K@Persons will contribute to create K@Networks of excellence, thanks to the combination of an Internet Platform and data standards, that will enable all of the participating entities to collaborate, share and build K@Nowledge.

1. Global Identities for Global
Knowledge sharing
K@Personas meet K@Networks
http://grou.ps/knetworks
Carlos Iglesias – Consultor TIC
contact@carlosiglesias.es | @carlosiglesias
es.linkedin.com/in/carlosiglesiasmoro

2. K@Networks
Sharing and dissemination
Networks to promote the exchange
of Knowledge, Good Practices and
implementation strategies.

3. K@Personas
Entities, individuals, organizations or
systems in a context where, throught a
set of authorizations, can trigger or use
different services, in order to, in real time,
interact with other K@Personas as part
of a knowledge transaction.

4. K@P build K@nowledge on K@N
K@Persons will contribute to create
K@Networks of excellence, thanks to the
combination of an Internet Platform and
data standards, that will enable all of the
participating entities to collaborate, share
and build K@Nowledge.

5. What a K@Persona knows
People often publish a huge data about
themselves on the Web, such as:
– Who they are.
– Who they know.
– What they are interested in.
– Their curriculum vitae
– Employment history.
– Projects they work on.
– Their publications.
– …

6. Every K@Persona is unique
An unique Web identity for each
K@Persona allow us to identify ourself
when publishing this sort of information
online and linking to each of those
resources from anywhere.
#me

7. The K@Persona Web ID
Having an unique Web ID allows
K@Personas to be referenciable and
built personal relations across different
thematic K@Networks on the Web.
This is key to enabling Knowledge
networks between different stakeholders
while allowing each player to remain in
control of the data they publish.

8. WebID s characteristics
• A way to uniquely identify a person,
company, organisation, or any other
agent using an URI.
• Web Identification mechanism based on
FOAF and SSL.
#me

9. K@Persona select what to share
To deal with privacy issues, a profile
server should reveal more or less about
a given K@Persona, its associated data
and knowledge depending on the
K@Network where the interaction
occurs.
P3P

10. K@Personas meet K@Networks
The creation of knowledge-driven
Networks involve a complex set of tasks
that require the joint effort of many
stakeholders, including individuals,
companies,universities and
governments.

11. K@Personas meet K@Networks
The success of a K@Network is closely
linked to the skills and knowledge of their
K@Personas and the tools they have to
share and collaborate.

12. K@nowledge across K@Networks
When a K@Network does not allow you to
link your data, it will be effectively
confining your data, only being used
within this network.
When data becomes linked to a given
K@Persona it travels with you from
network to network, and other people and
crawlers will find as much as you choose
to expose.

13. Confined K@nowledge
When a K@Network does not allow you to
link your data, it will be effectively
confining your data, only being used
within this network.

14. Confined K@nowledge
• Each K@Network stores all K@nowledge in its
own database and is responsible of updating.
• Duplication of knowledge and efforts.
• Using data from another network requires to
download and copy into your own.
• If one updates, everything becomes out of
synchronization, presenting conflicting
information to the community.

15. Linked Open K@nowledge
• Data is shared behind the scenes.
• Each K@Network focus on their topics and
can import supplemental data.
• Every imported data updates automatically.
• Consistent information across multiple sites.

16. K@Network Contributions
The different K@Networks can also
expose (some of) the data which you
have created into them and will thus be
associated to your K@Persona.
Any othe platform can consume the data
in whatever new way combining different
sources.

17. K@nowledge sharing
To enable K@nowledge sharing across
different K@Networks it is necessary that
every K@Persona talks the same
language.
But, what language to choose?

18. Controlled Vocabularies
• Simple but powerfull model of given objects in
a specific universe and their associated
terminology.
• Semantics and meaning to get context.
• Common understanding and interoperability.
• Multilingualism also easier to address.

19. Controlled Vocabularies
Universe of vocabularies that can be adapted
and reused for K@Networks and K@Personas:
– FOAF: People description
– SIOC: Semantically-Interlinked Online Communities
– SKOS: Simple Knowledge Organization System
– POWDER: Protocol for Web Description Resources
– ADMS: Asset Description Metadata Schema
– SCHEMA: Structured data markup

20. Specialized Vocabularies
More specialized vocabularies will also be
needed when dealing with specific topics:
– Tourism: e.g. Tourism resources.
– DCAT: Data Catalogues.
TOURISM OPEN DATA
T
T
T
T
O
O
O

21. Objectives: Tourism
• Thematic Tourism K@Network.
• Match supply (Tourism resources) and
K@Personas demand (K@Tourists).
• Ad-hoc Tourism specialized vocabulary
for resources and preferences.
• FOAF and SIOC adaptations and/or
extensions for demand profiling.

22. Objectives: Open Data
• Thematic OData in Society K@Network
• Enable K@Personas to easily create
Open Data apps to match K@Personas
(K@Citizens) demand of services.
• Ad-hoc Apps specialized vocabularies
for apps and demand.
• FOAF and SIOC adaptations and/or
extensions for demand profiling.

23. Expected outcomes
• WebID implementation for K@Personas
– Identification system integrated in the K@Platform.
• Profile Manager to explore K@Personas and
their networks.
• Core reference Vocabularies
– Adaptation of existing vocabularies and development
of new ones to cover the specific use cases: Tourism
and OGD.
• Two Thematic K@nowledge managers
(Tourism and OData apps) to import/export
relevant topic information from K@Networks and
K@Personas.