Unified views of business-critical information across all customer-facing processes and HR-related tasks are most relevant for decision makers. In this talk we present a SharePoint extension that supports the automatic linking of unstructured content like Word documents with structured information from other databases, such as statistical data. As a result, decision makers have knowledge portals based on linked data at their fingertips. While the importance of managed metadata and Term Store is clear to most SharePoint architects, the significance of a semantic layer outside of the content silos has not yet been explored systematically. We will present a four-layered content architecture and will take a close look on some of the aspects of the semantic layer and its integration with SharePoint: - Keeping Term Store and the semantic layer in sync - Automatic tagging of SharePoint content - Use of graph databases to store tags - Entity-centric search & analytics applications Metadata is most often stored per data source, and therefore it is meaningless outside of the silo. In this presentation, we will give a live demo of a SharePoint extension that makes use of an explicit semantic layer based on standards. This approach builds the basis to start linking data across the silos in a most agile way. The resulting knowledge graph can start on a small scale, to develop continuously and to grow with the requirements. In this presentation we will give an example to illustrate how initially disconnected HR-related data (CVs in SharePoint; statistical data from labour market; skills and competencies taxonomies; salary spreadsheets) gets linked automatically, and is then made available through an extensive search & analytics application.

1. Andreas Blumauer
Director
PoolParty Ltd, UK
Linking SharePoint
Documents with
Structured Data
Towards Unified Views of
Business-critical Information

2. Graph-based
Introduction
2
Semantic Web
Company
Founder &
CEO of
Andreas
Blumauer
developer &
vendor of
2004founded
7.0
version
active at
based on
Vienna
located
part of
Enterprise
Knowledge
Graphs
manages
standard for
part of
>200
serves customers
Taxonomies
Ontologies
standard for
graduates
Text
Mining
used for
Graph
databases
integrates
with
PoolParty
Software Ltd
Director of
parent
company of
London
located
named by

3. 3Problem
Statements from
our customers
Data is often bound to specific applications
which reduces reuse of the data dramatically.
Tight coupling of data to applications limits the options
to connect the data to other internal and external sources.
People should participate in a data sharing culture
which is beneficial for the whole enterprise.
When we cannot express data in relational formats we typically
leave the data in a non-machine readable format in a document.
Data scientists should benefit from more consistent semantics of data
to be reused for various machine learning tasks.

4. 4Gartner
Hype Cycle for
Artificial
Intelligence,
2018
> Read more
Organizations can expect significant value from knowledge graphs in many areas:
● ...
● Interrelated data is contextualized data, thereby aiding its discovery and
findability via implicit and indirect connections.
● Once structured in the form of a knowledge graph, unstructured data can be
queried, thereby preprocessing it for analysis.

5. 5Example:
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

6. 6Example:
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

7. 7Making Use of
Knowledge
Graphs
Experiments
→ Knowledge Graphs serve as means to enrich unstructured information
to provide a rich set of additional access points to document repositories

8. 8Making Use of
Knowledge
Graphs and
Automated
Reasoning

9. 9Example:
Recommender
System
Project database
Meeting notes
Interest and
working profiles
→ Benefiting from automatic
recommendations based on
personal profiles
As an expert working for an
International Development Bank, I
want
● to get a better way to
disseminate information to our
staff so that projects would be
more efficient and more
successful,
● to receive relevant articles and
information when meetings are
scheduled,
● during searches, and eventually
to power a chat bot.

10. “Things but not Strings”: Semantic Knowledge
Graphs manage resources, not just terms
http://www.my.com/
taxonomy/62346723
prefLabel
Retina
image
http://www.my.com/
images/90546089
http://www.my.com/
taxonomy/
97345854
prefLabel
Funduscope
altLabel
Ophthalmoscope
http://www.mycom.com
/taxonomy/4543567
prefLabel
Diagnostic Equipment
has broader

11. PoolParty
Semantic Suite
Most complete
Semantic
Middleware on
the Global Market
11
Bain Capital is a venture capital
company based in Boston, MA.
Since inception it has invested in
hundreds of companies including AMC
Entertainment, Brookstone, and Burger
King. The company was co-founded by
Mitt Romney.
Taxonomy &
Ontology Server
Entity Extractor &
Semantic Classifier
Data Integration &
Data Linking
Unstructured
Data
Semi-
structured
Data
Structured
Data
UnifiedViews
PoolParty
GraphSearch
Identify new
candidate concepts
to be included in a
controlled vocabulary
Controlled vocabularies as a basis for
highly precise knowledge extraction
and text classification
Entity Extractor informs
all incoming data streams
about its semantics and
links them
RDF
Graph Database
Factsheet
Schema mapping based
on ontologies

12. 12Integrating
data along the
Linked Data
Life Cycle

13. PowerTagging
for SharePoint
and PoolParty
at a Glance
13
▸ On-premise: PowerTagging Server can be part
of SharePoint farm’s application server
▸ Hybrid scenario: PowerTagging Server can be
both on-site or Azure located
▸ Integrations: SharePoint 2013 / 2016,
Office 365
PowerTagging for SharePoint has been developed by Soitron Group

14. Autotagging &
Manual Tagging
14
Tags are added automatically
based on PoolParty Entity Extractor

15. Autotagging &
Manual Tagging
15

16. Tag Refinement
Dialogue
16

17. Quick Filtering
by tags
17

18. PowerSearch:
Semantic Search
for your
SharePoint
18

19. PowerSearch:
Introducing your
own Knowledge
Graph
19 ▸ Entity-centric views and
context information about
your search term
▸ Additional search refiners
▸ ‘Traversing the knowledge
graph’
▸ Highly configurable
‘knowledge base’
▸ Search, Learn, and
Understand

20. Benefits
PowerTagging
▸ Consistent tagging based on controlled vocabularies
▸ Automatic tag suggestions based on text analytics
▸ User-friendly enterprise taxonomy management
▸ Taxonomies kept in sync with Term Store
PowerSearch
▸ Concept based search: autocomplete from taxonomy
▸ Automatic use of synonyms: get precise results
▸ Configurable search refiners: faceted search based on
taxonomy hierarchy
▸ Include fact box for search term in search results:
benefit from additional context information
20

21. Two Integration
Scenarios
21
DAM/CMS
Option 1:
Concepts are derived from a taxonomy and
tagging is stored together with the asset in
the DAM/CMS
http://apple.com/macmini.jpg
http://apple.com/graph/1234
PoolParty
API
Option 2:
Concepts are derived from taxonomies, and
any tag event is stored in a Graph Database to
link all assets with concepts from the graph.
DAM/CMS
http://apple.com/macmini.jpg
http://apple.com/graph/1234
PoolParty
API
http://apple.com/macmini.jpg
http://apple.com/macmini.jpg
http://apple.com/graph/1234
LD Store
Wed 3 May, 2017User4711
DAM/CMS
API
Pool
Party
Pool
Party

22. Maturity Model
Roadmap for a
more agile Data
Governance
Framework
22

23. GraphSearch over
Integrated Data
incl. SharePoint
23

24. Demo
24

25. How it works
25
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.

26. CONNECT
Andreas Blumauer
Director, PoolParty Software Ltd
CEO, Semantic Web Company GmbH
▸ andreas.blumauer@semantic-web.com
▸ http://linkedin.com/in/andreasblumauer
▸ https://twitter.com/semwebcompany
26
© Semantic Web Company - http://www.semantic-web.at/ and http://www.poolparty.biz/