Rapid development of Internet and Web technology is changing the state of the art in communication of knowledge, or results of research activities. Particularly, Semantic technology, linked and open data become key enablers for successful and efficient progress in research. At first, I define the research data service (RDS) and discuss typical current and possible future usage scenarios involving RDS. Further, I discuss the state of the art in the areas of semantic service and data annotation and API construction, as well as infrastructural solutions, applicable for RDS realisation. At last, innovative methods of online dissemination, promotion and efficient communication of research are discussed.

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Towards Semantic APIs for Research
Data Services
Anna Fensel
© Copyright 2016 | www.sti-innsbruck.at
22.06.2016
Metadata e-infrastructures workshop @ University of Vienna, Austria

2. • Motivation and Introduction to Research Data Service (RDS)
• Modeling RDS
• Managing and programming RDS
• Disseminating RDS
• Conclusions
Outline
2016 www.sti-innsbruck.at 2

3. • Motivation and Introduction to Research Data Service (RDS)
• Modeling RDS
• Managing and programming RDS
• Disseminating RDS
• Conclusions
Outline
2016 www.sti-innsbruck.at 3

4. • Acceptance of the open science* principle entails open access not
only to research data but also to tools that allow researchers to
perform various types of activities over these data including mining,
visualization, and analysis.
• Research Data Services (RDSs) enable researchers to conduct
efficiently and effectively their research activities.
• One of the challenges faced by researchers, in a globally networked
scientific world, is to be able to locate RDSs that fulfill their research
needs. By Research Data Service Discoverability we mean the
capability of automatically locating research data services that fulfill a
researcher goal.
• Making a RDS discoverable should enables the service (re-)use.
* https://ec.europa.eu/digital-single-market/en/open-science
Motivation
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5. Research Data Service (RDS):
•is a subclass of Service in a general sense
(it has a service provider and service
consumer, added value,…),
•is a data service, part of data economy,
•applicable in scenarios implementing some
part of research process,
•may be delivered by a program/IT system,
but also via other means e.g. a human.
What is a “Research Data Service”?
2016 www.sti-innsbruck.at 5
Wikipedia: Research comprises
"creative work undertaken on a
systematic basis in order to
increase the stock of knowledge,
including knowledge of humans,
culture and society, and
the use of this stock of knowledge
to devise new applications."
Wikipedia: Research comprises
"creative work undertaken on a
systematic basis in order to
increase the stock of knowledge,
including knowledge of humans,
culture and society, and
the use of this stock of knowledge
to devise new applications."

6. Definition at an abstract level: a “research data service is a rule of
correspondence between two sets”, or
“A Concrete Research Data Tool on which there exists an Institutional
Commitment in the form of a Service-Level Agreement.”.
These and more definitions are to be comprehensively presented at “White paper on
Research Data Service Discoverability” of the RDA Europe project (European Union’s
Horizon 2020 research and innovation programme) – ongoing work.
What is a “Research Data Service”?
2016 www.sti-innsbruck.at 6

7. • Web services are essentially a programmatic layer on top of
distributed systems.
• Research Data Service is defined in this presentation earlier.
– So it may or may not be implemented as Web service.
– And has specific characteristics related to research.
Differences between a Research Data
Service and a Web Service
2016 www.sti-innsbruck.at 7

8. • Motivation and Introduction to Research Data Service (RDS)
• Modeling RDS
• Managing and programming RDS
• Disseminating RDS
• Conclusions
Outline
2016 www.sti-innsbruck.at 8

9. Applicable to RDS: Web Service properties
• Functional
– contains the formal specification of what exactly the
service can do.
• Behavioral
– how the functionality of the service can be achieved
in terms of interaction with the service and as well in
terms of functionality required from the other Web
services.
• Non-functional properties
– captures constraints over the previous mentioned
properties.

10. Applicable to RDS: Web Service related tasks
• Discovery: “Find services that matches to the service requester
specification” .
• Selection and Ranking: “Choose the most appropriate services among the
available ones”
• Composition: “Assembly of services based in order to achieve a given goal
and provide a higher order of functionality”.
• Mediation: “Solve mismatches among domain knowledge used to describe
the services, protocols used in the communication, data exchanged in the
interaction (types used, and meaning of the information) and business
models of the different parties”.
• Execution: “Invocation of a concrete set of services, arranged in a
particular way following programmatic conventions that realizes a given
task”.
• Monitoring: “Supervision of the correct execution of services and dealing
with exceptions thrown by composed services or the composition workflow
itself”.
• Handover: “Replacement of services by equivalent ones, which solely or in
combination can realize the same functionality as the replaced one, in case
of failure while execution”.

