CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
The syntactic process
MindNet: acquiring and structuring semantic information from text
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
A commonsense approach to predictive text entry
CHI '04 Extended Abstracts on Human Factors in Computing Systems
ConceptNet — A Practical Commonsense Reasoning Tool-Kit
BT Technology Journal
Language independent NER using a maximum entropy tagger
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Extracting and evaluating general world knowledge from the Brown corpus
HLT-NAACL-TEXTMEANING '03 Proceedings of the HLT-NAACL 2003 workshop on Text meaning - Volume 9
Parsing the WSJ using CCG and log-linear models
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Espresso: leveraging generic patterns for automatically harvesting semantic relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
The importance of supertagging for wide-coverage CCG parsing
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Wide-coverage semantic representations from a CCG parser
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Knowledge integration across multiple texts
Proceedings of the fifth international conference on Knowledge capture
Conceptual knowledge acquisition using automatically generated large-scale semantic networks
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
A semantic network approach to measuring relatedness
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Semantic relatedness from automatically generated semantic networks
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Discovering novel biomedical relations using ASKNet semantic networks
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
LODifier: generating linked data from unstructured text
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
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The ASKNet system is an attempt to automatically generate large scale semantic knowledge networks from natural language text. State-of-the-art language processing tools, including parsers and semantic analysers, are used to turn input sentences into fragments of semantic network. These network fragments are combined using spreading activation-based algorithms which utilise both lexical and semantic information. The emphasis of the system is on wide-coverage and speed of construction. In this paper we show how a network consisting of over 1.5 million nodes and 3.5 million edges, more than twice as large as any network currently available, can be created in less than 3 days. We believe that the methods proposed here will enable the construction of semantic networks on a scale never seen before, and in doing so reduce the knowledge acquisition bottleneck for AI.