Knowledge graphs and graph-based data in general are becoming increasingly important for addressing various data management challenges in industries such as financial services, life sciences, healthcare or energy.
At the core of this challenge is the comprehensive management of graph-based data, ranging from taxonomy to ontology management to the administration of comprehensive data graphs along with a defined governance framework. Various data sources are integrated and linked (semi) automatically using NLP and machine learning algorithms. Tools for securing high data quality and consistency are an integral part of such a platform.
PoolParty 7.0 can now handle a full range of enterprise data management tasks. Based on agile data integration, machine learning and text mining, or ontology-based data analysis, applications are developed that allow knowledge workers, marketers, analysts or researchers a comprehensive and in-depth view of previously unlinked data assets.
At the heart of the new release is the PoolParty GraphEditor, which complements the Taxonomy, Thesaurus, and Ontology Manager components that have been around for some time. All in all, data engineers and subject matter experts can now administrate and analyze enterprise-wide and heterogeneous data stocks with comfortable means, or link them with the help of artificial intelligence.
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Leveraging Knowledge Graphs in your Enterprise Knowledge Management System
1. Andreas Blumauer
CEO, Semantic Web Company
Leveraging Knowledge
Graphs in your
Enterprise Knowledge
Management System
Use PoolParty 7.0
to manage Knowledge
Graphs along the whole
Linked Data Life Cycle
2. CMS/DMS
/DAM/..
Graph-based
Introduction
2
Semantic Web
Company
Founder &
CEO of
Andreas
Blumauer
developer &
vendor of
2004founded
7.0version
active at
based on
headquartered
part of
Knowledge
Graphs
manages
standard for
part of
>200
serves customers
Taxonomies
Ontologies
standard for
graduates
Text
Mining
used for
Graph
database
integrates
with
PoolParty
Software Ltd
Director of
parent
company of
London
located
named
by
Vienna
Gartner
KMWorld
Search
engine
6. 6Semantic AI
Fusing Machine
Learning with
Knowledge
Graphs
“...the use of graphs as a means to
better generalize from one instance of
a problem to another 1)
.”
1) Relational inductive biases, deep learning, and graph networks
2) AI Requires More Than Machine Learning (via Forbes)
3) DARPA Embraces ‘Common Sense’ Approach to AI
“The confluence of symbolic
reasoning and machine learning
enables the enterprise to solve an
assortment of complicated business
problems applicable to real-world
situations -- as opposed to simply
automating facile, repetitive tasks. 2)
.”
“The absence of common sense
prevents an intelligent system from …
communicating naturally with people.
3)
.”
7. 7The fast growing
Graph Database
Market
Amazon Neptune Azure Cosmos DB
▸ Stardog
▸ Marklogic
▸ AllegroGraph
▸ GraphDB
▸ Oracle Spatial&Graph
▸ Neo4j
▸ ...
Property Graph RDF Graph (Triple Stores)
Main use case Traverse a graph Query a graph
Typical
applications
Path Analytics,
Social Network Analysis
Data Integration,
Knowledge Representation
Standards No standards
→ Gremlin, Cypher, PGQL, ...
W3C Semantic Web standards
→ SPARQL 1.1
Additional
options
Shortest path calculations Inferencing
11. Knowledge
Graphs as input
for Machine
Learning
11 Unstructured Data
Structured Data
Other Domain-
Specific Graphs
Machine
Learning
Enterprise
Knowledge
Graph
Cognitive
Applications
13. 13Five Generic
Use Cases for
Graphs
1. dealing with hierarchical or highly connected datasets
2. entity-centric views (in contrast to document-centric views)
3. exploring the connections between the entities of a graph
4. integrating heterogeneous data sources
(structured & unstructured, “schema-late” approach)
5. federated (unified) views across multiple data silos within the
enterprise
15. 15Example:
Citizen portal
healthdirect.gov.au/
As a citizen I want to receive guidance to
find reliable health information,
including
● articles from trusted sources
● information about drugs and
medicines
● medical services
● guidance along symptoms
Trusted health
information
Australian Health
Thesaurus
DrugBank
→ Linking Structured Data and Documents
to Industry Knowledge Graphs
Australian Register of
Therapeutic Goods
16. 16Example:
HR Analytics
As an HR manager, for upcoming
training programmes, I want to
identify employees who
● have a certain skill set
● have a specific degree
● have skills that are increasingly
important on the labour market
● fall into a specific salary range
Employee database
Resumes
Labour market statistics
→ Linking Structured to Unstructured Data
17. How it works
17
Employee
database
Resumes
Labour market
statistics
PoolParty UnifiedViews
RDF
Graph Database
PoolParty GraphSearch
PoolParty
Thesaurus Server
PoolParty
User
Now I can
identify
employees
along many
dimensions.
