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Graphing Your Data
Or,
How I Stopped Worrying and Love the Triple
Store
Agenda
● What is Linked Data
● RDF
● Virtually Mapping Tuples to Triples
Some Problems...
● How to handle data reuse?
● How consistent is the data definition/model?
● How to join data across mult...
Alternative Solutions...
● Data Governance
● Master Data Management
● Data Integration/Transformation
Alternative Problems!
● Data Governance and MDM can take years
before value is shown
● Data Transformation can be expensiv...
Semantic Web to the
Rescue
● Translate data into plain-language
● Follow standard vocabularies (and create
your own)
● Pro...
Semantic Web In Context
● Will not replace the data warehouse
– Intended for exploratory research
– Ideal for researchers ...
An Example...
An Example...
How the Data Formats
● 123 is a vocab:Student
● 456 is a vocab:Teacher
● 789 is a vocab:Class
● 123 vocab:hasFirstName Bob...
RDF/XML
Turtle
Triples
Converting Your Data
● ETL
– Many tools will support XML data conversion
● Database
– Some databases will port data into X...
Using Protégé
● Stanford ontology modeling tool
● Ontop plugin provides mapping layer
● Fastest, most agile way to map dat...
Links
● Ontop - http://ontop.inf.unibz.it/
● Protege -
http://protege.stanford.edu/products.php
● Virtuoso - http://virtuo...
Graphing Your Data
Graphing Your Data
Graphing Your Data
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Graphing Your Data

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Triple stores are finally seeing mainstream use, but what exactly is all this talk about linked data? In this deck, we discuss what the semantic web is and how to map your relational data sets into a triple store database using open source software.

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Graphing Your Data

  1. 1. Graphing Your Data Or, How I Stopped Worrying and Love the Triple Store
  2. 2. Agenda ● What is Linked Data ● RDF ● Virtually Mapping Tuples to Triples
  3. 3. Some Problems... ● How to handle data reuse? ● How consistent is the data definition/model? ● How to join data across multiple data sources?
  4. 4. Alternative Solutions... ● Data Governance ● Master Data Management ● Data Integration/Transformation
  5. 5. Alternative Problems! ● Data Governance and MDM can take years before value is shown ● Data Transformation can be expensive (time, resources) when moving to a data warehouse
  6. 6. Semantic Web to the Rescue ● Translate data into plain-language ● Follow standard vocabularies (and create your own) ● Provides data in format that is: – Secure – Human and Machine readable – Web-ready (it's the start of “Web 3.0”)
  7. 7. Semantic Web In Context ● Will not replace the data warehouse – Intended for exploratory research – Ideal for researchers and data analysts
  8. 8. An Example...
  9. 9. An Example...
  10. 10. How the Data Formats ● 123 is a vocab:Student ● 456 is a vocab:Teacher ● 789 is a vocab:Class ● 123 vocab:hasFirstName Bob ● 456 vocab:hasFirstName Sarah ● 789 vocab:isTaughtBy 456 ● 123 vocab:isRegistered 789
  11. 11. RDF/XML
  12. 12. Turtle
  13. 13. Triples
  14. 14. Converting Your Data ● ETL – Many tools will support XML data conversion ● Database – Some databases will port data into XML format ● Mapping Tool – Real-time conversion layer between RDBMS and Triples
  15. 15. Using Protégé ● Stanford ontology modeling tool ● Ontop plugin provides mapping layer ● Fastest, most agile way to map data into triples.
  16. 16. Links ● Ontop - http://ontop.inf.unibz.it/ ● Protege - http://protege.stanford.edu/products.php ● Virtuoso - http://virtuoso.openlinksw.com/ ● RDF/XML Tutorial- http://www.w3schools.com/xml/xml_rdf.asp

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