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Proceedings of the third annual conference on Autonomous Agents
Efficient clustering of high-dimensional data sets with application to reference matching
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Reconciling schemas of disparate data sources: a machine-learning approach
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Multistrategy Learning for Information Extraction
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Potter's Wheel: An Interactive Data Cleaning System
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Database Schema Matching Using Machine Learning with Feature Selection
CAiSE '02 Proceedings of the 14th International Conference on Advanced Information Systems Engineering
Interactive deduplication using active learning
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Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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AI Magazine - Special issue on semantic integration
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IEEE Transactions on Knowledge and Data Engineering
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WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
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A Graph Partitioning Approach to Entity Disambiguation Using Uncertain Information
GoTAL '08 Proceedings of the 6th international conference on Advances in Natural Language Processing
A strategy for allowing meaningful and comparable scores in approximate matching
Information Systems
A strategy for allowing meaningful and comparable scores in approximate matching
Information Systems
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EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
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DS'05 Proceedings of the 8th international conference on Discovery Science
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Object matching is a fundamental problem that arises in numerous information integration scenarios. Virtually all existing solutions assume that the objects to be matched share the same attribute set and that systems can match them by comparing attribute similarities. Our work addresses the more general problem in which objects also have disjoint attributes-for example, matching tuples from relational tables that have different schemas, such as (age, name) and (name, salary). Profile-Based Object Matching, which applies this idea, exploits disjoint attributes to improve matching accuracy. PROM first matches any two tuples based on a shared attribute, such as name. It then applies a set of profilers, each of which contains some knowledge about what constitutes a typical person. The profilers examine the tuple pair to see if it plausibly describes a person. A profiler might state, for example, that if the pair produces a person with an age of 6 and a salary of $100,000, the pair doesn't describe a real person, so the tuples don't match. Profilers can be manually specified by domain experts, trained on training data, transferred from other matching tasks, or built from external data. PROM is thus distinct in that it not only exploits disjoint attributes to improve matching accuracy but also facilitates knowledge reuse from previous object-matching tasks.