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Retrieve (and Leverage) the Inner Graph Behind the Data
Published on Nov 28, 202416 Views
Session Chair: Gianluca Demartini Many challenging data integration problems, in particular in data journalism, feature heterogeneity at the level of the schema and the data model. To overcome the
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Chapter list
Retrieve (and Leverage) the Inner Graph Behind the Data00:00
Plan00:14
Part I01:06
Why journalism?01:13
Why journalism?01:25
Why journalism?02:01
Journalists vs. the data03:17
Journalists vs. the data04:18
Journalists vs. the data04:44
Data model heterogeneity05:37
Data model heterogeneity06:25
Data model fashions07:11
Data heterogeneity: how to live with it?07:47
Data heterogeneity: how to live with it?09:00
Making sense of heterogeneous data for journalism09:36
Making sense of heterogeneous data for journalism11:49
Part II11:59
ConnectionLens: integrating data into graphs [4]12:09
Relational data conversion to a graph12:44
XML documents: mostly trees13:24
JSON documents: trees13:58
XML documents: mostly trees14:01
JSON documents: trees14:25
Property graphs14:40
Property graphs transformed in ConnectionLens15:09
Entity extraction15:59
Entity extraction16:49
Sample graph: con icts of interest in the biomedical domain [2]17:56
Sample graph: con icts of interest in the biomedical domain [2]20:36
Sample graph: con icts of interest in the biomedical domain [2]21:10
Building ConnectionLens graphs [4]21:44
Building ConnectionLens graphs [4]22:41
Keyword search in ConnectionLens graphs23:42
Integrating keyword search into a graph query language [5]27:02
Integrating keyword search into a graph query language [5]28:19
Unfortunately, journalists dont like such views of the data!28:41
Journalists prefer simpler views over ConnectionLens graphs29:00
Towards data abstraction29:56
Part III30:29
How to help journalists understand heterogeneous data30:43
Sample abstraction of an XMark XML document31:33
Abstraction of NASA RDF graph32:48
Dataset abstraction stages33:15
Data graph summarization: which groups of nodes are equivalent?33:35
Data graph summarization: which groups of nodes are equivalent?35:43
Graph summarization result: collection graph36:07
From the collection graph to entities and relationships36:49
Which collections make good entity roots?37:13
Which collections make good entity roots?39:56
Which collections make good entity roots?41:01
Classifying entities41:17
NEs and their connections are interesting42:58
Sample entity paths identi ed by PathWays44:40
Identifying interesting paths in PathWays45:11
Part IV45:55
Summary45:56
In the larger landscape47:04