=Paper=
{{Paper
|id=Vol-2050/invited4
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2050/invited4-Mosca.pdf
|volume=Vol-2050
}}
==None==
https://ceur-ws.org/Vol-2050/invited4-Mosca.pdf
Ontology-mediated Data Integration and
Access in Research and Innovation Policy
Alessandro Mosca,
SIRIS Lab, Research Division of SIRIS Academic, Barcelona, Spain
{name.surname}@sirisacademic.com
Keywords. Open Innovation ecosystem, OBDA/I, Data-driven policies
1. Research and Innovation policy making: a preamble
The goal of the European Research and Innovation (R&I) Policy is to help tackle the
great challenges Europe is facing: spurring smart, sustainable and inclusive economic
growth and job creation, building a resilient society while accommodating globalisation.
Moreover, the current economic situation and the requirements of public accountability
require the maximisation of the Union’s budget’s effectiveness, the capacity to demon-
strate tangible results on the ground, and the relevance of funded research not only for
scientific communities but also for the economy and society at large.
Unfortunately, the emerging disconnection between the fast changing and fast grow-
ing field of scientific research and the tools that the policy makers have at their disposal
to measure and understand its current state, presents serious challenges. This threatens
the effective potential to translate knowledge into socio-economic value (Open Innova-
tion 2.0, Directorate-General for Research and Innovation, EU 2014). Decision-makers
at universities, research institutions, companies, together with policy makers and the
involved public administrations, all are part of a knowledge (learning, discovering and
innovating) engine that, if well managed, could create wealth, jobs, growth and social
progress (The Knowledge Future, Directorate-General for Research and Innovation, EU
2015).
The paper argues about the fact that the above scenario gives precise requirements to
those who are involved in the design and development of digital technologies. The tools
that the Commission, and the policy makers at different territorial levels, have at their
disposal to measure and understand both the current state of the scientific research and
the potentiality of the achieved results in fostering innovation, are still to be developed
indeed, in order to:
• define of future research and innovation priorities (e.g. fields, technologies, sec-
tors);
• identify of emerging fields and competitive solutions for transfer and investments
• shape of new policies by means of scientific and technological evidence;
• re-adjust of existing policies via evidence-grounded monitoring and learning
processes.
2. Open innovation: a challenge for semantic technologies
Being able to implement innovative knowledge transfer channels and to improve the
communication of scientific knowledge to a larger portion of the society is certainly one
the most import message the European Commission (EC) is currently trying to spread
into the scientific and entrepreneurial communities. However, the more we move in the
direction of the current European policies and reccomendations, the more we realise
that it is not only a matter of accountability and knowledge transfer in front of a larger
audience: the relevant actors in the research and innovation audience are expected to
assume new roles, duties and rights with respect to what we have seen happening during
the last ten years.
On this respect, the concept of triple helix of university-industry-government rela-
tions emerged in the mid ’90s [1], and has been further elaborated for explaining struc-
tural developments in knowledge-based economies [2]. This concept points towards the
dynamics that can be expected as a result of interactions involving bi-lateral and tri-
lateral relations among university, industry and government. The central issue of the
triple helix is that the three helices, as well as the interactions among them, define the
rules of the game of a place (e.g., a region or a nation), thereby constraining its devel-
opment possibilities. Full recognition of these constraints is the first step to understand
which development paths are viable and, eventually, how to politically intervene to mod-
ify them in itinere. Sustainable growth of the system requires that the helices actions are
consistent. In fact, when the helices are out of alignment, imbalances occur.
More recently, a fourth helix - the collective sphere of civic societies and larger social
networks - has been added to the three initial ones [3]. This latter definition makes ex-
plicit the role of non-university-industry-government organisations, such as civil asso-
ciations or non-profit organisations and social enterprises, in shaping local and regional
development paths. Quadruple helix processes and positive outcomes are rarely the re-
sult of undeliberate interactions. Collaborative and reflective schemes are needed, but
they cannot be based just on records of past success of single stakeholders and traditional
governance solutions, nor on optimistic declamations of nice common targets in public-
private-partnerships. Learning by interacting, learning by monitoring and evaluating, and
experimental solutions should be practiced deliberately, knowing that incredible power
(and potential support) of the present digital technology. The concept of Open Innovation
is indeed all about that.
