This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020

1. Property Graph vs RDF
Comparison

2. Outline
o Introduction
o RDF Model
o Property Graph Model
o Model Comparison
o Database Engines
o Q&A

3. Why Different Databases?
Database management systems are about series of
compromises in terms of performance, complexity, query style,
data types, scalability, transactions, consistency, etc.
There is no database management system that meets all needs.

4. Fundamental Types of Databases
Source: NoSQL Now! NoSQL Architecture Patterns

5. Gartner Data Store Spectrum
Source: An Introduction to Graph Data Stores and Applicable Use Cases.
Gartner report published: 24 January 2019 ID: G00361957, Analyst(s):
Sumit Pal

6. What is a Graph?
The graph in mathematics is not a chart,
but a data structure that represents a network of nodes and edges.
1
2
3
4 5

7. Outline
o Introduction
o RDF Model
o Property Graph Model
o Model Comparison
o Database Engines
o Q&A

8. Undirected Graph
1
2
3
4 5

9. Directed Graph
1
2
3
4 5

10. Directed Labeled Graph
1
2
3
4
livesIn
livesIn
5
partOf
worksIn
worksIn

11. knows
Directed Labeled Cyclic Graph
1 4
livesIn
livesIn
knows
3
2
knows
worksIn
worksIn
5
partOf

12. Directed Labeled Cyclic Multigraph
1 4
3
2
knows {g1}
worksIn {g1}
livesIn {g2}
livesIn {g2}
knows {g1}
knows {g1}
next {g2}
worksIn {g1}
5
partOf {g2}

13. Directed Labeled Cyclic Multigraph with Node
Labels
1 4
3
2
knows {g1}
worksIn {g1}
livesIn {g2}
livesIn {g2}
knows {g1}
knows {g1}
next {g2}
worksIn {g1}
Bulgaria
A. Kiryakov
Sofia
Ontotext
V. Momtchev
5
partOf {g2}

14. Directed Labeled Cyclic Multigraph with Node
Labels and Types
1 4 5
3
2
knows {g1}
livesIn {g2}
knows {g1}
knows {g1}
next {g2}
67 8
type
type
type
livesIn {g2}
Bulgaria
A. Kiryakov
type
worksIn {g1}
type
worksIn {g1}
partOf {g2}
Person
Place
Ontotext
V. Momtchev
Organization
Sofia

15. Directed Labeled Cyclic Multigraph with Node
Labels, Types and Logic
1 4 5
3
2
livesIn {inf}
livesIn {inf}
knows {g1}
worksIn {g1}
livesIn {g2}
knows {g1}
knows {g1}
next {g2}
167
type
8
type
type
partOf {g2}
livesIn {g2}
Bulgaria
A. Kiryakov
Sofia
Person
Place
Ontotext
V. Momtchev
Organization
worksIn {g1}
type
type

16. partOf {g2}
RDF: Directed Labeled Cyclic Multigraph with
Labels, Types, Logic and Semantics
1 4 5
3
2
Edges IDs:
1 - http://ontotext.com
2 - https://www.linkedin.com/in/atanas-kiryakov
3 - https://www.linkedin.com/in/vassil-momtchev
4 - https://en.wikipedia.org/wiki/Sofia
5 - https://en.wikipedia.org/wiki/Bulgaria
Optimized for:
o Flexible web model
o Multiple versions of the truth
o Global identifiers
o Information schema language
o Logic inference and data quality
Bulgaria
Ontotext
A. Kiryakov
V. Momtchev
Sofia
livesIn {inf}
livesIn {inf}
worksIn {g1}
knows {g1}
worksIn {g1}
livesIn {g2}
livesIn {g2}
knows {g1}
knows {g1}
next {g2}

17. What is Semantics?
o Formal semantics allows new valid
facts to be inferred
o Both data and schema can be interpreted
o Semantic schema = ontology
o Languages: RDF Schema (RDFS), OWL
o Only the relevant semantics is
formalized in the schema
o The meaning of relativeOf is not fully
described by defining it as
owl:SymmetricProperty
o The best model is the simplest one that can do
the work. But not simpler!

