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Property graph vs. RDF Triplestore comparison in 2020

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Property graph vs. RDF Triplestore comparison in 2020

  1. 1. Property Graph vs RDF Comparison
  2. 2. Outline o Introduction o RDF Model o Property Graph Model o Model Comparison o Database Engines o Q&A
  3. 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. 4. Fundamental Types of Databases Source: NoSQL Now! NoSQL Architecture Patterns
  5. 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. 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. 7. Outline o Introduction o RDF Model o Property Graph Model o Model Comparison o Database Engines o Q&A
  8. 8. Undirected Graph 1 2 3 4 5
  9. 9. Directed Graph 1 2 3 4 5
  10. 10. Directed Labeled Graph 1 2 3 4 livesIn livesIn 5 partOf worksIn worksIn
  11. 11. knows Directed Labeled Cyclic Graph 1 4 livesIn livesIn knows 3 2 knows worksIn worksIn 5 partOf
  12. 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. 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. 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. 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. 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. 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. 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. 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. 20. Outline o Introduction o RDF Model o Property Graph Model o Model Comparison o Database Engines o Q&A
  21. 21. Directed Graph 1 2 3 4 5
  22. 22. partOf worksIn Directed Labeled Graph 1 2 3 4 5 worksIn livesIn livesIn
  23. 23. worksIn Directed Labeled Graph with Types 1 2 3 4 5 worksIn livesIn livesIn partOf
  24. 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. 25. Outline o Introduction o RDF Model o Property Graph Model o Model Comparison o Database Engines o Q&A
  26. 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. 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. 28. What is a Good Compromise?
  29. 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. 30. Outline o Introduction o RDF Model o Property Graph Model o Model Comparison o Database Engines o Q&A
  31. 31. 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
  32. 32. 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!

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