The Open Innovation, Open Science, Open to the World - A vision for Europe docu-
ment1 , recently published by the EC, represents the synthesis of the new European Union
approach, based on quadruple helix collaborative design and work, and on the use of
digital technologies, in order to:
1. share information and knowledge beyond scientific publications, and
1 European Commission (2016). Open innovation, open science, open to the world- a vision for Europe.
European Commission’s Directorate-General for Research & Innovation (RTD), Bruxelles.
2. support the co-design and co-planning of scientific policy and strategies that
include the quadruple helix actors.
As one would expect, the new vision brings new needs, new requirements, and a
specific attention focused on two main elements: the Public Engagement - the users are
in the spotlight: an invention becomes an innovation only if users become a part of the
value creation process; and the Ecosystem - the creation of a well-functioning eco-system
that allows co-creation becomes essential for Open Innovation. In such an eco-systems,
the relevant stakeholders are collaborating along and across industry and sector-specific
value chains to co-create solutions to socio-economic and business challenges.
Open innovation has therefore to be taken as the outcome of a complex co-creation
process involving knowledge flows across businesses, academia, financial institutions,
public authorities or citizens. Consciously intervening to tune these flows, while having
in mind the challenges above, is owed to the taxpayer and to ourselves, and is tough.
Responses will need to be based on theory and empirical evidence, as well as conveyed
in a manner that must be understandable [4]. It is within this newly suggested concep-
tual framework that the digital technologies behind the design and the implementation of
support platforms gain a specific characterisation: they have to be open, capable of ac-
commodating new strategic demands, new uses and new data sources, both internal
and external.
On December 7-8, 2007, thirty open government advocates gathered in Sebastopol
(California) agreed on the following 8 principles characterising an Open Data Govern-
ment initiative, currently adopted also by the EC. “Government data shall be considered
open if it is made public in a way that complies with the principles below”: Complete:
All public data are made available; Primary: Data are as collected at the source, with the
highest possible level of granularity, not in aggregate or modified forms; Timely: Data
are made available as quickly as necessary to preserve their value; Accessible: Data are
available to the widest range of users for the widest range of purposes; Machine process-
able: Data are reasonably structured to allow automated processing; Non-discriminatory:
Data are available to anyone, with no requirement of registration; Non-proprietary: Data
are available in a format over which no entity has exclusive control; License-free: Data
are not subject to any copyright, patent, trademark or trade secret regulation.
The reference technologies in this context have been clearly stated. They have to
follow the EC “Linked Open Data” standard, clearly introduced in the EU policy “A
Digital Agenda for Europe”2 . In the document, Linked Open Data are introduced as the
current standards to represent data on a wide range of topics which makes it easier for
developers to connect information from different sources, resulting in new and innova-
tive applications: Linked Open Data enables, as said there, a “browsing” or “discovery”
approach to finding information, as compared to the usual “search” practice3 . The formal
languages behind the concrete realisation of a Linked Open Data initiative are the well
known standards: RDF (”Resource Description Framework”): the flexible data model
based upon the idea of making statements about resources in the form of subjectpredi-
cateobject expressions, known as triples; RDFS/OWL2 (”Resource Description Frame-
work Schema”/”Web Ontology Language”): the schema and ontology languages for de-
scribing concepts and relationships; SPARQL (SPARQL Protocol and RDF Query Lan-
2 https://europa.eu/european-union/file/1497/download_en?token=KzfSz-CR
3 https://data.europa.eu/euodp/en/linked-data
guage): the query language RIF (”Rule Interchange Format”): a rules language originally
designed to exchange rules among different existing rules dialects; RDFa (”Resource
Description Framework in Attributes”): the language for marking up data inside HTML-
based Web pages); and HTTP communication protocol (”Hypertext Transfer Protocol”):
the application protocol for distributed, collaborative, and hypermedia information sys-
tems, at the foundations of the so-called World Wide Web.
