Title :
Reinforcement learning-based annotation for Deep Web data
Author :
Lv, Yuefeng ; Fu, Yuchen
Author_Institution :
Sch. of Comput. Sci. & Technol., Soochow Univ., Suzhou, China
Abstract :
A semantic annotation method for web database query result which is under the condition of uncertain schema information is proposed in this paper by adopting the reinforcement learning method. Using domain ontology to annotate the query result, this paper takes the mapping between domain ontology and query result as a process of finding the best strategy. By training the numeric attribute value, we can find the best strategy to match and then annotate the query result. By collecting web databases from different domains, the experiments indicate that the approach proposed can annotate the web database query result properly and improve the efficiency of annotating.
Keywords :
Internet; database management systems; learning (artificial intelligence); Web database query; domain ontology; numeric attribute value; reinforcement learning based annotation; semantic annotation method; uncertain schema information; Application software; Computational intelligence; Computer industry; Computer science; Data mining; Databases; Learning; Ontologies; Research and development; Software engineering; Deep Web; reinforcement learning; schema matching; semantic annotation;
Conference_Titel :
Computational Intelligence and Industrial Applications, 2009. PACIIA 2009. Asia-Pacific Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4606-3
DOI :
10.1109/PACIIA.2009.5406419