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To make their data usable, Linked Data producers need to provide a minimum level of homogeneity. But the task of finding the missing attributes for a specific list of entities is notoriously difficult to complete. We make the hypothesis that identifying subsets of items with a similar structure can help finding the cause of incomplete- ness and decide if and how it could to be solved. Our visualisation tool “The Missing Path”, relies on dimensional reduction techniques to create a map of the entities based on missing attributes, revealing clusters. It let users inspect these clusters for diagnosing the reasons for incompleteness.
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To make their data usable, Linked Data producers need to provide a minimum level of homogeneity. But the task of finding the missing attributes for a specific list of entities is notoriously difficult to complete.
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|
|
|
|
We make the hypothesis that identifying subsets of items with a similar structure can help finding the cause of incomplete- ness and decide if and how it could to be solved.
|
|
|
|
|
|
Our visualisation tool “The Missing Path”, relies on dimensional reduction techniques to create a map of the entities based on missing attributes, revealing clusters. It let users inspect these clusters for diagnosing the reasons for incompleteness.
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* Overview
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* Selecting entities
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