.png){kind=icon}
.png){kind=icon}
Return
Benchmarking Blocking Algorithms for Web Entities
.png){kind=icon}
OA
AI
DOI：10.1109/TBDATA.2016.2576463.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
An increasing number of entities are described by interlinked data rather than documents on the Web. Entity Resolution (ER) aims to identify descriptions of the same real-world entity within one or across knowledge bases in the Web of data. To reduce the required number of pairwise comparisons among descriptions, ER methods typically perform a pre-processing step, called blocking, which places similar entity descriptions into blocks and thus only compare descriptions within the same block. We experimentally evaluate several blocking methods proposed for the Web of data using real datasets, whose characteristics significantly impact their effectiveness and efficiency. The proposed experimental evaluation framework allows us to better understand the characteristics of the missed matching entity descriptions and contrast them with ground truth obtained from different kinds of relatedness links.
Keywords:
Erbium
Big data
Poles and towers
Semantics
Knowledge engineering
Knowledge based systems
Information retrieval
Blocking
entity resolution
web of data
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
5.7
Papers:
860
Citations:
3.0K