps that surface semantic neighbors, antonyms, and example sentences for any word in dozens of languages using the IsA, SimilarTo, and Antonym relation edges. - name: Commonsense Reasoning Datasets description: >- Use ConceptNet as a gold-standard knowledge source for generating or evaluating commonsense reasoning benchmarks and training data for language models. - name: Chatbot Knowledge Enrichment description: >- Query ConceptNet for UsedFor, CapableOf, and AtLocation edges to give chatbots and virtual assistants grounded commonsense knowledge about everyday objects and actions. integrations: - name: Python (conceptnet5) description: >- The open-source Python codebase includes a local AssertionFinder API for querying a self-hosted ConceptNet database without HTTP overhead. url: https://github.com/commonsense/conceptnet5 - name: ConceptNet Numberbatch description: >- Pre-trained multilingual word embedding vectors (h5 and text formats) that can be loaded directly into NumPy, PyTorch, or TensorFlow for offline semantic similarity computation. url: https://github.com/commonsense/conceptnet-numberbatch - name: Linked Data / RDF description: >- JSON-LD responses integrate directly with JSON-LD processors, RDF triple stores (Apache Jena, Virtuoso), and SPARQL query engines. solutions: - name: Zero-Cost Semantic Enrichment description: >- Query /related for any concept in an NLP pipeline at zero cost and without authentication, adding knowledge-graph context to otherwise purely statistical models. - name: Self-Hosted High-Volume Deployment description: >- For applications exceeding 3,600 requests/hour, download the full ConceptNet data dump and run a local PostgreSQL-backed instance using the open-source codebase, eliminating rate limits entirely. maintainers: - FN: Kin Lane email: kin@apievangelist.com