Reference reconciliation in complex information spaces

By Xin Luna Dong, Alon Y. Halevy, Jayant Madhavan. ACM SIGMOD Conference, 2005

654 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/d5f1bf73988ade49a6358afa12b36041118bb042

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In Artificial Intelligence

In Management Science and Operations Research

Citations in the corpus

Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions (2015, methodology): “[38] proposed a novel record linkage algorithm based on a general framework for propagating information from one linkage deci-”
Data Integration for the Relational Web (2009, background): “Second, it must solve a reference reconciliation problem [8] to ensure that values in the join column c match up if they represent the same real-world object, even if the string representation diﬀers ( e.g. , realizing that Alon Halevy and Alon Levy as the same person but both are diﬀerent from…”
Quantitative Data Cleaning for Large Databases (2008, background): “, [Singla and Domingos, 2006], [Bhattacharya and Getoor, 2007], [Dong et al., 2005]).”
Entity resolution with iterative blocking (2009, background): “, most previous works [7, 11, 17] assume that their matching is precise and run a transitive closure on all the matching pairs of records to produce an ER result).”
Ontology-based information extraction: An introduction and a survey of current approaches (2010, background): “One such problem is reference reconciliation (object reconciliation), which refers to the task of determining whether two instances are one and the same [69].”
Large-Scale Deduplication with Constraints Using Dedupalog (2009, methodology): “[48] present a deduplication algorithm for personal information management.”
Evaluation of entity resolution approaches on real-world match problems (2010, methodology): “See, for example, the differently sized Cora-based datasets used in [25], [30], [13], and [12].”
Data Matching (2012, methodology): “These techniques are known as ‘collective entity resolution’ techniques, and they employ either iterative or hierarchical clustering [31, 181], or graph-based approaches [93, 155, 195].”
ZenCrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking (2012, background): “Reference Reconciliation [14], for example, builds a dependency graph and exploits relations to propagate information among entities.”
QuERy: A Framework for Integrating Entity Resolution with Query Processing (2015, methodology): “In this paper, we focus on the Entity Resolution (ER) challenge [6,10], the task of which is to discover duplicate entities that refer to the same real-world object and then to group them into a single cluster that uniquely represents that object.”
Large-Scale Collective Entity Matching (2011, background): “As their name suggests, iterative approaches [4, 7] iterate over”
Web-Scale Data Integration: You can afford to Pay as You Go (2007, methodology): “…we employ techniques for discovering addi-348 tional relationships between data in different sources (in the spirit of Semex [12], iMeMex [5], and DBLife [9]), techniques for rec-onciling references to the same real-world objects (e.g., [13]), and techniques for information extraction from text.”
A Latent Dirichlet Model for Unsupervised Entity Resolution (2005, methodology): “Recently, generative [22, 29] and discriminative [24, 28] probabilistic approaches have been proposed as well as non-probabilistic algorithms [20, 12].”
Adaptive Blocking: Learning to Scale Up Record Linkage (2006, background): “The problem of identifying coreferent records has been studied in several research communities under different names that include record linkage [12, 36], the merge/purge problem [36], duplicate detection [26, 31, 4], reference/citation matching [24, 20], object identification [35, 34], entity name ”
Frameworks for entity matching: A comparison (2010, background): “, semantic relationships, hierarchies) in a graph structure, see for example [16,34,7,6,15,21,22,51].”