11. • Going mainstream: schema.org
• Linked Open Data cloud counts
25 billion triples
• Open government initiatives
• BBC, Facebook, Google, Yahoo,
etc. use semantics
• SPARQL becomes W3C
recommendation
• Life science and other scientific
communities use ontologies
• RDF, OWL become W3C
recommedations
• Research field on ontologies and
semantics appears
• Term „Semantic Web“ has been
„seeded“, Scientific American
article, Tim Berners-Lee et al.
Semantic Web Evolution in One Slide
2008
2001
2010
2004 Source: Open Knowledge Foundation

12. 5-star Linked OPEN Data: Applicable
to Any, also Research Data
★ Available on the web (whatever
format) but with an open lisence, to
be Open Data
★★ Available as machine-readable
structured data (e.g. excel instead of
image scan of a table)
★★★ as (2) plus non-proprietary
format (e.g. CSV instead of excel)
★★★★ All the above plus, Use open
standards from W3C (URIs, RDF and
SPARQL) to identify things, so that
people can point at your stuff
★★★★★ All the above, plus: Link
your data to other people’s data to
provide context

13. What is Schema.org?
• Schema.org provides a collection of shared vocabularies.
• Launched in June 2011 by Bing, Goolge and Yahoo
• Yandex joins in November
• Purpose:
Create a common set of schemas for webmasters to mark-
up with structured data their websites.
13

14. Motivation: What for?
1) Lead to the generation of rich snippets in search engine results more
attractive for the users
14
14

15. Motivation: What for?
2) Query/Answer based Search Engine
•Semantic Search
•Making use of structured data, the search engine can understand the
content of your web site and make use of it to give a more accurate
search result.
15
15

16. Advantages
• Webmasters can use schema.org to mark up their web
pages (creating enriched snippets) in a way that is
recognized by major search engines.
• The enriched snippets enable search engines to
understand the information on web pages that results
in richer and more attractive search results for the
users Easier for users to find relevant and right
information on the web.
• Search engines including Bing, Google, Yahoo! and
Yandex rely on this markup to improve the display of
search results.
• Helps webmasters to rank higher in search results
• This markup has the potential to enhance the CTR
(click through ratio) from the search results from
anywhere between 10-25%. 16

17. Advantages
• Schema.org can be also used for structured data
interoperability.
• Its usage can also lead to the development of new tools, for
example Google Recipe Search, which may open up other
marketing channels if not now, in the near future.
• Obviously also relevant for RDS.
Information from: http://builtvisible.com/micro-data-schema-org-guide-generating-rich-snippets/#schemaorg
17

18. Input:
•Aim of the task/part of the research process to be executed (goal),
•Context and implementation restrictions (functional and non-functional
properties):
– Domain, QoS, scalability, price...
Output:
•Information, data,
•Research process step completed,
•Knowledge (if e.g. the service has a human in the loop),
•Change of state (can be intangible).
Metadata semantics needed for the
description of the “input set” (domain) and
the “output set” (co-domain)
2016 www.sti-innsbruck.at 18

19. • In “ideal” world, yes.
• Also yes in perspective, as the amount of research data services
increased & research steering service economy tends to be more
interdisciplinary.
– It becomes more difficult to identify and find relevant services.
• In real world however, simpler things and models spread better.
– E.g. programmableweb still does not have semantic descriptions for
service APIs
• Yet, here is a clear progressive usage potential here, as the research
community is a good choice to be early adopters.
Do we need to specify the syntax and
semantics of the elements of the
domain and co-domain?
2016 www.sti-innsbruck.at 19

20. • A Research Data Service instance definitely changes with time
– E.g. its quality may alter, its non-functional properties change
– Its context and usage vary
– It may appear and disappear
– Its implementation may change
– …
• However, modeling programmatically such state changes is difficult,
and make them widely used is even more difficult.
• Therefore, Research Data Services should be designed as stateless.
RDS: “stateless” or “state-based”?
2016 www.sti-innsbruck.at 20

