Semantic Web Dog Food

Presented at: 20th International World Wide Web Conference (WWW2011)

by Danushka Bollegala, Yutaka Matsuo, Mitsuru Ishizuka

We propose a method to adapt an existing relation extraction system to extract new relation types with minimum supervision. Our proposed method comprises two stages: learning a lower-dimensional projection between different relations, and learning a relational classifier for the target relation type with instance sampling.We evaluate the proposed method using a dataset that contains 2000 instances for 20 different relation types. Our experimental results show that the proposed method achieves a statistically significant macro-average F-score of 62.77. Moreover, the proposed method outperforms numerous baselines and a previously proposed weakly-supervised relation extraction method.

From Actors, Politicians, to CEOs: Domain Adaptation of Relational Extractors Using a Latent Relational Mapping was presented at this event.

Keywords: World Wide Web

Resource URI on the dog food server: http://data.semanticweb.org/conference/www/2011/poster/from-actors-politicians-to-ceos-domain-adaptation-

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