Collaboratively Patching Linked Data
نویسندگان
چکیده
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 is often 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 become 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 in the example of a collaborative game that patches statements in DBpedia data and provides notifications for relevant changes.
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عنوان ژورنال:
- CoRR
دوره abs/1204.2715 شماره
صفحات -
تاریخ انتشار 2012