conceptnet — ConceptNet commonsense knowledge graph (2017-2019)

Description

ConceptNet is a multilingual knowledge graph of general (commonsense) knowledge, which connects words and phrases of natural language with labeled and weighted relations. It combines knowledge collected by crowdsourcing (the Open Mind Common Sense project and its sister projects in other languages), games with a purpose (Verbosity, nadya.jp), expert-created resources (WordNet, Open Multilingual WordNet, JMDict, CC-CEDICT, OpenCyc, Unicode CLDR), Wikipedia infoboxes (via DBpedia) and the Wiktionaries of several languages. Each assertion is a directed edge from a term to another, with one of 34 relations, e.g. /r/IsA ("a dog is an animal"), /r/PartOf, /r/UsedFor, /r/CapableOf, /r/Synonym, /r/Antonym, /r/RelatedTo, /r/FormOf, /r/DerivedFrom, /r/EtymologicallyRelatedTo or /r/Translation-like relations between languages (/r/Synonym), and a weight that expresses the strength of the evidence (e.g. how many sources assert it).

Nodes are terms, identified by their ConceptNet URI /c/<language>/<text> (e.g. /c/en/ice_cream); assertions that refer to a particular sense of a term (e.g. /c/en/dog/n for the noun, or /c/en/dog/n/wn/animal for a WordNet sense) are attached to the term, and the sense is kept as an edge property. This follows ConceptNet's own convention, in which terms are the nodes of the graph and the senses are refinements of them. Each network corresponds to a release of ConceptNet: 5.7.0 (2019), 5.6.0 (2018) and 5.5.5 (2017).

The assertions of the relation /r/ExternalURL, which link terms to the URLs of the same concept in other linked-data resources (e.g. DBpedia, Wikidata, WordNet RDF, OpenCyc) rather than to other terms, are left out; their number is given by the graph property external_url_assertions. The provenance of each assertion beyond its dataset (the contributors and processes in its sources) and its surface text (the natural-language sentence it was collected from, if any) are also left out.

The networks have the following properties:

(Node) uri: the ConceptNet URI of the term, e.g. /c/en/ice_cream.
(Node) name: the text of the term, with underscores instead of spaces, e.g. ice_cream.
(Node) language: the language code of the term, e.g. en, fr, zh.
(Edge) relation: the relation of the assertion, e.g. /r/IsA.
(Edge) weight: the weight of the assertion (1 for most; higher for assertions with more supporting evidence, and lower for less reliable ones).
(Edge) dataset: the dataset the assertion comes from, e.g. /d/wiktionary/en, /d/conceptnet/4/en, /d/wordnet/3.1.
(Edge) license: the license of the assertion, cc:by/4.0 or cc:by-sa/4.0.
(Edge) start_sense, end_sense: the part of the URI of the start (end) node of the assertion that refines the term, e.g. n (a noun) or n/wn/animal (a WordNet sense), or empty if the assertion refers to the term as a whole.
(Node) release (graph): the release of ConceptNet.
(Node) external_url_assertions (graph): the number of /r/ExternalURL assertions, which are left out.

Tags

Informational Language Weighted Multigraph Metadata

Added

2026-10-28

Citation

R. Speer, J. Chin, C. Havasi, "ConceptNet 5.5: An Open Multilingual Graph of General Knowledge." Proceedings of the AAAI Conference on Artificial Intelligence 31(1), 4444-4451 (2017) 10.1609/aaai.v31i1.11164 Sci-Hub

Upstream URL OK

https://conceptnet.io/
https://github.com/commonsense/conceptnet5/wiki/Downloads

Upstream license

CC BY-SA 4.0 (some assertions CC BY 4.0), https://github.com/commonsense/conceptnet5/wiki/Copying-and-sharing-ConceptNet

Networks

Tip: click on a column header to sort the list, or hover over it for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
5.7.0	11,084,004	24,329,980	2.20	50.12	293.25	967.44	-0.01	0.01	36	0.96	Directed	Unipartite	uri name language	relation weight dataset license start_sense end_sense	156.3 MiB	238.4 MiB	228.1 MiB	234.3 MiB
5.6.0	10,897,724	23,133,509	2.12	59.28	325.92	878.96	-0.01	0.00	45	0.96	Directed	Unipartite	uri name language	relation weight dataset license start_sense end_sense	146.4 MiB	225.1 MiB	214.8 MiB	219.1 MiB
5.5.5	9,499,226	20,315,704	2.14	50.97	293.27	1296.12	-0.01	0.01	46	0.97	Directed	Unipartite	uri name language	relation weight dataset license start_sense end_sense	129.2 MiB	197.5 MiB	188.1 MiB	192.3 MiB