Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning to extract symbolic knowledge from the World Wide Web
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Efficient clustering of high-dimensional data sets with application to reference matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Semantic Networks for Knowledge Discovery
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Discovering relations among named entities from large corpora
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Names and similarities on the web: fact extraction in the fast lane
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Preemptive information extraction using unrestricted relation discovery
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Statistical predicate invention
Proceedings of the 24th international conference on Machine learning
Strategies for lifelong knowledge extraction from the web
Proceedings of the 4th international conference on Knowledge capture
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Learning systems of concepts with an infinite relational model
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Machine reading at the University of Washington
FAM-LbR '10 Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading
FAM-LbR '10 Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading
Learning complex action models with quantifiers and logical implications
Artificial Intelligence
Facilitating pattern discovery for relation extraction with semantic-signature-based clustering
Proceedings of the 20th ACM international conference on Information and knowledge management
Conceptual clustering of multi-relational data
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Ensemble semantics for large-scale unsupervised relation extraction
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Structural linguistics and unsupervised information extraction
AKBC-WEKEX '12 Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction
WiSeNet: building a wikipedia-based semantic network with ontologized relations
Proceedings of the 21st ACM international conference on Information and knowledge management
Machine learning of syntactic parse trees for search and classification of text
Engineering Applications of Artificial Intelligence
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
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Extracting knowledge from text has long been a goal of AI. Initial approaches were purely logical and brittle. More recently, the availability of large quantities of text on the Web has led to the development of machine learning approaches. However, to date these have mainly extracted ground facts, as opposed to general knowledge. Other learning approaches can extract logical forms, but require supervision and do not scale. In this paper we present an unsupervised approach to extracting semantic networks from large volumes of text. We use the TextRunner system [1] to extract tuples from text, and then induce general concepts and relations from them by jointly clustering the objects and relational strings in the tuples. Our approach is defined in Markov logic using four simple rules. Experiments on a dataset of two million tuples show that it outperforms three other relational clustering approaches, and extracts meaningful semantic networks.