Collective entity resolution in relational data

By Indrajit Bhattacharya, Lise Getoor. 2007

703 citations recorded by the source corpus.

Subfields: Information Systems, Management Science and Operations Research, Natural Language Processing, Evaluation & Benchmarks, Signal Processing

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Citations in the corpus

SIGMa: simple greedy matching for aligning large knowledge bases (2012, methodology): “and Getoor [3], solved using a greedy agglomerative clustering algorithm.”
Applications of Topic Models (2017, methodology): “used as the concepts [Bhattacharya, 2006] in the prior tree (Figure 8.”
Quantitative Data Cleaning for Large Databases (2008, background): “, [Singla and Domingos, 2006], [Bhattacharya and Getoor, 2007], [Dong et al.”
OAG: Toward Linking Large-scale Heterogeneous Entity Graphs (2019, background): “The problem of entity linking is related to entity matching [20, 25, 36], entity resolution [2, 3], web appearance disambiguation [1, 11], name identification [16], object distinction [37], and name disambiguation [8, 30, 40], and has been extensively studied for decades by different communities.”
A taxonomy of privacy-preserving record linkage techniques (2013, background): “Alternatively, collective entity resolution techniques [35,36] can be employed that analyze not only attribute values of records but also relationships between records to determine the match status of pairs or groups of records.”
A Survey of Statistical Network Models (2009, background): “The related task of discovering the “roles” of individual nodes is useful for identity disambiguation [36] and for business organization analysis [207].”
Generalizable Adversarial Attacks Using Generative Models (2019, methodology): “We consider two standard node classiﬁcation datasets, Cora and CiteSeer [20, 4, 31].”
Data Matching (2012, methodology): “These approaches apply relational clustering or graph-based techniques [31, 155, 272] to generate a global decision model.”
Research directions in data wrangling: Visualizations and transformations for usable and credible data (2011, neutral): “2011–6:17pm] [1–18] K:/IVI/IVI 415994.”
Trends in Cleaning Relational Data: Consistency and Deduplication (2015, background): “Bhattacharya and Getoor [13] model collective entity resolution as a clustering problem, and encodes the additional relational information in the similarity computation between two clusters.”
Large-Scale Collective Entity Matching (2011, background): “As their name suggests, iterative approaches [4, 7] iterate over”
When Social Influence Meets Item Inference (2015, methodology): “cross multiple OSNs. Account matching across multiple OSNs plays a crucial role to discover the user behaviors in multiple OSNs. Existing schemes achieve good performance by employing link prediction [10, 17, 18, 30], which assumes that similar nodes belong to the same user and have a high potential”
Progressive Approach to Relational Entity Resolution (2014, background): “Prior research [6, 11, 12] has shown that such decision propagation via relationships can significantly improve data quality in datasets where such relationships can be specified.”
PARIS: Probabilistic Alignment of Relations, Instances, and Schema (2011, methodology): “There have been many works on entity resolution, i.e., on what is traditionally known as the “A-Box” [1, 4, 12, 17, 18,25,27,28,31].”
Large-Scale Deduplication with Constraints Using Dedupalog (2009, positive): “Finally, we conclude with the important physical optimizations (execution strategies) that allow our algorithms to scale to large datasets.”
A Survey of Fake News (2020, methodology): “…as entity resolution [Getoor and Machanavajjhala 2012], which is also known as deduplication or record linkage [Nickel et al. 2016] (e.g., see related studies such as Altowim et al. [2014], Bhattacharya and Getoor [2007], Christen [2008], Stoerts et al. [2016], and Whang and Garcia-Molina [2012]).”
Joint Entity Resolution (2012, methodology): “Among the existing works on joint ER [1], [2], [3], [4], [5], [6], few have focused on scalability, which is crucial in resolving large data (e.”