21. • Aimed dataset or service,
• Scientific discipline,
• Scientific method,
• Domain,
• Information about quantity, quality, availability, creator(s)/provider(s),
• Access and license policies,
• Origin and annotation of reused/subcontracted sources (when
applicable).
(Some – even all - of the above can be optional.)
Examples:
• “compare performance of my semantic repository at criteria X”,
• “find datasets with energy consumption of fridges in Vienna”.
What should be included in a RDS profile
in order to appropriately describe its
functionality?
2016 www.sti-innsbruck.at 21

22. • Possibly e.g. in conjunction with research method description.
– Think BPEL, etc.
Scientific workflows: appropriate for
describing the process model of a
Research Data Service
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Basing on an USDL and Linked USDL example,
source: Cardoso, J., Lopes, R., Poels, G. “Service Systems”, Springer, 2014. ISBN 978-3-319-10813-1.

23. • Motivation and Introduction to Research Data Service (RDS)
• Modeling RDS
• Managing and programming RDS
• Disseminating RDS
• Conclusions
Outline
2016 www.sti-innsbruck.at 23

24. • In most cases existing service description frameworks like
OWL-S and WSMO are too complex, except.
– In genetics, for example, some data annotations are made
using OWL, and building a service on top of it would be a
natural extension.
• But in many cases it would not be the best choice.
– Most data are not shared on the Web with OWL, in
particular.
– OWL is too complex and too expressive.
• Using real data in research more essential, so eventually
the frameworks in which the real data mostly is, are
important, so eventually linked (open) data, schema.org
Using Semantic Web Services for RDS?
2016 www.sti-innsbruck.at 24

25. An Option: Applying Linked Service
System (LSS) Model Structure
Source: Cardoso, J., Lopes, R., Poels, G. “Service Systems”, Springer, 2014. ISBN 978-3-319-10813-1.

26. LSS Implementation Example
- Goals
Source: Cardoso, J., Lopes, R., Poels, G. “Service Systems”, Springer, 2014. ISBN 978-3-319-10813-1.

27. LSS Implementation Example
- Locations
Source: Cardoso, J., Lopes, R., Poels, G. “Service Systems”, Springer, 2014. ISBN 978-3-319-10813-1.

28. • Presence of the ways to annotate the functionality, domain,…
• Assumption of “incorrectness and incompleteness” (LarKC project),
• Much of matchmaking and reasoning should be moved to the
applications – but: semantics can support maintenance of community-
generated reusable mapping (e.g. stating that two service parts are
the same).
Main characteristics and capabilities of a
knowledge representation language
appropriate for the description of the
functionality of a data service as well as
for effectively supporting reasoning in the
matchmaking process
2016 www.sti-innsbruck.at 28

29. • Possible assuming a non 100% performance and a human in the loop.
Example: ongoing STI’s PhD of Ioannis Stavrakantonakis (winner of Netidee
scholarship 2015 as one of the best Internet-related dissertations in
Austria)
Towards Automatic Matching of APIs
2016 www.sti-innsbruck.at 29

30. Modeling manually
even simple domain and web page takes time
Suggesting terms speed up the process:
LOV: vocabulary ranking for different
domains
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More at: Stavrakantonakis, I., Fensel, A., & Fensel, D. (2015, November).
Linked Open Vocabulary Recommendation Based on Ranking and Linked Open Data.
In Joint International Semantic Technology Conference (pp. 40-55).
Springer International Publishing.

31. We need it, particularly because:
• Data sets vary from domain to domain, and often the research is
domain specific,
• Eases discoverability.
Also in agreement with other trends in the area e.g. microservices.
Discipline-specific classification of data
services (classes of data services) supported
by discipline-specific ontologies
2016 www.sti-innsbruck.at 31
Danado, J., Davies, M., Ricca, P., & Fensel, A. (2010).
An authoring tool for user generated mobile services.
In Future Internet-FIS 2010 (pp. 118-127).
Springer Berlin Heidelberg.
Davies, M., Carrez, F., Heinilä, J., Fensel, A.,
Narganes, M., & Carlos dos Santos Danado, J. (2011).
m: Ciudad: enabling end-user mobile service creation.
International Journal of Pervasive Computing and
Communications, 7(4), 384-414.

