CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
WordNet: a lexical database for English
Communications of the ACM
Fast planning through planning graph analysis
Artificial Intelligence
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
Learner: a system for acquiring commonsense knowledge by analogy
Proceedings of the 2nd international conference on Knowledge capture
A goal-oriented interface to consumer electronics using planning and commonsense reasoning
Knowledge-Based Systems
What's next?: emergent storytelling from video collection
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Digital Intuition: Applying Common Sense Using Dimensionality Reduction
IEEE Intelligent Systems
AnalogySpace: reducing the dimensionality of common sense knowledge
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Planning for contingencies: a decision-based approach
Journal of Artificial Intelligence Research
Partition-based logical reasoning for first-order and propositional theories
Artificial Intelligence - Special volume on reformulation
The why UI: using goal networks to improve user interfaces
Proceedings of the 15th international conference on Intelligent user interfaces
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
EventNet: inferring temporal relations between commonsense events
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Common sense reasoning – from cyc to intelligent assistant
Ambient Intelligence in Everyday Life
Capability modeling of knowledge-based agents for commonsense knowledge integration
PRIMA'11 Proceedings of the 14th international conference on Agents in Principle, Agents in Practice
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Intelligent user interfaces require common sense knowledge to bridge the gap between the functionality of applications and the user’s goals. While current reasoning methods have been used to provide contextual information for interface agents, the quality of their reasoning results is limited by the coverage of their underlying knowledge bases. This article presents reasoning composition, a planning-based approach to integrating reasoning methods from multiple common sense knowledge bases to answer queries. The reasoning results of one reasoning method are passed to other reasoning methods to form a reasoning chain to the target context of a query. By leveraging different weak reasoning methods, we are able to find answers to queries that cannot be directly answered by querying a single common sense knowledge base. By conducting experiments on ConceptNet and WordNet, we compare the reasoning results of reasoning composition, directly querying merged knowledge bases, and spreading activation. The results show an 11.03% improvement in coverage over directly querying merged knowledge bases and a 49.7% improvement in accuracy over spreading activation. Two case studies are presented, showing how reasoning composition can improve performance of retrieval in a video editing system and a dialogue assistant.