Name
Abstract | ||
|---|---|---|
Entity Resolution is a core task for merging data collections. Due to its quadratic complexity, it typically scales to large volumes of data through blocking: similar entities are clustered into blocks and pair-wise comparisons are executed only between co-occurring entities, at the cost of some missed matches. There are numerous blocking methods, and the aim of this work is to offer a comprehensive empirical survey, extending the dimensions of comparison beyond what is commonly available in the literature. We consider 17 state-of-the-art blocking methods and use 6 popular real datasets to examine the robustness of their internal configurations and their relative balance between effectiveness and time efficiency. We also investigate their scalability over a corpus of 7 established synthetic datasets that range from 10,000 to 2 million entities. |
Year | Venue | Field |
|---|---|---|
2016 | PROCEEDINGS OF THE VLDB ENDOWMENT | Data mining,Quadratic complexity,Name resolution,Computer science,Robustness (computer science),Blocking techniques,Artificial intelligence,Empirical survey,Merge (version control),Machine learning,Scalability |
DocType | Volume | Issue |
Journal | 9 | 9 |
ISSN | Citations | PageRank |
2150-8097 | 21 | 0.71 |
References | Authors | |
24 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| George Papadakis | 1 | 58 | 4.27 |
| Jonathan Svirsky | 2 | 21 | 0.71 |
| Avigdor Gal | 3 | 23 | 2.45 |
| Themis Palpanas | 4 | 1136 | 91.61 |