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 demonstrate 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 growing 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 Innovation 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, sectors); 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.
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
relations emerged in the mid ’90s [
The Open Innovation, Open Science, Open to the World - A vision for Europe document1, 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:
1European Commission (2016). Open innovation, open science, open to the world- a vision for Europe. European Commission’s Directorate-General for Research & Innovation (RTD), Bruxelles.
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 [
On December 7-8, 2007, thirty open government advocates gathered in Sebastopol (California) agreed on the following 8 principles characterising an Open Data Government 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 processable: 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 innovative 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 subjectpredicateobject expressions, known as triples; RDFS/OWL2 (”Resource Description Framework Schema”/”Web Ontology Language”): the schema and ontology languages for describing concepts and relationships; SPARQL (SPARQL Protocol and RDF Query Lan2https://europa.eu/european-union/file/1497/download_en?token=KzfSz-CR 3https://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 HTMLbased Web pages); and HTTP communication protocol (”Hypertext Transfer Protocol”): the application protocol for distributed, collaborative, and hypermedia information systems, 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. Semantic Web technologies are formal languages and solutions that bring structure and meaning to information, that adhere to the specific set of W3C open technology standards. 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 recommendations, 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
particular, 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
OntologyBased Data Access and Integration (OBDA/I) [
In an OBDA/I setting, users simply query the ontology, and no longer need an understanding 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 facilitates 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 4https://www.w3.org/OWL/ 5http://www.w3.org/TR/owl-profiles/
OBDA/I: A proposal of architecture Privateactors& otheragents LocalGovernments& Publicadministration
UNIProjectofice&
TTOstaf Civilsociety
KPIs EXPLORER - I KPIs EXPLORER - I KPIs EXPLORER - I I CFoUmLpLli-aFnLtSEPDARGQELpAroCtoCcoElSseSrvPicOeINT
Ontology Mappings itrryLaaeeFneod
Open Data Government Higher Education & Research Unstructured Regional Data
Proprietary Data END USERS
KPIs INTERACTIVE VISUALISATION TOOLS, QUERY SYSTEMS, …
ONTOLOGY-BASED DATA INTEGRATION LAYER
DATA SOURCES 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.
The introduced architecture represents only one possible exploitation of semantic web technologies and principles to support the current EC vision and strategy on the Research and Innovation policy making. OBDA/I technologies support the actors of the quadruple helix, who are usually neither computer scientist nor database experts, in looking 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 processes, human resources, skills and research portfolios, technological and economic 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 recognised internally as legitimate.
It will be, of course, in charge of the policy makers to then translate the insights
coming 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 Dole´ances 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, [