18. What is Semantics Good For?
o Schema alignment and easy querying in diverse datasets
o Across sources, similar relationships can be modeled in a different way - one can use
parentOf, another - childOf and a third one - only the more general relativeOf
o The database will return Ivan as a result of the query (Maria relativeOf ?x) when the
fact derived from the source and asserted is(Ivan childOf Maria)
o Identifying meaning by reusing identifiers for Types and Instances
o Schema.org defines a large number of popular entities and related metadata
o LinkedIn URL is the central professional network
o Making it easier to query for multi-hop relationships
o Consistency checking and quality validation
o RDF Shapes ensure graph consistency and quality

19. RDF* and SPARQL* allow Edge Descriptions
● Statements about
statements
○ Allows multiple level of nesting
○ Backward compatible
○ Much more expressive than the
properties (key-value pairs) in PG
● RDF* is included in the
upcoming RDF 1.2 standard

20. Outline
o Introduction
o RDF Model
o Property Graph Model
o Model Comparison
o Database Engines
o Q&A

21. Directed Graph
1
2
3
4 5

22. partOf
worksIn
Directed Labeled Graph
1
2
3
4 5
worksIn
livesIn
livesIn

23. worksIn
Directed Labeled Graph with Types
1
2
3
4 5
worksIn
livesIn
livesIn
partOf

24. Directed Property Labeled Graph with Types
1
2
3
4 5
Id: 1,
name: “Ontotext”,
employees: 75
Id: 2,
name: “A. Kiryakov”,
height: 180
Id:3,
name: “V. Momtchev”,
height: 185
Id:4,
name: “Sofia”
population: 1.5M
id:5,
name: “Bulgaria”,
size: “110 km2”
from:2001/01/01
from:2005/05/07
worksIn
worksIn
livesIn
livesIn
partOf

25. Outline
o Introduction
o RDF Model
o Property Graph Model
o Model Comparison
o Database Engines
o Q&A

26. RDF vs. Property Graph (PG) Data Model
Feature RDF Property Graph
Expressivity Arbitrary complex descriptions via links to
other nodes; no properties on edges
With RDF* the model gets much more
expressive than PG
Limited expressivity, beyond the basic
directed cyclic labeled graph
Properties (key-value pairs) for nodes and
edges balance between complexity and utility
Formal semantics Yes, standard schema and model
semantics foster data reuse and inference
No formal model representation
Standardization Driven by W3C working groups and
standardization processes
Different competing vendors
Query language SPARQL specifications: Query Language,
Updates, Federation, Protocol (end-point)...
Cypher, PGQL, GCore, GQL (no standard)
Serialization format Multiple serialization formats No serialization format
Schema language RDFS, OWL, Shapes None

27. RDF vs. Property Graph Data Model (ctd)
Feature RDF Property Graph
Designed for Linked Open Data (Semantic Web):
Publishing and linking data with formal
semantics and no central control
Graph representation for analytics
Processing
Strengths
Set analysis operations (as in SQL, but with
schema abstraction and flexibility)
Graph traversal
Plenty of graph analytics and ML libraries
Data
Management
Strengths
Interoperability via global identifiers
Interoperability via a standard: schema
language, protocol for federation, reasoning
semantics
Data validation, data type support,
multilinguality
Compact serialization, shorter learning curve,
functional graph traversal language (Gremlin)
Main use cases Data-driven architecture
Master/reference data sharing in enterprises
Кnowledge representation
Data integration
Metadata management
Graph analytics and path search

28. What is a Good Compromise?

29. Gartner: Knowledge Graphs are Built with RDF
An Introduction to Graph Data Stores and Applicable Use Cases
Gartner report published: 24 January 2019 ID: G00361957, Analyst(s): Sumit Pal
Quotations:
o KGs are built on a graph data store with an RDF-based data model.
o KGs encompass both explicit and inferred relationships with the
connected data. Knowledge graphs can be used to query
complicated questions and obtain comprehensible, actionable
answers, including logical reasoning, machine learning and rules
management.

30. Outline
o Introduction
o RDF Model
o Property Graph Model
o Model Comparison
o Database Engines
o Q&A

32. Graph Database Market Update 2020 (Bloor)
…, the market leaders in this space
continue to be Neo4J and Ontotext
(GraphDB), which are graph and RDF
database providers respectively. These
are the longest established vendors in
this space (both founded in 2000) so
they have a longevity and experience
that other suppliers cannot yet match.
Bloor Research
Graph Database Market Update 2020

33. Get your GraphDB Today:
https://ontotext.com/products/graphdb/
FactForge: Hub for open data and news about People and Organizations
http://factforge.net/
Experience the technology with NOW: Semantic News Portal
http://now.ontotext.com
Thank you
for your attention!