Title
Collaboratively Patching Linked Data
Abstract
Today's Web of Data is noisy. Linked Data often needs extensive preprocessing to enable efficient use of heterogeneous resources. While consistent and valid data provides the key to efficient data processing and aggregation we are facing two main challenges: (1st) Identification of erroneous facts and tracking their origins in dynamically connected datasets is a difficult task, and (2nd) efforts in the curation of deficient facts in Linked Data are exchanged rather rarely. Since erroneous data often is duplicated and (re-)distributed by mashup applications it is not only the responsibility of a few original publishers to keep their data tidy, but progresses to be a mission for all distributers and consumers of Linked Data too. We present a new approach to expose and to reuse patches on erroneous data to enhance and to add quality information to the Web of Data. The feasibility of our approach is demonstrated by example of a collaborative game that patches statements in DBpedia data and provides notifications for relevant changes.
Year
Venue
Field
2012
CoRR
Data mining,Mashup,Data processing,World Wide Web,Information retrieval,Computer science,Reuse,Semantic Web,Linked data,Preprocessor
DocType
Volume
Citations 
Journal
abs/1204.2715
9
PageRank 
References 
Authors
0.95
8
3
Name
Order
Citations
PageRank
Magnus Knuth115314.29
Johannes Hercher2283.76
Harald Sack355572.65