Enterprise systems are increasingly complex, often requiring data and software components to be accessed and maintained by different company departments. This complexity often becomes an organization’s biggest challenge as changing data fields and adding new applications rapidly grow to meet business demands for increased customer insights. These slides are from a Webinar discussing how using SHACL and JSON-LD with AllegroGraph helps our customers simplify the complexity of enterprise systems through the ability to loosely combine independent elements, while allowing the overall system to function smoothly. In this Webinar we will demonstrate how AllegroGraph’s SHACL validation engine confirms whether JSON-LD data is conforming to the desired requirements. We will describe how SHACL provides a way for a Data Graph to specify the Shapes Graph that should be used for validation and describes how a given shape is linked to targets in the data. The recording is at youtube.com/allegrograph

1. JSON-LD and SHACL
for Knowledge Graphs
Dr. Jans Aasman
(allegrograph.com)

2. Contents
• Knowledge Graphs are getting popular very fast, are you
building a Knowledge Graph already?
• JSON-LD will help you add and delete objects to a
Knowledge Graph as easy as MongoDB
• SHACL will help you validate your data in the Knowledge
Graph.

3. Knowledge Graphs on the rise
• Line one

4. All the big ones in the US heavily investing in it
• Good luck trying to find a definition on the web that is not ideology or
vendor based or very application specific

5. Many technologies make a
Semantic Knowledge Graph

6. Documents: JSON, JSON-LD Graphs: RDF, Quads, Properties
Storage: Triple Attributes, Security Filters, Compression, Indexing, Full-text
Transactions: “Real” ACID, 2 Phase Commit
Management: Security, Multi-Master Replication, Backup/Restore, Warm Failover
Stored Procs:
JavaScript
Lisp
Prolog
SPARQL
Magic Predicates
Reasoning:
RDFS++
OWL2-RL
Prolog
Probabilistic
NLP:
Taxonomies
Entity Extract
Text Classify
Sentiment
Machine
Learning
ETL:
RDBMS
CSV
TEXT
NoSQL
Events:
Geospatial
Temporal
Social
REST GUI: GRUFF/AGWebView
Java Python Lisp
Built-In Integrations
Cloud:
Amazon AWS
Microsoft Azure
Data Science:
Anaconda
R Studio
Knowledge:
Linked Open Data
Editors:
Ontology, Taxonomy
NoSQL:
Cloudera, MongoDB,
Solr
Containers:
Docker, VMWare
Massively Parallel - Federation and Sharding
OSS Clients
SPARQL Prolog
Don’t worry, it is all easily accessible in
AllegroGraph Architecture

7. Successful Knowledge Graphs built on
• SKOS Taxonomies & OWL Ontologies
• RDF based Semantic Graph Technologies:
• Based on the uniqueness principle: one Thing, one URL
• If you don’t have that, you don’t have anything
• Property graphs destined to reinvent semantics
• But:
• Your User Experience and Application developers don’t want to learn
that entire stack

8. Challenge # 1 for UI and application developers:
How do you make it easy to
• Add data to a knowledge graph
• Retrieve data from a knowledge graph
• Validate your data

9. Solution:
• Knowledge Graphs are getting popular very fast, are you
building a Knowledge Graph already?
• JSON-LD will help you add, retrieve and delete objects to a
Knowledge Graph as easy as MongoDB
• SHACL will help you validate your data in the Knowledge
Graph.

10. JSON Won
• Messaging:
• the lingua franca for messaging and data exchange
• Configuration:
• JSON is replacing XML for configuration of nearly
anything
• Document and key/value store:
• JSON is the main data format stored in Document Stores
(Couchbase, Mongo, etc…)

11. JSON – the good
• Simple standard:
• Json.org spec is 5 pages, XML spec on W3C = 60 pages J
• only a few datatypes and with arrays!
• you can make your own complex data types if you want
• Easy to read and parse by humans and machines
• Easy to store in document stores
• Easy to program: support in every programming language

12. JSON (and JSON stores) – the bad
• No standards Schema (but close!)
• How do I know that the data I received is good, how do I know that
the data I’m going to send to my document store is good?
• No Semantics for attributes
• What does that attribute mean?
• Not set up for linking data
• How do I express linkage between JSON objects?
• No joins or graph search in document stores
• There simply is no concept of a relations between objects
• Client side joins or awkward procedures in javascript in the DB

13. JSON-LD = 100 % JSON +
• Add basic schema support to JSON: (but SHACL more complete)
• Add semantics to JSON objects: what does this attribute mean
• Designed to link JSON objects together
• Enables joins and graph search in document stores

14. Learn from JSON-LD.ORG

15. Google for
allegrograph python tutorial jsonld

16. It is everywhere: let’s look at this product

17. Search for @context in the source

18. JSON alone would lead to confusion, JSON-LD and
SCHEMA.ORG to the rescue
NO Meaning
WITH Meaning

20. Demo JSON-LD in Python
• Based on crunch base data from early 2000 till 2014
• Core objects: Investments, acquisitions, investors, companies
• For developers: how can you implement basic CRUD with AllegroGraph
JSONLD
• You can add and retrieve Python dictionaries directly
• Like many other document databases
• Objects are indexed with triples but can also be stored as blobs
• You can retrieve parts of objects in a SPARQL queries
• And you can retrieve as dictionaries.

21. And now SHACL

22. Semantic graphs allow you to be very ‘wild’
with your data
• Triples can be added without any schema definition
• Sometimes too flexible for the enterprise
• So the most asked question the last two years:
• Ummm, do you guys support SHACL validation?

23. SHACL seems to be replacing OWL
• Easier to read
• Less complicated
• OWL can still be derived automatically from SHACL
• Great tutorials on the web.

24. title
• Line one

25. SHACL
• Is data modeling language developed by a W3C Working Group.
• describes the “shapes” of the data so that applications can take better advantage
of that data.
• describes which properties go with which classes (like OWL)
• defines constraints on data with standardized models instead of procedural code.
• Has several built-in types of constraints such as cardinality
(minCount/maxCount), value type and allowed values, but it is also possible to
define more complex kinds of constraints for almost arbitrary validation conditions
• SHACL validation tools can verify whether your data fulfills the constraints
described by your data model, similar to how XML Schema or JSON Schema are
being used.
Derived from https://www.topquadrant.com/technology/shacl/tutorial/

26. Call SHACL validate from the command line

27. Deep integration with SPARQL

28. Conclusion: JSON-LD and SHACL
for Knowledge Graphs
• Make life easier for User Experience and Application
Developers that need to work with Knowledge Graphs.
• JSON-LD hides complexity of semantics and graphs
• SHACL easy way to validate new data.

29. Thank you