Paper Info
Title
From Data Fusion to Knowledge Fusion.
Abstract
The task of data fusion is to identify the true values of data items (e.g., the true date of birth for Tom Cruise) among multiple observed values drawn from different sources (e.g., Web sites) of varying (and unknown) reliability. A recent survey [20] has provided a detailed comparison of various fusion methods on Deep Web data. In this paper, we study the applicability and limitations of different fusion techniques on a more challenging problem: knowledge fusion. Knowledge fusion identifies true subject-predicate-object triples extracted by multiple information extractors from multiple information sources. These extractors perform the tasks of entity linkage and schema alignment, thus introducing an additional source of noise that is quite different from that traditionally considered in the data fusion literature, which only focuses on factual errors in the original sources. We adapt state-of-the-art data fusion techniques and apply them to a knowledge base with 1.6B unique knowledge triples extracted by 12 extractors from over 1B Web pages, which is three orders of magnitude larger than the data sets used in previous data fusion papers. We show great promise of the data fusion approaches in solving the knowledge fusion problem, and suggest interesting research directions through a detailed error analysis of the methods.
Year
DOI
Venue
2014
10.14778/2732951.2732962
very large data bases
DocType
Volume
Issue
Journal
abs/1503.00302
10
ISSN
Citations 
PageRank 
2150-8097
62
1.58
References 
Authors
30
7
Name
Order
Citations
PageRank
Xin Luna Dong12524129.18
Evgeniy Gabrilovich24573224.48
geremy heitz3107652.33
Wilko Horn454914.20
Michael Kuperberg57589529.66
Shaohua Sun662216.73
Wei Zhang722611.96