Bundles in Captivity: An Application of Superimposed Information
Proceedings of the 17th International Conference on Data Engineering
Interactive deduplication using active learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Putting integrated information in context: superimposing conceptual models with SPARCE
APCCM '04 Proceedings of the first Asian-Pacific conference on Conceptual modelling - Volume 31
Personal information management with SEMEX
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
A formal characterization of PIVOT/UNPIVOT
Proceedings of the 14th ACM international conference on Information and knowledge management
Unsupervised personal name disambiguation
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Pair-Wise entity resolution: overview and challenges
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Capturing users' everyday, implicit information integration decisions
ER '07 Tutorials, posters, panels and industrial contributions at the 26th international conference on Conceptual modeling - Volume 83
| Hi-index | 0.00 |
Data integration on a human-manageable scale, by users without database expertise, is a more common activity than integration of large databases. Users often gather fine-grained data and organize it in an entity-centric way, developing tables of information regarding real-world objects, ideas, or people. Often, they do this by copying and pasting bits of data from e-mails, databases, or text files into a spreadsheet. During this process, users evolve their notions of entities and attributes. They combine sets of entities or attributes, split them again, update attribute values, and retract those updates. These functions are neither well supported by current tools, nor formally well understood. Our research seeks to capture and make explicit the data integration decisions made during these activities. In this paper, we formally define entity resolution and de-resolution, and show that these functions behave predictably and intuitively in the presence of attribute value updates.