@article {14365,
	title = {Collective entity resolution in relational data},
	journal = {ACM Trans. Knowl. Discov. Data},
	volume = {1},
	year = {2007},
	month = {2007/03//},
	abstract = {Many databases contain uncertain and imprecise references to real-world entities. The absence of identifiers for the underlying entities often results in a database which contains multiple references to the same entity. This can lead not only to data redundancy, but also inaccuracies in query processing and knowledge extraction. These problems can be alleviated through the use of entity resolution. Entity resolution involves discovering the underlying entities and mapping each database reference to these entities. Traditionally, entities are resolved using pairwise similarity over the attributes of references. However, there is often additional relational information in the data. Specifically, references to different entities may cooccur. In these cases, collective entity resolution, in which entities for cooccurring references are determined jointly rather than independently, can improve entity resolution accuracy. We propose a novel relational clustering algorithm that uses both attribute and relational information for determining the underlying domain entities, and we give an efficient implementation. We investigate the impact that different relational similarity measures have on entity resolution quality. We evaluate our collective entity resolution algorithm on multiple real-world databases. We show that it improves entity resolution performance over both attribute-based baselines and over algorithms that consider relational information but do not resolve entities collectively. In addition, we perform detailed experiments on synthetically generated data to identify data characteristics that favor collective relational resolution over purely attribute-based algorithms.},
	keywords = {data cleaning, entity resolution, graph clustering, record linkage},
	isbn = {1556-4681},
	doi = {10.1145/1217299.1217304},
	url = {http://doi.acm.org/10.1145/1217299.1217304},
	author = {Bhattacharya,Indrajit and Getoor, Lise}
}
@conference {14464,
	title = {Iterative record linkage for cleaning and integration},
	booktitle = {Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery},
	series = {DMKD {\textquoteright}04},
	year = {2004},
	month = {2004///},
	pages = {11 - 18},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Record linkage, the problem of determining when two records refer to the same entity, has applications for both data cleaning (deduplication) and for integrating data from multiple sources. Traditional approaches use a similarity measure that compares tuples{\textquoteright} attribute values; tuples with similarity scores above a certain threshold are declared to be matches. While this method can perform quite well in many domains, particularly domains where there is not a large amount of noise in the data, in some domains looking only at tuple values is not enough. By also examining the context of the tuple, i.e. the other tuples to which it is linked, we can come up with a more accurate linkage decision. But this additional accuracy comes at a price. In order to correctly find all duplicates, we may need to make multiple passes over the data; as linkages are discovered, they may in turn allow us to discover additional linkages. We present results that illustrate the power and feasibility of making use of join information when comparing records.},
	keywords = {clustering, deduplication, distance measure, record linkage},
	isbn = {1-58113-908-X},
	doi = {10.1145/1008694.1008697},
	url = {http://doi.acm.org/10.1145/1008694.1008697},
	author = {Bhattacharya,Indrajit and Getoor, Lise}
}