2.1. Ontology-based data management
The above mentioned standards are usually referred as Semantic Web technologies. Se-
mantic Web technologies are formal languages and solutions that bring structure and
meaning to information, that adhere to the specific set of W3C open technology stan-
dards. The languages and technologies introduced here are the languages that the open
innovation and open science platform must rely on for the design and implementation of
their data integration and data access services, according to the present EC recommen-
dations, guidelines and visions: the accomplishment of these requirements would ensure
the platforms to be compliant with the directives of the EU about Open Innovation and
Open Science.
The management of complex kinds of information has traditionally been the concern
of Knowledge Representation and Reasoning (KR&R) in Artificial Intelligence. In par-
ticular, a recently introduced paradigm that combines the possibility of using reasoning
with respect to domain knowledge encoded in an ontology, with a mechanism to use the
same ontology also for high level, integrated access to data sources, is that of Ontology-
Based Data Access and Integration (OBDA/I) [5]. Ontologies are usually specified in De-
scription Logics (DLs) [6], a family of knowledge representation languages that provide
one of the main underpinnings for the OWL Web Ontology Language as standardised
by the W3C4 . DLs are equipped with a formal semantics based on First-Order Logic.
This formal semantics allows humans and computer systems to exchange DL ontolo-
gies without ambiguity as to their meaning, and also makes it possible to use logical
deduction to infer additional information from the facts stated explicitly in an ontology.
In the OBDA/I setting, the most commonly used language is OWL 2 QL5 , which is the
profile (i.e., sub-language, in W3C terminology) of OWL 2 that is specifically tailored
for efficiently querying large amounts of data. The domain ontology is then connected
to the data sources through a declarative specification given in terms of mappings that
relate symbols in the ontology (concepts and properties) to views over the data expressed
by means of SQL queries. The ontology and mappings together expose the data in the
sources in the form of an RDF graph, which however is not materialised. Queries, which
can be formulated over the concepts and properties of the ontology, are interpreted over
a virtual RDF graph, and are translated, making use of the mappings, into SQL queries
over the data sources.
In an OBDA/I setting, users simply query the ontology, and no longer need an under-
standing of the data sources, the relation between them, or the encoding of the data. Due
to the presence of an ontology, and of explicitly defined mappings, OBDA technology fa-
cilitates the access and the SPARQL-based exploration of the integrated data, especially
when non-technical end-users are involved. Fig. 1 shows a high-level representation of
4 https://www.w3.org/OWL/
5 http://www.w3.org/TR/owl-profiles/
OBDA/I: A proposal of architecture
KPIs EXPLORER - I
Open Data Government
UNI Project office &
TTO staff
KPIs EXPLORER - II
Higher Education & Research
Private actors &
other agents
Ontology
Federation Layer
KPIs EXPLORER - III
Mappings Unstructured Regional Data
Local Governments &
Public administration
Civil society Proprietary Data
FULL-FLEDGE ACCESS POINT
Compliant SPARQL protocol service
END USERS KPIs INTERACTIVE VISUALISATION ONTOLOGY-BASED DATA DATA SOURCES
TOOLS, QUERY SYSTEMS, … INTEGRATION LAYER
Figure 1. UNiCS (http://university-analytics.com/) platform architecture tailored for the Tuscany’s
Observatory of Research and Innovation. InJOWOthe2017
right-hand side of the image, the list of repositories to be in-
@Bolzano, September 21-23, 2017
tegrated, currently organised according to the Academia, Technology & Innovation, Health care, Public Policy
Making, and Social Sciences and Humanities scenes. The central part of the figure points to the two major
components of the platform: the master ontology and the source(s)-to-target mappings. On the left hand side,
the front-end of the platform, made out of data visualisation tools which are designed and implemented to
answer the information needs of distinct users, and the SPARQL endpoint.
an OBDA/I system for data integration and access developed by SIRIS Academic in the
context the Tuscany Region “Regional Research and Innovation Observatory” project in
Italy, is presented.
