Debunking some “RDF vs. Property Graph” Alternative Facts
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The document provides a refresher on RDF and property graphs, comparing their models and query languages. It debunks some common misconceptions about RDF versus property graphs, noting that RDF does not impose a particular storage and can be stored in graph databases. Semantics in RDF are just optional rules that are difficult to implement effectively. The nature of the data and intended usage should be considered rather than assuming one model is inherently better for unstructured or semantic data.
![Query: Who likes a person named Ann?
Cypher
MATCH (who)-[:LIKES]->(a:Person)
WHERE a.name CONTAINS ‘Ann’
RETURN who
SPARQL
prefix ms: <http://myschma.me/>
prefix rdf: <http://www[...]#>
SELECT ?who
{
?a rdf:type ms:Person .
?a ms:name ?asName .
FILTER regex(?asName,’Ann’)
?who ms:likes ?a .
}](https://image.slidesharecdn.com/gctalk-debunking-170518091344/85/Debunking-some-RDF-vs-Property-Graph-Alternative-Facts-25-320.jpg)
![#2 The semantics of semantics
“suppose you entered the details ‘Philip owns a Mercedes’
where ‘Philip’ and ‘Mercedes’ are both entities and ‘owns’
is a relationship. An inference engine can deduce that
Mercedes in this instance is a car whereas in ‘Juan is
married to Mercedes’ it would deduce that Mercedes is a
person
[...]
contrast this with the inability of a database to understand
anything it isn’t explicitly told then you should be able to
see the potential advantages”](https://image.slidesharecdn.com/gctalk-debunking-170518091344/85/Debunking-some-RDF-vs-Property-Graph-Alternative-Facts-32-320.jpg)
![#2 The semantics of semantics
“suppose you entered the details ‘Philip owns a Mercedes’
where ‘Philip’ and ‘Mercedes’ are both entities and ‘owns’
is a relationship. An inference engine can deduce that
Mercedes in this instance is a car whereas in ‘Juan is
married to Mercedes’ it would deduce that Mercedes is a
person
[...]
contrast this with the inability of a database to understand
anything it isn’t explicitly told then you should be able to
see the potential advantages”
WRONG!!](https://image.slidesharecdn.com/gctalk-debunking-170518091344/85/Debunking-some-RDF-vs-Property-Graph-Alternative-Facts-33-320.jpg)
Debunking some “RDF vs. Property Graph” Alternative Facts
- 1. Debunking some “RDF vs. Property Graph” Alternative Facts
- 2. Dr. Jesús Barrasa Neo Technology
- 3. Quick refresher on LPG & RDF
- 4. RDF Resource Description Framework W3C recommendation model for information exchange. Feb‘99
- 5. The Semantic Web “RDF is a key tech for developing the Semantic Web”. Article on SciAm in 2001 (1) https://www.scientificamerican.com/article/the-semantic-web/
- 6. Persisted RDF Specialized RDF stores (triple/quad stores) Semantic Graph Databases
- 7. LPG Sweden (2000 - 2007) efficient (graph native) storage fast query and traversal humane model: close to the way we humans understand and reason about the world
- 9. A look at the models
- 10. GRAPH = VERTICES + EDGES
- 11. ppl://ann is a person ppl//ann user ID is @ann ppl://ann name is Ann Smith ppl://dan likes ppl://ann RDF statements (triples)
- 12. ppl://ann is a person ppl//ann user ID is @ann ppl://ann name is Ann Smith ppl://dan likes ppl://ann RDF statements (triples) ppl://an n is_a _:Person
- 13. ppl://ann is a person ppl//ann user ID is @ann ppl://ann name is Ann Smith ppl://dan likes ppl://ann RDF statements (triples) ppl://an n @ann is_a user_ID _:Person
- 14. ppl://ann is a person ppl//ann user ID is @ann ppl://ann name is Ann Smith ppl://dan likes ppl://ann RDF statements (triples) ppl://an n @ann Ann Smith is_a name _:Person user_ID
- 15. ppl://ann is a person ppl//ann user ID is @ann ppl://ann name is Ann Smith ppl://dan likes ppl://ann RDF statements (triples) ppl://an n ppl://da n @ann Ann Smith is_a name _:Person likes user_ID
- 16. ppl://ann is a person ppl//ann user ID is @ann ppl://ann name is Ann Smith ppl://dan likes ppl://ann RDF statements (triples) ppl://an n ppl://da n @ann Ann Smith is_a name _:Person likes Vertices Edges user_ID
- 17. There is a person that is described by her name: Ann, her user ID: @ann and a globally unique identifier: <ppl://ann> There is another person with a unique identifier: <ppl://dan> Dan likes Ann LPG connected objects (with properties)
- 18. There is a person that is described by her name: Ann, her user ID: @ann and a globally unique identifier: <ppl://ann> There is another person with a unique identifier: <ppl://dan> Dan likes Ann LPG connected objects (with properties) { name: Ann, app_user_ID: @ann, uri: ppl://ann } :Person
- 19. There is a person that is described by her name: Ann, her user ID: @ann and a globally unique identifier: <ppl://ann> There is another person with a unique identifier: <ppl://dan> Dan likes Ann LPG connected objects (with properties) { uri: ppl://dan} { name: Ann, app_user_ID: @ann, uri: ppl://ann } :Person:Person
