Building an Enterprise Knowledge Graph

1. Graph Gurus Episode 1
Building An Enterprise Knowledge Graph
with Resource Description Framework (RDF) and Graph Database

2. © 2018 TigerGraph. All Rights Reserved
Welcome
● Attendees are muted but you can talk to us via Chat in Zoom
● We will have 10 min for Q&A at the end
● Send questions at any time using the Q&A tab in the Zoom menu
● The webinar will be recorded
● A link to the presentation and reproducible steps will be emailed
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Developer Edition Download https://www.tigergraph.com/developer/

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Speaking Today
● BS in Electrical Engineering and Computer
Science from UC Berkeley
● MS in Electrical Engineering from Stanford
University
● PhD in Computer Science from Kent State
University focused on graph data mining
● 15+ years in tech industry
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Victor Lee, Director of Product Management

4. © 2018 TigerGraph. All Rights Reserved
Speaking Today
● Master in Computer Science from
Kent State University.
● Ph.D. Candidate from Kent State
University.
● With TigerGraph for 4 years,
co-author of GSQL query language.
Developed production solutions for
major customers.
● Expertise in Graph Algorithms, Big
Data Analytic, Anti-Fraud and AML.
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Xinyu Chang, Solutions Engineering Team Leader
Photo?

5. © 2018 TigerGraph. All Rights Reserved
Agenda
● Welcome to Graph Gurus!
● Episode 1: Building an Enterprise Knowledge
Graph from an RDF Dataset
● Q&A
5
Developer Edition Download https://www.tigergraph.com/developer/

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● Topics: How to solve many important data-centric problems
using the right graph and right queries.
● Goal: To make you graph-smart!
● Format: Series of short webinars
○ Aimed at Developers and Data Scientists
○ Presented by Developers
○ Code & data used in demos available for download
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7. © 2018 TigerGraph. All Rights Reserved
Episode 1: Building an Enterprise Knowledge
Graph from RDF
Knowledge Graph
● Network of interconnected
entities in a given domain
● Semantic implications for
entities and relationships
Enterprise Knowledge Graph
● Domain is an Enterprise
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Why use TigerGraph (a Property Graph) for an
RDF-based Knowledge Graph?
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● Motivation
○ RDF stores usually use relational join to handle graph traversal
-- too slow
○ RDF/SparQL not designed for fine-controlled graph analytics --
hard to use
● Takeaways from this episode
○ Generic property graph schema applicable to any RDF data set
○ GSQL multiple-hop queries with real-time response speed and
intuitive visualization out of the box

9. © 2018 TigerGraph. All Rights Reserved
Getting Value from a Knowledge Graph
● I’ve got my data in a knowledge graph.
Now what?
Query the graph to obtain information:
● Search: Are there any ____ ?
● Reasoning: If THIS and THAT, then __
● Discover trends and norms
● Discover outliers
● Explore relationships to see what you
find...
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Demonstration
1. Example Data Set
2. Defining a Generic Knowledge Graph Schema
3. Mapping the RDF Data to the Graph Database
4. Extracting Knowledge from the Graph
10
Developer Edition Download https://www.tigergraph.com/developer/

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1. Example Data Set - DBpedia
https://wiki.dbpedia.org/
● Structured content extracted from the information created in
various Wikimedia projects.
● April 2016 Data Set
○ https://wiki.dbpedia.org/dbpedia-version-2016-04
○ Size: 150 GB
○ 9.5 billion pieces of information (RDF triples)
○ 6.0M entities of which 4.6M have abstracts, 1.53M have geo coordinates and 1.6M depictions
○ Download: http://fragments.dbpedia.org/hdt/dbpedia2016-04en.hdt
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Data Format - RDF
● RDF is an abstract data model with several serializations.
● Our DBpedia data is in n-triple format:
<subject> <predicate> <object> .
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<http://0-access.newspaperarchive.com.topcat.switchinc.org/Viewer.aspx?img=7578853> <http://dbpedia.org/property/accessdate>
"2010-04-21"^^<http://www.w3.org/2001/XMLSchema#date> .
<http://0-access.newspaperarchive.com.topcat.switchinc.org/Viewer.aspx?img=7578853> <http://dbpedia.org/property/date>
"1937-01-04"^^<http://www.w3.org/2001/XMLSchema#date> .
<http://0-access.newspaperarchive.com.topcat.switchinc.org/Viewer.aspx?img=7578853> <http://dbpedia.org/property/format>
"PDF" .
<http://0-access.newspaperarchive.com.topcat.switchinc.org/Viewer.aspx?img=7578853> <http://dbpedia.org/property/isCitedBy>
<http://dbpedia.org/resource/List_of_Attorneys_General_of_
Subject Predicate
Object

