This document discusses the author's company's portfolio of Linked Data projects in 2017 and their perspective on future growth opportunities in this area. It provides a snapshot of 9 current clients representing a variety of sectors and use cases for Linked Data including business vocabularies, reference data management, and semantic enterprise content management. The author analyzes the relative revenue contributions of different use cases in 2017 and expectations for relative growth. Specific examples are discussed including collating concepts across vocabularies for a client in education and the potential for advanced data extraction techniques to support semantic ECM.

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Linked Data: A Commercial Outlook
Our LD-Portfolio in 2017 and a perspective on the future
Jan E. Voskuil
SEMANTiCS 2017
Amsterdam

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2012 2013 2014 2015 2016 2017
Revenue
from Linked
Data work
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Our LD-Portfolio: A Snapshot
• Kennisnet
• KOOP
• Wolters Kluwer NL
• Alliander
• Beeld en Geluid
• Kadaster
• National Police
• CROW
• OntoPharma
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Our LD-Portfolio: A Snapshot
• Kennisnet
• KOOP
• Wolters Kluwer NL
• Alliander
• Beeld en Geluid
• Kadaster
• National Police
• CROW
• OntoPharma
SEMANTiCS 2017 5
Kennisnet supports
schools with IT.
Provides a basic IT-
infrastructure and
shares knowledge.
Maintains several
standards for
information exchange.

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Our LD-Portfolio: A Snapshot
• Kennisnet
• KOOP
• Wolters Kluwer NL
• Alliander
• Beeld en Geluid
• Kadaster
• National Police
• CROW
• OntoPharma
SEMANTiCS 2017 6
Publication office for
the Dutch
government
organisations.
Maintains value lists
that have a legal
status, e.g., the list of
municipalities.

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Our LD-Portfolio: A Snapshot
• Kennisnet
• KOOP
• Wolters Kluwer NL
• Alliander
• Beeld en Geluid
• Kadaster
• National Police
• CROW
• OntoPharma
SEMANTiCS 2017 7
Wolters Kluwer Legal &
Regulatory offers
information, software and
tools for legal
professionals.
Business vocabularies for
knowledge management
and search.

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Our LD-Portfolio: A Snapshot
• Kennisnet
• KOOP
• Wolters Kluwer NL
• Alliander
• Beeld en Geluid
• Kadaster
• National Police
• CROW
• OntoPharma
SEMANTiCS 2017 8
Alliander is an energy
network company. We are
bringing an open and
sustainable energy market
closer to the consumer.
Maintains several large
business vocabularies and
large amounts of
technical documents.

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Our LD-Portfolio: A Snapshot
• Kennisnet
• KOOP
• Wolters Kluwer NL
• Alliander
• Beeld en Geluid
• Kadaster
• National Police
• CROW
• OntoPharma
SEMANTiCS 2017 9
The Netherlands Institute
for Sound and Vision is a
cultural-historical
organization. It collects,
preserves and opens the
audiovisual heritage.
Maintains several large
thesauri.

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Our LD-Portfolio: A Snapshot
• Kennisnet
• KOOP
• Wolters Kluwer NL
• Alliander
• Beeld en Geluid
• Kadaster
• National Police
• CROW
• OntoPharma
SEMANTiCS 2017 10
The Netherlands’
Cadastre, Land Registry
and Mapping Agency – in
short Kadaster – collects
and registers
administrative and spatial
data on property and the
rights involved.
Publishes Linked Data on
a large scale.

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Our LD-Portfolio: A Snapshot
• Kennisnet
• KOOP
• Wolters Kluwer NL
• Alliander
• Beeld en Geluid
• Kadaster
• National Police
• CROW
• OntoPharma
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The Dutch police uses
Linked Data-technologies
to collate data from
disparate sources.

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Our LD-Portfolio: A Snapshot
• Kennisnet
• KOOP
• WoltersKluwer NL
• Alliander
• Beeld en Geluid
• Kadaster
• National Police
• CROW
• OntoPharma
SEMANTiCS 2017 12
CROW is a non-profit
knowledge partner in
road construction.
CROW maintains thesauri
and a large knowledge
base.