18. 18Example:
Research in
Life Sciences
As a researcher in pharmaceutical
industry, I want to plan new
experiments more efficiently.
I want to know what’s already
available. I’m interested in former
experiments where
● certain genes were tested
● under specific treatment conditions
● in a target therapeutic area
● with help from categorisation
systems like ‘disease hierarchies’
UniProt, ChEMBL
Experiments
Documentation
MeSH
DrugBank
→ Linking Structured to Unstructured Data
and to Industry Knowledge Graphs
19. 19Making Use of
Knowledge
Graphs
→ Knowledge Graphs serve as means to enrich unstructured information
to provide a rich set of additional access points to document repositories
Experiments
Document
Store
23. Labels and basic relations:
Taxonomies and Thesauri
prefLabel
Venice
prefLabel
St. Mark’s Square
altLabel
Piazza
San Marco
Peggy
Guggenheim
Museum
prefLabel
Piazza
altLabel
Town Square
related
related
prefLabel
broader
http://my.com/1
http://my.com/2
http://my.com/3
http://my.com/4
24. Classes, specific relations, restrictions:
Ontologies and Custom Schemas
prefLabel
Venice
prefLabel
St. Mark’s Square
altLabel
Piazza
San Marco
http://schema.org/City
http://schema.org/TouristAttraction
http://schema.org/ArtGallery
Monday through
Sunday, all day
opening
Hours
image
http://schema.org/containedInPlace
prefLabel
Piazza
altLabel
Town Square
Peggy
Guggenheim
Museum
prefLabel
containedInPlace
containedInPlace
broader
25. Metadata and Graph annotations
prefLabel
Venice
prefLabel
St. Mark’s Square
altLabel
Piazza
San Marco
http://schema.org/City
http://schema.org/TouristAttraction
http://schema.org/ArtGallery
Monday through
Sunday, all day
opening
Hours
image
http://schema.org/containedInPlace
prefLabel
Piazza
altLabel
Town Square
Peggy
Guggenheim
Museum
prefLabel
containedInPlace
containedInPlace
CC BY-SA 3.0
broader
26. Entity linking and schema mappings:
Links to other graphs
prefLabel
Venice
prefLabel
St. Mark’s Square
altLabel
Piazza
San Marco
http://schema.org/City
http://schema.org/TouristAttraction
http://schema.org/ArtGallery
Monday through
Sunday, all day
opening
Hours
image
http://schema.org/containedInPlace
prefLabel
Piazza
altLabel
Town Square
Peggy
Guggenheim
Museum
prefLabel
CC BY-SA 3.0
broader
containedInPlace
containedInPlace
27. Linking to data and documents
stored in other systems
prefLabel
Venice
prefLabel
St. Mark’s Square
altLabel
Piazza
San Marco
http://schema.org/City
http://schema.org/TouristAttraction
http://schema.org/ArtGallery
Monday through
Sunday, all day
opening
Hours
image
http://schema.org/containedInPlace
prefLabel
Piazza
altLabel
Town Square
broader
Peggy
Guggenheim
Museum
prefLabel
containedInPlace
CC BY-SA 3.0
The Peggy
Guggenheim
Collection is
a modern art
museum on the
Grand Canal in
the Dorsoduro
sestiere of
Venice, Italy.
containedInPlace
32. PoolParty
GraphEditor
32
▸ create ontology-driven custom editors to work with graph data
▸ use multiple graphs to create integrated views on graph data
▸ import and export of RDF graphs
▸ benefit from assisted search over graph data
▸ benefit from assisted bulk editing of RDF graphs
▸ administrate graphs based on user-friendly inline editing
▸ generate SPARQL queries based on an assistant
33. PoolParty
Notifications
33
▸ stay informed on
changes in your
project
▸ configure multiple
notification settings
per project
▸ get notifications via
webhooks
▸ connect APIs to
consume notifications
34. Improved
Ontology
Management
34
▸ access your ontologies via a
tree view
▸ apply multilingual labels to
your classes, attributes and
relations
▸ define user group based
access rights on your
ontologies and custom
schemes
35. Improved User
Management
35
▸ access users/ roles/
groups via a tree
view
▸ an action-based role
management has
been implemented
▸ define
project-based roles
per user
36. Integration of
NER based on
Machine
Learning
▸ With 7.0, named entities can be extracted by using the
concept extract service (extract call).
▸ This complements PoolParty’s vocabulary-based
entity extraction method
▸ Two methods are now supported by default
▹ Maximum Entropy classification: person,
location, organisation (more specific classifiers
can be added programmatically)
▹ Rule-based recognition by using regex
expressions
36