In this paper, we develop a decision-theoretic framework for ordering candidate matches for user confirmation using the concept of the value of perfect information (VPI). At the core of this concept is a utility function that quantifies the desirability of a given state; thus, we devise a utility function for dataspaces based on query result quality. We show in practice how to efficiently apply VPI in concert with this utility function to order user confirmations. A detailed experimental evaluation on both real and synthetic datasets shows that the ordering of user feedback produced by this VPI-based approach yields a dataspace with a significantly higher utility than a wide range of other ordering strategies. Finally, we outline the design of Roomba, a system that utilizes this decision-theoretic framework to guide a dataspace in soliciting user feedback in a pay-as-you-go manner. ER - TY - CONF AU - Dhamankar, Robin AU - Lee, Yoonkyong AU - Doan, AnHai AU - Halevy, Alon Y. AU - Domingos, Pedro A2 - Weikum, Gerhard A2 - König, Arnd Christian A2 - Deßloch, Stefan T1 - iMAP: Discovering Complex Mappings between Database Schemas. T2 - SIGMOD Conference PB - ACM C1 - PY - 2004/ CY - VL - IS - SP - 383 EP - 394 UR - http://www.cs.washington.edu/homes/pedrod/papers/sigmod04.pdf DO - KW - semantic KW - srl KW - mapping KW - web L1 - SN - 1-58113-859-8 N1 - dblp N1 - AB - ER - TY - CONF AU - Halevy, Alon Y. AU - Madhavan, Jayant A2 - Gottlob, Georg A2 - Walsh, Toby T1 - Corpus-Based Knowledge Representation T2 - IJCAI-03, Proceedings of the Eighteenth International Joint Conference
on Artificial Intelligence, Acapulco, Mexico, August 9-15, 2003 PB - Morgan Kaufmann C1 - PY - 2003/ CY - VL - IS - SP - 1567 EP - 1572 UR - DO - KW - representation KW - based KW - corpus KW - knowledge L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Doan, AnHai AU - Madhavan, Jayant AU - Domingos, Pedro AU - Halevy, Alon A2 - T1 - Learning to Map between Ontologies on the Semantic Web T2 - Proceedings to the Eleventh International World Wide PB - C1 - Honolulu, Hawaii, USA PY - 2002/05 CY - VL - IS - SP - EP - UR - http://www.cs.washington.edu/homes/alon/site/files/glue.pdf DO - KW - srl KW - mapping KW - ontology L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Doan, AnHai AU - Domingos, Pedro AU - Halevy, Alon Y. T1 - Reconciling schemas of disparate data sources: a machine-learning approach JO - SIGMOD Rec. PY - 2001/ VL - 30 IS - 2 SP - 509 EP - 520 UR - http://portal.acm.org/citation.cfm?id=375731&dl=GUIDE&coll=GUIDE&CFID=75153142&CFTOKEN=89522229 DO - http://doi.acm.org/10.1145/376284.375731 KW - schema KW - ol KW - learning KW - mapping KW - data KW - mining L1 - SN - N1 - N1 - AB - A data-integration system provides access to a multitude of data sources through a single mediated schema. A key bottleneck in building such systems has been the laborious manual construction of semantic mappings between the source schemas and the mediated schema. We describe LSD, a system that employs and extends current machine-learning techniques to semi-automatically find such mappings. LSD first asks the user to provide the semantic mappings for a small set of data sources, then uses these mappings together with the sources to train a set of learners. Each learner exploits a different type of information either in the source schemas or in their data. Once the learners have been trained, LSD finds semantic mappings for a new data source by applying the learners, then combining their predictions using a meta-learner. To further improve matching accuracy, we extend machine learning techniques so that LSD can incorporate domain constraints as an additional source of knowledge, and develop a novel learner that utilizes the structural information in XML documents. Our approach thus is distinguished in that it incorporates multiple types of knowledge. Importantly, its architecture is extensible to additional learners that may exploit new kinds of information. We describe a set of experiments on several real-world domains, and show that LSD proposes semantic mappings with a high degree of accuracy. ER -