Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
Coupled semi-supervised learning for information extraction
Proceedings of the third ACM international conference on Web search and data mining
Scalable knowledge harvesting with high precision and high recall
Proceedings of the fourth ACM international conference on Web search and data mining
REX: explaining relationships between entity pairs
Proceedings of the VLDB Endowment
Discovering relations between noun categories
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Structured relation discovery using generative models
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Identifying relations for open information extraction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Search Computing: challenges and Directions
Search Computing: challenges and Directions
Probase: a probabilistic taxonomy for text understanding
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
PATTY: a taxonomy of relational patterns with semantic types
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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We propose a demonstration of PATTY, a system for learning semantic relationships from the Web. PATTY is a collection of relations learned automatically from text. It aims to be to patterns what WordNet is to words. The semantic types of PATTY relations enable advanced search over subject-predicate-object data. With the ongoing trends of enriching Web data (both text and tables) with entity-relationship-oriented semantic annotations, we believe a demo of the PATTY system will be of interest to the database community.