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,
affect-sensing (e.g. this email is sad and angry),
analogy-making
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.