Cyc: toward programs with common sense
Communications of the ACM
CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
Open Mind Common Sense: Knowledge Acquisition from the General Public
On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002
ConceptNet — A Practical Commonsense Reasoning Tool-Kit
BT Technology Journal
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind
The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind
Deriving generalized knowledge from corpora using WordNet abstraction
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
AnalogySpace: reducing the dimensionality of common sense knowledge
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
SemEval-2007 task 07: coarse-grained English all-words task
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Advanced Engineering Informatics
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The paper describes a semi-automatic method of identifying common-sense knowledge by running an ontological semantic system and focusing on its failures to interpret sentences that a human is not challenged by. Without common-sense knowledge, "He put a banana in his trunk" produces 6 representations, corresponding to 6 senses of "trunk" recorded in the lexicon and anchored in different ontological concepts -- roughly, car-part, elephant-part, tree-part, torso, luggage, software-term. A human language user reduces them to 3, using several pieces of common-sense knowledge that should therefore be added to the system resources to improve its performance. The significant result is that by running the system not only experimentally but also in real-life applications, we ensure an ongoing test against the lack of common-sense knowledge, at least some of which is known to be captured by the system, if it interprets the text correctly and not captured when the interpretation is inadequate.