Joint Entity Resolution

43 citations recorded by the source corpus.

How researchers see this paper

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Fast Learning from Distributed Datasets without Entity Matching (2016, neutral): “ER takesO((m/m)(2) ·Tsim) to match entities in each of the m blocks, where Tsim is the cost of evaluation any similarity function; learning-based methods spend additional time for training; advanced blocking strategies can reduce the average complexity [Bilenko et al., 2006; Whang et al., 2009; Whan”
A Review of Relational Machine Learning for Knowledge Graphs (2015, background): “In a relational setting, the decisions about which objects are assumed to be identical can propagate through the graph, so that matching decisions are made collectively for all objects in a domain rather than independently for each object pair (see, for example, [51]–[53]).”
Data Matching (2012, background): “When several databases are to be matched, the matches identified between records from two databases can help to inform the matching of records from other databases [276].”
Progressive Approach to Relational Entity Resolution (2014, background): “In this paper, we study the problem in the case of relational ER [6, 11, 12, 23].”
LINDA: distributed web-of-data-scale entity matching (2012, methodology): “Smart blocking techniques are often employed to reduce similarity comparisons [9, 11, 15].”