CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
YAGO: A Large Ontology from Wikipedia and WordNet
Web Semantics: Science, Services and Agents on the World Wide Web
Searching for common sense: populating Cyc™ from the web
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Deriving a large scale taxonomy from Wikipedia
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Decoding wikipedia categories for knowledge acquisition
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
DBpedia - A crystallization point for the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Distinguishing between instances and classes in the wikipedia taxonomy
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
Using ontological and document similarity to estimate museum exhibit relatedness
Journal on Computing and Cultural Heritage (JOCCH)
Photo retrieval combining ontology with visual information
Proceedings of the 2012 ACM Research in Applied Computation Symposium
Wikipedia-based WSD for multilingual frame annotation
Artificial Intelligence
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In order to achieve genuine web intelligence, building some kind of large general machine-readable conceptual scheme (i.e. ontology) seems inescapable. Yet the past 20 years have shown that manual ontology-building is not practicable. The recent explosion of free user-supplied knowledge on the Web has led to great strides in automatic ontology-building, but quality-control is still a major issue. Ideally one should automatically build onto an already intelligent base. We suggest that the long-running Cyc project is able to assist here. We describe methods used to add 35K new concepts mined from Wikipedia to collections in ResearchCyc entirely automatically. Evaluation with 22 human subjects shows high precision both for the new concepts’ categorization, and their assignment as individuals or collections. Most importantly we show how Cyc itself can be leveraged for ontological quality control by ‘feeding’ it assertions one by one, enabling it to reject those that contradict its other knowledge.