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Semantically Mapping Science (SMS) Platform

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

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Semantically Mapping Science (SMS) Platform

  1. 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. 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. 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. 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. 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. 6. Semantically Mapping Science (SMS) Platform: http://sms.risis.eu 6 SMS Platform Data Architecture
  7. 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. 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. 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. 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. 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. 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. 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. 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. 15 DEMO Using the platform: http://sms.risis.eu
  16. 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. 17. Semantically Mapping Science (SMS) Platform: http://sms.risis.eu 17 SMS Platform Any questions? comments?

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