- 20. There is a person that is described by her name: Ann, her user ID: @ann and a globally unique identifier: <ppl://ann> There is another person with a unique identifier: <ppl://dan> Dan likes Ann LPG connected objects (with properties) LIKES { date: 02/03/17} { uri: ppl://dan} { name: Ann, app_user_ID: @ann, uri: ppl://ann } :Person:Person
- 21. There is a person that is described by her name: Ann, her user ID: @ann and a globally unique identifier: <ppl://ann> There is another person with a unique identifier: <ppl://dan> Dan likes Ann LPG connected objects (with properties) LIKES { date: 02/03/17} { uri: ppl://dan} { name: Ann, app_user_ID: @ann, uri: ppl://ann } Vertices Edges :Person:Person
- 22. RDF Graph Vertices Every statement produces two vertices in the graph. Some are uniquely identified by URIs: Resources Some are property values: Literals Edges Every statement produces an edge. Uniquely identified by URIs Vertices or Edges have NO internal structure
- 23. RDF Graph Vertices Every statement produces two vertices in the graph. Some are uniquely identified by URIs: Resources Some are property values: Literals Edges Every statement produces an edge. Uniquely identified by URIs Vertices or Edges have NO internal structure Property Graph Vertices Unique Id + set of key-value pairs Edges Unique Id + set of key-value pairs Vertices and Edges have internal structure
- 25. Query: Who likes a person named Ann? Cypher MATCH (who)-[:LIKES]->(a:Person) WHERE a.name CONTAINS ‘Ann’ RETURN who SPARQL prefix ms: <http://myschma.me/> prefix rdf: <http://www[...]#> SELECT ?who { ?a rdf:type ms:Person . ?a ms:name ?asName . FILTER regex(?asName,’Ann’) ?who ms:likes ?a . }
- 26. End of refresher. OK so far?
- 27. #1 RDF vs Graph DBs. Apples to apples #2 the semantics of semantics #3 structured vs unstructured data
- 28. #1 RDF vs Graph DBs. Apples to apples What are we comparing? Is it models or is it stores? “RDF vs Graph DBs…” RDF Stores Native Graph DB (Neo4j) ❏ Very strongly index based (some graph DBs are also strongly index based!! Graph as a feature) ❏ RDFsed on mostly additive, typically slow changing if not immutable data sets ❏ Neo4j is Graph native ❏ Neo4j excels with highly dynamic datasets & transactional UC where data Integrity is key
- 29. #1 RDF vs Graph DBs. Apples to apples Separating the model from the storage ○ RDF does not impose any particular type of data storage ○ Micro-demo: A Turing test of RDFness(*) “RDF vs Graph DBs…” (*) https://jesusbarrasa.wordpress.com/2016/11/17/neo4j-is-your-rdf-store-part-1/
- 30. #1 Demo
- 31. #1 Demo <RDF> =
- 32. #2 The semantics of semantics “suppose you entered the details ‘Philip owns a Mercedes’ where ‘Philip’ and ‘Mercedes’ are both entities and ‘owns’ is a relationship. An inference engine can deduce that Mercedes in this instance is a car whereas in ‘Juan is married to Mercedes’ it would deduce that Mercedes is a person [...] contrast this with the inability of a database to understand anything it isn’t explicitly told then you should be able to see the potential advantages”
- 33. #2 The semantics of semantics “suppose you entered the details ‘Philip owns a Mercedes’ where ‘Philip’ and ‘Mercedes’ are both entities and ‘owns’ is a relationship. An inference engine can deduce that Mercedes in this instance is a car whereas in ‘Juan is married to Mercedes’ it would deduce that Mercedes is a person [...] contrast this with the inability of a database to understand anything it isn’t explicitly told then you should be able to see the potential advantages” WRONG!!
- 34. #2 The semantics of semantics The semantics in RDF are JUST RULES (!) These rules are called ontologies and are an optional layer on top of RDF data. Noone likes rules engines → horrible to debug / performance Reasoning with ontology languages quickly gets intractable/undecidable
- 35. #2 The semantics of semantics Yes, inference is expensive. When considering it, you should: 1) run it over as small a dataset as possible 2) use only the rules you need 3) consider alternatives to inference Wow! Thanks for the tip, vendor of “the world’s only Enterprise Triple Store” ;-)
- 36. #3 Unstructured data likes RDF ● RDF is no less structured than the LPG ● Association between NLP/EE and RDF “NLP is based on subjects, verbs and objects. There is a simple parallel between this and RDF” “RDF stores have the flexibility to accommodate unstructured data”
- 37. ● Consider both model and storage option separately ○ How do you want to represent and query your data? ○ How are you going to use your data? Analytical / operational ● What’s the nature of your data: Immutable, slow changing or dynamic and transactional? ● Do I need really need rule based inferencing? Please, try it! don’t make decisions based on “alternative facts” Takeaways: Before starting your graph project...
- 38. Thank you!