Semantically Mapping Science (SMS) Platform presentation at SemSci2017 workshop: ISWC2017

1. Semantically Mapping Science (SMS) Platform
Ali Khalili, Peter van den Besselaar*, Al Koudous Idrissou, Klaas Andries de Graaf and Frank van
Harmelen
*Department of Organization Sciences, Faculty of Social Science
Department of Computer Science, Faculty of Sciences
SemSci 2017: Enabling Open Semantic Science
October 2017, Vienna

2. Semantically Mapping Science (SMS) Platform: http://sms.risis.eu 2
RISIS Project risis.eu
build a distributed
research infrastructure to
advance Science,
Technology & innovation
studies.

3. Semantically Mapping Science (SMS) Platform: http://sms.risis.eu
• Many heterogeneous data in the social sciences
• Linking -> More units of analysis; More variables
• For many research questions, approaches
• Challenges
• Linking of heterogeneous data -> big data network
• Enriching data
• Using links and enriched data for quality control and quality
improvement
• Including links to proprietary and confidential data
• Usability: understanding the data (availability, quality), browsing,
selecting, retrieving
3
SMS Platform CHALLENGE Goal

4. Semantically Mapping Science (SMS) Platform: http://sms.risis.eu
• Challenges
• Linking of heterogeneous data -> big data network
• Enriching data
• Using links and enriched data for quality control and
quality improvement
• Including links to proprietary and confidential data
• Usability:
• For many research questions, approaches
• Understanding the data (availability, quality),
• Browsing, selecting, retrieving
4
SMS Platform CHALLENGE Goal

5. Semantically Mapping Science (SMS) Platform: http://sms.risis.eu 5
SMS Platform Focus Goal
How to capture new insights
by integrating data from
multiple heterogeneous data
sources in the STI domain?

6. Semantically Mapping Science (SMS) Platform: http://sms.risis.eu 6
SMS Platform Data Architecture

7. Semantically Mapping Science (SMS) Platform: http://sms.risis.eu 7
SMS Platform Modules
• Data Curation
• Browsing and Querying
• Data Enrichment
• Data Linking
Core Modules

8. Semantically Mapping Science (SMS) Platform: http://sms.risis.eu 8
SMS Platform Linked Entities
ORGANIZATIONS PROJECTS
GEO
LOCATIONS
GEO
BOUNDARIES
GEO
STATISTICAL
DATA
PUBLICATIONS
FUNDING
PROGRAMS
PATENTS
PERSONS
ORGANIZATION
RANKINGS
Geo
Geo
Geo
OTHER
ENTITY TYPES
Conceptual Model

9. Metadata Editor
Semantically Mapping Science (SMS) Platform: http://sms.risis.eu 9
SMS Platform Applications
http://datasets.risis.euhttps://youtu.be/p_2D3ydcx1U?list=PLSBPxopOi20XPOn1sGBthbNtXIUOqM_4b
Services

10. Adaptive Multigraph-based Faceted Browser
Semantically Mapping Science (SMS) Platform: http://sms.risis.eu 10
SMS Platform Applications Browsing Data
Khalili, P. van Andel, P. van den Besselaar, and K. A. de Graaf,
“Fostering serendipitous knowledge discovery using an adaptive multigraph-based faceted browser,”
in The ninth international conference on knowledge capture: k-cap 2017
Based on LD-R framework (http://ld-r.org)
Demo: https://youtu.be/9TMLKdGZExY

11. Data Enrichment
Semantically Mapping Science (SMS) Platform: http://sms.risis.eu 11
SMS Platform Applications Services
Geo-Enrichment-APIs
Named Entity Recognition UI
Google Spreadsheet Add-on

12. Data Linking using Lenticular Lenses
(Is my:sameAs the same as your:sameAs ?)
Semantically Mapping Science (SMS) Platform: http://sms.risis.eu 12
SMS Platform Applications Services
A. K. Idrissou, R. Hoekstra, F. van Harmelen, A. Khalili, and P. van den Besselaar, “Is my:sameAs the same
as your:samAas?,” in The ninth international conference on knowledge capture: k-cap 2017

13. 13
USE CASE:
- Question: Which characteristics of universities and of the geo-environment predict the
performance of universities?
- Other research intensive orgs in the environment (labor market, collaboration, funding)
- Creative population (heterogeneity, singles, age)
- University characteristics (fields covered, budget, student population, grad/undergrad,
staff/student)

14. 14
DATA-DECOMPOSITION
• Entity = Organization many linked datasets
• Variables:
• Ranking (performance) Leiden ranking
• High tech / knowledge intensive organizations GRID
• Characteristics university (size, student population, staff/student
ratio, budget, fields) ETER
• Characteristics of the local environment (number and variety of
knowledge intensive organizations; population age/heterogeneity)
Statistics Netherlands

15. 15
DEMO
Using the platform: http://sms.risis.eu

16. 16
RESULTS
• Four partial datasets; Integration is easy
• Linked through ID universities and through the selected geo-boundary
• For each university (apart from missing values)
• Number of knowledge intensive organizations in area (GRID)
• Total, type, Variety
• Population in the environment (Statistics NL)
• Age, single, minorities
• Characteristics universities (ETER)
• Nr students, gender, nr staf, etc(
• Performance (Leiden Ranking)
• Top cited papers
• The produced query can be used for extending the scope / size

17. Semantically Mapping Science (SMS) Platform: http://sms.risis.eu 17
SMS Platform
Any questions? comments?