NDLI: Reference reconciliation in complex information spaces

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Reference reconciliation in complex information spaces

Content Provider	ACM Digital Library
Author	Madhavan, Jayant Halevy, Alon Dong, Xin
Abstract	Reference reconciliation is the problem of identifying when different references (i.e., sets of attribute values) in a dataset correspond to the same real-world entity. Most previous literature assumed references to a single class that had a fair number of attributes (e.g., research publications). We consider complex information spaces: our references belong to multiple related classes and each reference may have very few attribute values. A prime example of such a space is Personal Information Management, where the goal is to provide a coherent view of all the information on one's desktop.Our reconciliation algorithm has three principal features. First, we exploit the associations between references to design new methods for reference comparison. Second, we propagate information between reconciliation decisions to accumulate positive and negative evidences. Third, we gradually enrich references by merging attribute values. Our experiments show that (1) we considerably improve precision and recall over standard methods on a diverse set of personal information datasets, and (2) there are advantages to using our algorithm even on a standard citation dataset benchmark.
Starting Page	85
Ending Page	96
Page Count	12
File Format	PDF
ISBN	1595930604
DOI	10.1145/1066157.1066168
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-06-14
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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