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2. Defining A Knowledge Graph Schema
Traditional approach
● Numerous vertex & edge types
○ predicate value ←→ edge type
○ “is_a_” predicate ← → vertex type
● Pro: Clear semantics
● Con: Schema can grow with every
new data triple
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A Universal Knowledge Graph Schema
Alternate approach: Universal Schema
● Only 2 vertex types and 3 edge types
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<http://0-access.newspaperarchive.com.topcat.switchinc.org/Viewer.aspx?img=7578853> <http://dbpedia.org/property/isCitedBy>
<http://dbpedia.org/resource/List_of_Attorneys_General_of_
“isCitedBy”
<http://0-acce
ss.newspaper
archive.com.t
opcat.switchin
c.org/Viewer.a
spx?img=7578
853>
Subject
<http://dbpedi
a.org/resource
/List_of_Attorn
eys_General_o
f_
Object
isCitedBy
Predicate
SubHasPred ObjHasPred
● Example: 1 Data line → up to 3 vertices and 3 edges
Each vertex will serve as an
index node, including the
Predicate vertex, speeding
up searches.
3. Mapping RDF Data to the Graph Database

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Predicate Semantics in the Universal Schema
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“isCitedBy”
<http://0-acce
ss.newspaper
archive.com.t
opcat.switchin
c.org/Viewer.a
spx?img=7578
853>
Subject
<http://dbpedi
a.org/resource
/List_of_Attorn
eys_General_o
f_
Object
isCitedBy
Predicate
SubHasPred ObjHasPred
Searching from Predicate to
Subject accesses all the
subjects that have a specific
predicate
(Country, date…)
Searching from Predicate to
Object accesses all the objects
associated with a specific
predicate
(Country, date…)
<http://0-access.newspaperarchive.com.topcat.switchinc.org/Viewer.aspx?img=7578853> <http://dbpedia.org/property/isCitedBy>
<http://dbpedia.org/resource/List_of_Attorneys_General_of_

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Loading RDF using GSQL
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Subject Predicate
Object
<http://0-access.newspaperarchive.com.topcat.switchinc.org/Viewer.aspx?img=7578853> <http://dbpedia.org/property/isCitedBy>
<http://dbpedia.org/resource/List_of_Attorneys_General_of_
CREATE LOADING JOB loadRDF FOR GRAPH rdf {
LOAD "/home/ubuntu/dbpedia2016-04.rdf"
TO EDGE HasObject VALUES (subject($0), object($2), predicate($1)),
TO EDGE SubHasPred VALUES (subject($0), predicate($1)),
TO EDGE ObjHasPred VALUES (object($2), predicate($1))
USING Separator=">", Header="false";
}
subject(), object(), and predicative() are user-defined functions which parse the RDF syntax.

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4. Extracting Knowledge from the Graph
1. getMostRelatedPerson
Given a person, output the top k most related person via common objects.
• http://dbpedia.org/resource/Limas_Sweed
• http://dbpedia.org/resource/Tony_Hills_(American_football)
2. getTheTopPublisher
Given a place, output the top k most productive publishers who born in that place.
• http://dbpedia.org/resource/New_York
• http://dbpedia.org/resource/Pittsburgh
3. getRelatedTopics
Given a Predicate, output the top k most visited Entities during the traversal.
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4. Extracting Knowledge from the Graph
1. getMostRelatedPerson
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Input
Person
Common
Object
Common
Object
Common
Object
Related
Person
Person
Natural
Person

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4. Extracting Knowledge from the Graph
2. getTheTopPublisher
or k most visited Entities during the traversal.
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Person
Article
Article
ArticlePerson
Input
Place
Born in
Born in
Published
Published
Published

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4. Extracting Knowledge from the Graph
3. getRelatedTopics
Given a Predicate, output the top k most visited Entities during the traversal.
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Input
Predicate
Related
Entities
Related
Entities
Related
Entities
Related
Entities
Related
Entities
… ...
Related
Entities
Related
Entities
Related
Entities

22. Q&A
Please send your questions via the Q&A menu in Zoom
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23. © 2018 TigerGraph. All Rights Reserved
Episode 2:
Building the Next Generation Recommendation Engine with a Graph DB
https://info.tigergraph.com/graph-gurus-2
Episode 3:
Detecting Fraud and Money Laundering in Real-Time with a Graph DB
https://info.tigergraph.com/graph-gurus-3
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