32. • A single point to make services discovered.
– A meta- Research Data Service in itself.
• Ideologically like UDDI, or like programmableweb for Web APIs now.
On the role and architecture of
Registries/Directories/Catalogs of
Services
2016 www.sti-innsbruck.at 32

33. • By data, by domain, by functionality,…
• The classifications do not have to be created apriori, but could be
created ad hoc once the annotations are there – not to restrict the
usage.
• De facto seem to be classified by provider platforms e.g. in area:
How to classify RDS? “stateless/state-
based”, “type of input data: discrete
data/vectors/functions/streaming data”,
“types of output data”, etc.
2016 www.sti-innsbruck.at 33
For publications –
numerous
repositories
e.g. from
publishers,
Zenodo,
GoogleScholar

34. • It is a necessity and a criterion.
• But the situation when the citations number is decisive in the ranking
(e.g. how Google Scholar does – most cited on top) is not optimal, as
such search may overlook data with a closest match, and the output
itself also impacts citations.
Is “citation” instrumental in making
research data service discoverable?
2016 www.sti-innsbruck.at 34
Matthew 25:29
New American Standard Bible
"For to everyone who has, more shall be given, and he will have an abundance;
but from the one who does not have, even what he does have shall be taken away.
Matthew 25:29
New American Standard Bible
"For to everyone who has, more shall be given, and he will have an abundance;
but from the one who does not have, even what he does have shall be taken away.

35. • Motivation and Introduction to Research Data Service (RDS)
• Modeling RDS
• Managing and programming RDS
• Disseminating RDS
• Conclusions
Outline
2016 www.sti-innsbruck.at 35

36. • Research is disseminated nowadays also via social media
• Social media is arguably used by young generations more commonly
than email
• There are many social media channels allowing research
communication existing, to name a few:
– ResearchGate
– Academia.edu
– Google Scholar
– SlideShare
– …
• ONLIM is a start-up of STI Innsbruck delivering a solution to efficient
dissemination on social media, based on semantics (schema.org)
• ONLIM is available as a tool at www.onlim.com
The world is multi-channel now: being
present everywhere to be seen
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41. www.onlim.com
ONLIM: Dashboard with suggested posts
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42. Data licensing
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Image from DALICC consortium: FH St Pölten, STI Innsbruck, WU Wien,
Semantic Web Company, Höhne i. d. Maur & Partner Rechtsanwälte OG
Data licensing is still complicated, formats for automated licensed data use are under-defined.
Semantic standards for license development are in progress e.g. ODRL, RightsML.
A new project delivering a support system for data licensing is funded in this year‘s FFG
Ikt der Zukunft call, named DALICC: Data Licences Clearance Center.

43. • Motivation and Introduction to Research Data Service (RDS)
• Modeling RDS
• Managing and programming RDS
• Disseminating RDS
• Conclusions
Outline
2016 www.sti-innsbruck.at 43

44. • Research Data Services are data services, and surely belong to the
data service value chain.
• There exist promising semantic languages and technologies to be
applied to solutions of RDS modeling and discovery problems e.g.
linked services, linked data, schema.org.
• Efficient dissemination of research is very important. Also
dissemination needs to multi-channel now, and new kind of channels
appear e.g. social media.
• Relevant data value chain techniques and tools are in development
e.g. on (semantic) data licensing.
• See more of smart data development at
Summary
2016 www.sti-innsbruck.at 44
29-30 June 2016, 12-15 September 2016,
Eindhoven Leipzig

45. • C. Thanos (2014) Mediation: The Technological Foundation of
Modern Science. Data Science Journal, Vol.13, pp.88–105. DOI:
10.2481/dsj.14-016
• T. Gruber, “Towards Principles for the Design of Ontologies Used for
Knowledge Sharing” in “Formal Ontology in Conceptual Analysis and
Knowledge Representation”, Technical Report KSL 93-04,
Knowledge Systems Laboratory, Stanford University.
• D. Fensel, F. Facca, E. Simperl and I.Toma. Semantic Web Services
Textbook, Springer, 2011.
• Silvio Peroni, Alexander Dutton, Tanya Gray, David Shotton (2015).
Setting our bibliographic references free: towards open citation data.
Journal of Documentation, 71 (2): 253-277.
http://dx.doi.org/10.1108/JD-12-2013-0166
• Fensel, A., Toma, I., García, J. M., Stavrakantonakis, I., & Fensel, D.
(2014). Enabling customers engagement and collaboration for small
and medium-sized enterprises in ubiquitous multi-channel
ecosystems. Computers in Industry, 65(5), 891-904.
References
2016 www.sti-innsbruck.at 45

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