the largest freely available commonsense knowledgebase and natural-language-processing toolkit
Project description
ConceptNet is a freely available commonsense knowledgebase and
natural-language-processing toolkit which supports many practical
textual-reasoning tasks over real-world documents right out-of-the-box (without
additional statistical training) including
* topic-jisting (e.g. a news article containing the concepts, gun,
convenience store, demand money and make getaway might suggest the topics
robbery and crime),
* affect-sensing (e.g. this email is sad and angry),
* analogy-making (e.g. scissors, razor, nail clipper, and sword are
perhaps like a knife because they are all sharp, and can be used to cut
something),
* text summarization
* contextual expansion
* causal projection
* cold document classification
* and other context-oriented inferences
The ConceptNet knowledgebase is a semantic network presently available in two
versions: concise (200,000 assertions) and full (1.6 million assertions).
Commonsense knowledge in ConceptNet encompasses the spatial, physical, social,
temporal, and psychological aspects of everyday life. Whereas similar
large-scale semantic knowledgebases like Cyc and WordNet are carefully
handcrafted, ConceptNet is generated automatically from the 700,000 sentences of
the Open Mind Common Sense Project a World Wide Web based collaboration with
over 14,000 authors.
ConceptNet is a unique resource in that it captures a wide range of commonsense
concepts and relations, such as those found in the Cyc knowledgebase, yet this
knowledge is structured not as a complex and intricate logical framework, but
rather as a simple, easy-to-use semantic network, like WordNet. While ConceptNet
still supports many of the same applications as WordNet, such as query expansion
and determining semantic similarity, its focus on concepts-rather-than-words,
its more diverse relational ontology, and its emphasis on informal
conceptual-connectedness over formal linguistic-rigor allow it to go beyond
WordNet to make practical, context-oriented, commonsense inferences over
real-world texts.
At the end of the day, we want ConceptNet to be simply useful to AI Researchers
and computer enthusiasts who want to experiment with adding commonsense to make
their smart robots and programs smarter. And it's working! ConceptNet is
currently driving tens of new innovative research projects at MIT and elsewhere!
natural-language-processing toolkit which supports many practical
textual-reasoning tasks over real-world documents right out-of-the-box (without
additional statistical training) including
* topic-jisting (e.g. a news article containing the concepts, gun,
convenience store, demand money and make getaway might suggest the topics
robbery and crime),
* affect-sensing (e.g. this email is sad and angry),
* analogy-making (e.g. scissors, razor, nail clipper, and sword are
perhaps like a knife because they are all sharp, and can be used to cut
something),
* text summarization
* contextual expansion
* causal projection
* cold document classification
* and other context-oriented inferences
The ConceptNet knowledgebase is a semantic network presently available in two
versions: concise (200,000 assertions) and full (1.6 million assertions).
Commonsense knowledge in ConceptNet encompasses the spatial, physical, social,
temporal, and psychological aspects of everyday life. Whereas similar
large-scale semantic knowledgebases like Cyc and WordNet are carefully
handcrafted, ConceptNet is generated automatically from the 700,000 sentences of
the Open Mind Common Sense Project a World Wide Web based collaboration with
over 14,000 authors.
ConceptNet is a unique resource in that it captures a wide range of commonsense
concepts and relations, such as those found in the Cyc knowledgebase, yet this
knowledge is structured not as a complex and intricate logical framework, but
rather as a simple, easy-to-use semantic network, like WordNet. While ConceptNet
still supports many of the same applications as WordNet, such as query expansion
and determining semantic similarity, its focus on concepts-rather-than-words,
its more diverse relational ontology, and its emphasis on informal
conceptual-connectedness over formal linguistic-rigor allow it to go beyond
WordNet to make practical, context-oriented, commonsense inferences over
real-world texts.
At the end of the day, we want ConceptNet to be simply useful to AI Researchers
and computer enthusiasts who want to experiment with adding commonsense to make
their smart robots and programs smarter. And it's working! ConceptNet is
currently driving tens of new innovative research projects at MIT and elsewhere!