3. Concluding remarks
The introduced architecture represents only one possible exploitation of semantic web
technologies and principles to support the current EC vision and strategy on the Re-
search and Innovation policy making. OBDA/I technologies support the actors of the
quadruple helix, who are usually neither computer scientist nor database experts, in look-
ing for interesting correlation and/or patterns in the data, especially when the data are
coming from a multitude of disparate, originally not-homogeneous, data sources. More
concretely, OBDA/I can be used by private and public R&I actors to get:
1. an exact and detailed map of their own current state, including internal pro-
cesses, human resources, skills and research portfolios, technological and eco-
nomic strengths and weaknesses;
2. extensive knowledge of the context in which they are operating, including needs
and requirements of their stakeholders and competitors strategic profiles;
3. a robust decision-making process that ensures priorities are informed and recog-
nised internally as legitimate.
It will be, of course, in charge of the policy makers to then translate the insights com-
ing from the data into applicable and reasonable political actions (such as, research and
innovation investments). Here, we simply tried to convey the message that the introduced
vision and strategy about R&I policy making, may represent an opportunity to further
support the research activity in KR&R, and the consequent development of OBDA/I and
semantic technologies, in the next few years. Strictly speaking, rather than assuming a
passive role and spending further energies in devising ‘Cahier de Doléances on the actual
budgetary austerity’ in the academia, we strongly suggest to opportunistically point out
the pivotal role the semantic- and ontology-based technologies can play in such an arena,
where a multitude of information sources and relevant datasets have to be identified,
consistently integrated, and accessed, for instance, by policy makers, stakeholders, and
domain experts who are not computer scientists. Over the last few years, a multitude of
collaboration raised with the objective of developing ontology-based platforms for data
integration and access in the Italian, Spanish, and French policy making arenas (see, for
instance, [7] and [8]), at different organisational levels. All of them represent successful
experience in the application of semantic technologies for supporting policy making in
research and innovation. Nonetheless, chances to get the ‘ontology-based’ message heard
by the European Commission itself are real nowadays, and the playground is open by
default to all of you, experts in the knowledge representation and semantic technologies
field.
References
[1] H. Etzkowitz and L. Leydesdorff: Universities and the global knowledge economy: a triple helix of
university-industry-government relations. Amsterdam: University of Amsterdam, 1995.
[2] H. Etzkowitz and L. Leydesdorff: The dynamics of innovation: from National Systems and “Mode 2” to
a Triple Helix of universityindustrygovernment relations. Research policy 29(2): 109-123, 2000.
[3] E.G. Carayannis and D.F. Campbell ‘Mode 3’ and ‘Quadruple Helix’: toward a 21st century fractal
innovation ecosystem. International journal of technology management, 46(3-4), 201-234, 2009.
[4] J. Lane, Assessing the Impact of Science Funding, Science 2009.
[5] D. Calvanese, G. De Giacomo, D. Lembo, M. Lenzerini, A. Poggi, M. Rodriguez-Muro and R. Rosati,
R, Ontologies and Databases: The DL-Lite Approach. Reasoning Web, 5689, 255-356, 2009.
[6] F. Baader (Ed.), The description logic handbook: Theory, implementation and applications. Cambridge
university press, 2003.
[7] N. Antonioli, F. Castanò, C. Civili, S. Coletta, S. Grossi, D. Lembo, M. Lenzerini, A. Poggi, D.F. Savo,
E. Virardi, Ontology-Based Data Access: The Experience at the Italian Department of Treasury. CAiSE
Industrial Track 2013: 9-16.
[8] A. Mosca, B. Rondelli, G. Rull, The OBDA-based “Observatory of Research and Innovation” of the Tus-
cany Region. In Proceedings of The Joint Ontology Workshops, JOWO 2017 (WS-CEUR proceedings).
To appear.