Conceptual knowledge acquisition using automatically generated large-scale semantic networks

Authors:
Pia-Ramona Wojtinnek;Brian Harrington;Sebastian Rudolph;Stephen Pulman
Affiliations:
Oxford University Computing Laboratory, Oxford, UK;Oxford University Computing Laboratory, Oxford, UK;Institute AIFB, Karlsruhe Institute of Technology, DE;Oxford University Computing Laboratory, Oxford, UK
Venue:
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Year:
2010

Citing 6
Cited 1

Conceptual structures: information processing in mind and machine

Conceptual structures: information processing in mind and machine
A semantic concordance

HLT '93 Proceedings of the workshop on Human Language Technology
Word sense disambiguation: A survey

ACM Computing Surveys (CSUR)
Linguistically motivated large-scale NLP with C&C and boxer

ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
ASKNet: automated semantic knowledge network

AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
UBC-AS: a graph based unsupervised system for induction and classification

SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations

LODifier: generating linked data from unstructured text

ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications

Quantified Score

Hi-index	0.01

Visualization

Abstract

We present a method for automatically creating large-scale semantic networks from natural language text, based on deep semantic analysis. We provide a robust and scalable implementation, and sketch various ways in which the representation may be deployed for conceptual knowledge acquisition. A translation to RDF establishes interoperability with a wide range of standardised tools, and bridges the gap to the field of semantic technologies.