1. RDF and triplestores CMSC 461 Michael Wilson

2. Reasoning  Relational databases allow us to reason about data that is organized in a specific way  Data that models specific relationships  Data that is very cleanly structured  What other reasoning methods are available to us?

3. Metadata  “Data about data”  Data that describes other data  Gives context  Example metadata:  Image EXIT data (geolocation, rotation, etc.)  User statistics  Last saved information in a file

4. What’s so important?  The context that we gather from metadata often allows us to understand a much greater picture  Can correlate and tie metadata together  Calculate statistics on metadata  Understand trends  Infinite possibilities

5. The depth of metadata  Many systems have their own way of storing metadata  Database tables may be organized to house specific metadata  This does not lend itself well to discovering new types of metadata  Person may have age, DOB  Later want to add new types (friends, Facebook ID, Twitter ID, etc.)

6. Metadata structures  RDF  Resource Description Framework  OWL  Web Ontology Language  Ontology – established vocabulary to describe knowledge within a domain  RDF is more widely used

7. Schemas  RDF and other structured metadata formats allow us to establish a common language to describe different sorts of metadata  We can make schemas that describe  Social media  Physical location  Job details  Moreover, we can tie them all to one subject  Doesn’t require database reorganization

8. Why is that cool?  What this means is that we can tie any arbitrary sets of data together with very little work on our part  We make a schema that describes a new domain, and staple that information onto an existing subject

9. Triples  Within these schemas, data is conceptually organized as   Subject  The subject of the expression  Predicate  The relationship between the subject and object  Object  The direct object of the expression  These expressions are called “triples”

10. Triple examples  Examples?

11. Storing triples  Since we are often interesting in large amounts of data, we need to think on how to store these  Triplestores  Pretty obvious  What do these give us over doing something like storing the information in a database?

12. Triplestore querying  Triplestores can also be queried  SQL is more limited for the kinds of queries we’d like to be able to make  SPARQL  The acronym stands for:  SPARQL Protocol and RDF Query Language

13. SPARQL  SPARQL is a SQL-like query language  Allows us to query on the various schemas we have assigned to our subjects  SPARQL queries can look surprisingly readable

14. SPARQL example PREFIX abc: SELECT ?capital ?country WHERE { ?x abc:cityname ?capital ; abc:isCapitalOf ?y. ?y abc:countryname ?country ; abc:isInContinent abc:Africa.

15. Querying power  Using SPARQL, you can make extremely deep, powerful queries and reason very intuitively on the data present in a triplestore  Organizing data this way allows computers to actually be able to reason on data as well

16. Caveats  All this tech is SUPER new  All tied very heavily into the Semantic Web  Basically introduce a system like this into the web at large  Metadata stored about web pages, computers can reason about them  Much of this is a moving target  Not a whole lot of production applications using this stuff yet

17. Tools  There are a few triplestore servers and other tools you can use  Jena  Apache project  Framework that allows for Semantic Web concepts to be employed  Can query using SPARQL  Jena can use Postgres in the background

18. More tools  RDFLib  https://github.com/RDFLib https://github.com/RDFLib  Python library for RDF  Can run entirely in memory  Good for experimentation purposes and more