Name
Abstract | ||
|---|---|---|
Entity Resolution (ER), a core task of Data Integration, detects different entity profiles that correspond to the same real-world object. Due to its inherently quadratic complexity, a series of techniques accelerate it so that it scales to voluminous data. In this survey, we review a large number of relevant works under two different but related frameworks: Blocking and Filtering. The former restricts comparisons to entity pairs that are more likely to match, while the latter identifies quickly entity pairs that are likely to satisfy predetermined similarity thresholds. We also elaborate on hybrid approaches that combine different characteristics. For each framework we provide a comprehensive list of the relevant works, discussing them in the greater context. We conclude with the most promising directions for future work in the field.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3377455 | ACM Computing Surveys |
Keywords | DocType | Volume |
Blocking,entity resolution,filtering | Journal | 53 |
Issue | ISSN | Citations |
2 | 0360-0300 | 5 |
PageRank | References | Authors |
0.42 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| G. Papadakis | 1 | 43 | 4.69 |
| Dimitrios N. Skoutas | 2 | 6 | 1.13 |
| Emmanouil Thanos | 3 | 7 | 1.47 |
| Themis Palpanas | 4 | 1136 | 91.61 |