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Towards a Vocabulary for Incorporating

Predictive Models into the Linked Data Web
Evangelos
Kalampokis, AretiKaramanou, EfthimiosTambouris, K
onstantinosTarabanis

http://islab.uom.gr
© Information Systems Lab- 2013
Objective
 “To propose an RDF Schema vocabulary, named the Linked
Statistical Models (limo) vocabulary, that will enable the
incorporation of descriptions of predictive models into the Linked
Data Web and establish links to other resources such as
datasets, other models, academic articles and studies.”

© Information Systems Lab, University of Macedonia
© Information Systems Lab, University of Macedonia
Data analytics
 Easy access to large
amounts of data
 Combine data and
perform data analytics

 Create statistical or
data mining models
for understanding and
describing various
problem areas and
domains
http://www.flickr.com/photos/seandreilinger/2587606107
© Information Systems Lab, University of Macedonia
E. Kalampokis, E. Tambouris and K. Tarabanis (2013) Linked Open Government Data Analytics, M.A. Wimmer, M. Janssen, and
H.J. Scholl (Eds.): EGOV 2013, LNCS 8074, pp. 99-110. IFIP International Federation for Information Processing.
© Information Systems Lab, University of Macedonia
© Information Systems Lab, University of Macedonia

6
Controversial results
 11 models aiming at
predicting elections
results using Social Media
(SM) related variables
 Only 3 of them included
sentiment related
variables
 Only 1 of them employed
predictive analytics
evaluation methods

http://www.flickr.com
/photos/tonythemisfit
/3023871670/

 6 supported SM predictive
power while 5 challenged it

http://www.flickr.com/photos/cainandtoddbenson/717520970
E. Kalampokis, E. Tambouris and K. Tarabanis (2013) Understanding the Predictive Power of Social Media, Internet
Research, Vol.23, No.5, pp. 544-559
© Information Systems Lab, University of Macedonia
Understanding the predictive power of SM
 52 empirical studies that
exploit Social Media for
predictions
 The predictive power of a
model is directly related to:
– Selected predictors
– Statistical or data mining
method used
– Evaluation method
employed

– Datasets selected
– Approaches used to
collect, filter and process
E. Kalampokis,data
E. Tambouris and K. Tarabanis (2013) Understanding the Predictive Power of Social Media, Internet
Research, Vol.23, No.5, pp. 544-559
© Information Systems Lab, University of Macedonia
 Why don’t we reuse all this information?

http://www.flickr.com/photos/pagedooley/8435953365
© Information Systems Lab, University of Macedonia
Social Media Data Analysis Process for Predictive Analytics

E. Kalampokis, E. Tambouris and K. Tarabanis (2013) Understanding the Predictive Power of Social Media, Internet
Research, Vol.23, No.5, pp. 544-559
© Information Systems Lab, University of Macedonia
Reuse of Descriptions of Predictive Models
 Discover variables that a predictive relationship between them have
been suggested by a model
 Discover predictor variables that are connected to the same
response
 Discover statistical or data mining methods used in certain cases
 Discover datasets used or could be reused in existing or new models
 Discover predictive models that could be reused (e.g. for baseline
predictions or with different data)

 This is where Linked Data comes in …
© Information Systems Lab, University of Macedonia
Predictive
Models

http://www.flickr.com/photos/mbiskoping/6075387388
© Information Systems Lab, University of Macedonia
A vocabulary for describing predictive models as Linked Data
 Asimple vocabulary that enables the creation of description of
predictive models based on linked data principles

© Information Systems Lab, University of Macedonia
Relevant endeavors - PMML
 The Predictive Model Markup Language (PMML) is a standard for
XML documents which express trained instances of analytic models
 Main goal: cross-platform interoperability
 PMML contains over 700 elements

© Information Systems Lab, University of Macedonia
© Information Systems Lab, University of Macedonia
PMML’s Model Element

© Information Systems Lab, University of Macedonia
Linked Statistical Models Vocabulary (LIMO)
 LIMO will enable the creation of predictive models descriptions
adhering to the Linked Data principles
 First unofficial draft in:
– http://www.purl.org/limo-ontology/limo

© Information Systems Lab, University of Macedonia
http://purl.org/limo-ontology/limo
© Information Systems Lab, University of Macedonia
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., Brilliant, L.: Detecting inuenza epidemics using
search engine query data. Nature, 457(7232), 1012{4 (2009)
© Information Systems Lab, University of Macedonia
© Information Systems Lab, University of Macedonia
Future Work
 Finalize the model
 Create a dataset with predictive models described using LIMO
 Develop LIMO descriptions exporter

© Information Systems Lab, University of Macedonia
Future Work
 OpenCube: Publishing and Enriching Linked Open Statistical Data for
the Development of Data Analytics and Enhanced Visualization
Services
 FP7-ICT-2013-SME-DCA No 611667
 Start date: 1 November 2013
 Duration: 24 months

http://www.flickr.com/photos/stuckincustoms/524185543
© Information Systems Lab, University of Macedonia
© Information Systems Lab, University of Macedonia
Thank you for your attention!!
http://kalampok.is