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Our LD-Portfolio: A Snapshot
• Kennisnet
• KOOP
• WoltersKluwer NL
• Alliander
• Beeld en Geluid
• Kadaster
• National Police
• CROW
• OntoPharma
SEMANTiCS 2017 13
OntoPharma, a spin-off of
Taxonic, delivers solutions
for a Data First-approach in
the pharma sector.
Solutions include
automated data extraction,
management of reference
data and product data, and
structured authoring.

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Use Cases
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Use Cases
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Vocabulary-
oriented
Data-oriented
Documents and
data

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Use Cases
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Business vocabularies
Reference Data
Management
Collating business concepts across vocabularies
Vocabulary-
oriented
Data-oriented
Documents and
data

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Use Cases
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Business vocabularies
Reference Data
Management
Collating business concepts across vocabularies
Vocabulary-
oriented
Data-oriented
Documents and
data
Publishing interoperable data
Publishing datamodels Managing datasets

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Use Cases
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Business vocabularies
Reference Data
Management
Collating business concepts across vocabularies
Vocabulary-
oriented
Data-oriented
Documents and
data
Publishing interoperable data
Publishing datamodels Managing datasets
Semantic ECM
Tagging
Extraction
Indexing
Content
Classification
Structured Authoring

19. 19ggg Kennisnet
KOOP
WoltersKluwerNL
Alliander
BeeldenGeluid
Kadaster
Police
CROW
OntoPharma
Managing metadata sets
Business vocabularies
Publishing datamodels
Publishing interoperable data
Collating business concepts
across vocabularies
Reference Data Management
Managing datasets
Semantic ECM

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Business vocabularies
Publishing interoperable data
Managing datasets
Managing metadata sets
Semantic ECM
Reference Data Management
Collating business concepts across vocabularies
Publishing datamodels
Relative contribution 2017
SEMANTiCS 2017 20

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Business vocabularies
Publishing interoperable data
Managing datasets
Managing metadata sets
Semantic ECM
Reference Data Management
Collating business concepts across vocabularies
Publishing datamodels
Expectations of Relative Growth
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Collating concepts across vocabularies
Kennisnet
• Develops standards for
information exchange in the field
of education
• Overlapping semantics
• Same concepts in different models
• Specialized tooling
• TopQuadrant Enterprise Vocabulary
Network
• Custom concept browsers for
visualizing semantic overlap

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> The browser shows info about the term “Samengestelde groep”
> This term is defined in EDEXML, a data exchange standard

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> The browser shows info about the term “Samengestelde groep”
> This term is defined in EDEXML, a data exchange standard
> Also occurs in UWRL and ECKID
> The browser shows a detailed comparison
> Similarities and differences made visible
> High level of automation

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The potential for Semantic ECM
Data First
• From documents to data
• Regulatory requirements
• Data extraction as a service
• Future outlook: create data
and documents in concert

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Advanced algorithms for extracting data
Extracting
concepts not
literally
mentioned in
the text
Source Text
The tablets shall be taken with
liquid, and should not be crushed or
chewed. Ontopharmanax may be
taken with or without food
Extracted data
RoutesOfAdministration
{
“routes”: [“OralUse”]
}
Source Text
Ontopharmanax is for
subcutaneous injection only and
shall not be used for intramuscular
injection.
Extracted data
RoutesOfAdministration
{
“routes”: [
“SubcutaneousInjection”
]}
Dealing with
negations.

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The potential for Semantic ECM
Serve information in the field
• Operatives need specific
information
• Buried in documents
• Semantic Search
• Auto-tagging, extraction,
indexation
• Future outlook: template-
based authoring

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Semantic ECM
• Semantic ECM is transformational
• Affects many processes in many ways
• Authoring
• Redacting
• Retrieval
• Difficulty is structurally underestimated
• Complexity
• Need for solid know-how and the right tools
• Many projects start and then fail miserably
• Still much evangelizing needed!
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Business
vocabularies
Thesauri &
metadata
Data publishing
& management
Semantic
ECM
Concept
Computing
Evolving Use Cases
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• Open Source
• Created by Taxonic, with Kadaster
• RML standard for mapping &
transformation
• From relational to RDF
• From any format to RDF
• First release now available from
Github!
• https://carml.gitlab.io/plain-html/

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Knowledge Works