This document discusses an ontology-based data management (OBDM) approach for integrating data for research and innovation policy analysis. The main advantages of the OBDM approach over traditional "silos" approaches are openness, interoperability, and improved data quality. The OBDM approach uses an ontology to provide a common conceptual framework that can be mapped to different data sources to allow integrated access and analysis of the data without merging the sources.

1. E-mail for correspondence: daraio@dis.uniroma1.it
OECD Blue Sky Forum on Science and Innovation Indicators, 19-21 September 2016, Ghent
Key References
Daraio, C., Lenzerini, M., Leporelli, C., Moed, F. H., Naggar, P., Bonaccorsi, A. & Bartolucci, A.
(2016a). Data integration for research and innovation policy: An Ontology-Based Data Management
approach. Scientometrics, 106 (2), 857-871.
Daraio, C., Lenzerini M., Leporelli C., Naggar P., Bonaccorsi A. & Bartolucci, A. (2016b). The
advantages of an Ontology-based Data Management Approach: openness, interoperability and data
quality. Scientometrics, 108 (1), 441-455.
Main advantages with respect to a traditional
silos-based approach
A new approach to data integration for STI
Sapientia and Ontology-Based Data Management (OBDM) as two
Key Enabling Technologies for the development of integrated Data
Platforms for Science, Technology and Innovation (STI) studies
Cinzia Daraio, Maurizio Lenzerini, Claudio Leporelli, Paolo Naggar,
Emanuele Fusco and Alessandro Balducci
Department of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, (Italy)
Illustration of an open OBDM information system. Source: Daraio et al. 2016b.
Acknowledgments
Financial support from the Project Sapienza Awards 2015 n.
6H15XNFS and the Project FILAS RU 2014-1186 is
gratefully acknowledged.
The first version of
Sapientia was closed
the 22 December 2014
and consisted in 14
modules of around 350
symbols.
The current version
Sapientia 2.1 includes
523 concepts, roles
and attributes and is
organized in 10 modules
(see the picture on the
left).
Sapientia: the ontology of Multidimensional Research Assessment
(1) Agents (2) Activities
(3) R&D
(4) Publishing
(5) Education (6) Resources
(7) Review
(8) Taxonomies (9) Space
(10) Time
Sapientia
Sapientia models all the activities relevant for the evaluation of research and for assessing
its impact. For impact, in a broad sense, we mean any effect, change or benefit, to the
economy, society, culture, public policy or services, health, the environment or quality of life,
beyond academia.
Modules of Sapientia 2.1: OBDM for multidimensional R&I Assessment
How an OBDM system is organized?
Main advantages of an OBDM approach over a traditional “sylos”-based approach
(Source: Daraio, Lenzerini et al. 2016a)
Advantage Short Description
Conceptual access to the data Users can access the data by using the elements of the ontology.
Re-usability By making the representation of the domain explicit, we gain re-usability
of the acquired knowledge.
Documentation and
standardization
The mapping layer explicitly specifies the relationships between the
domain concepts and the data sources. It is useful for documentation and
standardization purposes.
Flexibility of the system You do not have to merge and integrate all the data sources at once
which could be extremely costly.
Extensibility of the system You can incrementally add new data sources or new elements (ability to
follow the incremental understanding of the domain) when they become
available.
Opening of the system Provide a conceptual framework which can be used as a common
language by the community.
The three main advantages of OBDM in data integration for research and
innovation analysis (Daraio et al. 2016b), which encompass and further
expand those listed in the previous Table are: openness, interoperability and
data quality.
Declarative database integration
Q1 Q2 Q3
Q1 Q2 Q3
Procedural or bottom-up
(called in gergo silos approach):
For every «indicator need», figure out
which data you need and how they can be
accessed, and design/realize a corresponding
service
Declarative or top-down:
Define a global structure which is valid for all source
data, link this structure to the data, use this structure
to specify the «indicator needs» and automatically
extract the right data from the sources
Ontology Database schema
OBDM
OBDM (Ontology-Based Data Management):
A new declarative paradigm for STI data integration
and governance
‒ Use knowledge Representation and Reasoning
principles and techniques for managing data.
‒ Leave the data where they are.
‒ Build a conceptual specification of the domain.
‒ Map such knowledge structure to concrete data sources
‒ Express all the indicators over the abstract representation
‒ Automatically translate conceptual indicators to data
Overcoming the «silos» approach to data integration:
OBDM a new declarative paradigm for STI data
integration and management!