© Information Systems Lab, University of Macedonia

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Towards a Vocabulary for Incorporating Predictive Models into the Linked Data Web

  • 1. Towards a Vocabulary for Incorporating Predictive Models into the Linked Data Web Evangelos Kalampokis, AretiKaramanou, EfthimiosTambouris, K onstantinosTarabanis http://islab.uom.gr © Information Systems Lab- 2013
  • 2. Objective  “To propose an RDF Schema vocabulary, named the Linked Statistical Models (limo) vocabulary, that will enable the incorporation of descriptions of predictive models into the Linked Data Web and establish links to other resources such as datasets, other models, academic articles and studies.” © Information Systems Lab, University of Macedonia
  • 3. © Information Systems Lab, University of Macedonia
  • 4. Data analytics  Easy access to large amounts of data  Combine data and perform data analytics  Create statistical or data mining models for understanding and describing various problem areas and domains http://www.flickr.com/photos/seandreilinger/2587606107 © Information Systems Lab, University of Macedonia
  • 5. E. Kalampokis, E. Tambouris and K. Tarabanis (2013) Linked Open Government Data Analytics, M.A. Wimmer, M. Janssen, and H.J. Scholl (Eds.): EGOV 2013, LNCS 8074, pp. 99-110. IFIP International Federation for Information Processing. © Information Systems Lab, University of Macedonia
  • 6. © Information Systems Lab, University of Macedonia 6
  • 7. Controversial results  11 models aiming at predicting elections results using Social Media (SM) related variables  Only 3 of them included sentiment related variables  Only 1 of them employed predictive analytics evaluation methods http://www.flickr.com /photos/tonythemisfit /3023871670/  6 supported SM predictive power while 5 challenged it http://www.flickr.com/photos/cainandtoddbenson/717520970 E. Kalampokis, E. Tambouris and K. Tarabanis (2013) Understanding the Predictive Power of Social Media, Internet Research, Vol.23, No.5, pp. 544-559 © Information Systems Lab, University of Macedonia
  • 8. Understanding the predictive power of SM  52 empirical studies that exploit Social Media for predictions  The predictive power of a model is directly related to: – Selected predictors – Statistical or data mining method used – Evaluation method employed – Datasets selected – Approaches used to collect, filter and process E. Kalampokis,data E. Tambouris and K. Tarabanis (2013) Understanding the Predictive Power of Social Media, Internet Research, Vol.23, No.5, pp. 544-559 © Information Systems Lab, University of Macedonia
  • 9.  Why don’t we reuse all this information? http://www.flickr.com/photos/pagedooley/8435953365 © Information Systems Lab, University of Macedonia
  • 10. Social Media Data Analysis Process for Predictive Analytics E. Kalampokis, E. Tambouris and K. Tarabanis (2013) Understanding the Predictive Power of Social Media, Internet Research, Vol.23, No.5, pp. 544-559 © Information Systems Lab, University of Macedonia
  • 11. Reuse of Descriptions of Predictive Models  Discover variables that a predictive relationship between them have been suggested by a model  Discover predictor variables that are connected to the same response  Discover statistical or data mining methods used in certain cases  Discover datasets used or could be reused in existing or new models  Discover predictive models that could be reused (e.g. for baseline predictions or with different data)  This is where Linked Data comes in … © Information Systems Lab, University of Macedonia
  • 13. A vocabulary for describing predictive models as Linked Data  Asimple vocabulary that enables the creation of description of predictive models based on linked data principles © Information Systems Lab, University of Macedonia
  • 14. Relevant endeavors - PMML  The Predictive Model Markup Language (PMML) is a standard for XML documents which express trained instances of analytic models  Main goal: cross-platform interoperability  PMML contains over 700 elements © Information Systems Lab, University of Macedonia
  • 15. © Information Systems Lab, University of Macedonia
  • 16. PMML’s Model Element © Information Systems Lab, University of Macedonia
  • 17. Linked Statistical Models Vocabulary (LIMO)  LIMO will enable the creation of predictive models descriptions adhering to the Linked Data principles  First unofficial draft in: – http://www.purl.org/limo-ontology/limo © Information Systems Lab, University of Macedonia
  • 19. Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., Brilliant, L.: Detecting inuenza epidemics using search engine query data. Nature, 457(7232), 1012{4 (2009) © Information Systems Lab, University of Macedonia
  • 20. © Information Systems Lab, University of Macedonia
  • 21. Future Work  Finalize the model  Create a dataset with predictive models described using LIMO  Develop LIMO descriptions exporter © Information Systems Lab, University of Macedonia
  • 22. Future Work  OpenCube: Publishing and Enriching Linked Open Statistical Data for the Development of Data Analytics and Enhanced Visualization Services  FP7-ICT-2013-SME-DCA No 611667  Start date: 1 November 2013  Duration: 24 months http://www.flickr.com/photos/stuckincustoms/524185543 © Information Systems Lab, University of Macedonia
  • 23. © Information Systems Lab, University of Macedonia
  • 24. Thank you for your attention!! http://kalampok.is © Information Systems Lab, University of Macedonia