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Collecting, Integrating, Enriching and Republishing Open City Data as Linked Data
Published on Nov 10, 20151527 Views
Access to high quality and recent data is crucial both for decision makers in cities as well as for the public. Likewise, infrastructure providers could offer more tailored solutions to cities based
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Chapter list
Collecting, integrating, enriching and republishing open city data as linked data00:00
Which city is the best? Compare cities!00:12
What we have: European Green City Index00:20
Use data to compare cities00:55
Integrated Open Data is very sparse03:12
How can we fill in missing values?03:20
Use domain knowledge to predict missing values03:28
Use machine learning to predict missing values04:14
Approach 1: Complete subset regression04:53
Approach 1: How many predictors needed?05:34
Approach 2: Principal component regression06:35
Approach 2: How many predictors needed?07:34
Cross-dataset prediction 1/2: (How) can this be used for cross-dataset prediction? - 108:06
Cross-dataset prediction 1/2: (How) can this be used for cross-dataset prediction? - 208:50
Cross-dataset prediction 1/2: (How) can this be used for cross-dataset prediction? - 310:20
Cross-dataset prediction 2/2: Pairwise Linear regression can be used to "learn ontology mappings" from values10:31
Conclusion on various things we tried11:41
Now what's Semantic Web/Linked Data here?13:35
Current and future work14:09