Semantics and analytics = making the data and the decisions smarter?

1. Semantics and analytics = making the data and the decisions smarter? Digital Antiquity CI Feb 7-8, 2013, Arlington VA Peter Fox (RPI and WHOI) pfox@cs.rpi.edu, @taswegian, http://tw.rpi.edu/web/person/PeterFox Tetherless World Constellation http://tw.rpi.edu and AOP&E

2. Analytics – data and visual

3. Producers Consumers Experience • Analytics Ecosystem • Stimulate Innovation Research Exploration Discovery Data Information Knowledge Creation Gathering Presentation Organization Integration Conversation Context 4

4. Data as Infostructure

5. Curation for analytics Producers Consumers Quality Control Quality Assessment Fitness for Purpose Fitness for Use Trustor Trustee Others… Others…

6. Technical advances From: C. Borgman, 2008, NSF Cyberlearning Report

7. Working with knowledge Rule execution Expressivity Implement -ability Query Inference Maintainability/ Extensibility

8. For real discovery – we need abduction! - a method of logical inference introduced by C. S. Peirce which comes prior to induction and deduction for which the colloquial name is to have a "hunch” • Importantly - human intuition is needed in interacting with large-scale data

9. Yes, we need a Knowledge Base

10. Smart visual exploration

11. Semantics - Modern informatics enables a new scale-free** framework approach • Use cases • Stakeholders • Distributed authority • Access control • Ontologies • Maintaining Identity

12. Finally • Significant opportunities for smart data-as-a-service approaches to ‘scale’ for big data (on the web) • Delivering ‘products’ allows analytics on the back end, but tools to plug into a framework are lacking • Exploit late semantic binding for ABDUCTION • Next generation analytics must accommodate: abduction, translucency, interactivity and retain what they do well! • So we all need to get cracking! • Thanks. @taswegian, pfox@cs.rpi.edu

13. Back shed

14. 1: Integrating Multiple Data Sources • The Semantic Web lets us merge statements from different sources • The RDF Graph Model allows programs to use data uniformly regardless of the source • Figuring out where to find such data is a motivator for Semantic Web Services #Ionosphere hasCoordinates #magnetic name hasLowerBoundaryValue “100” “Terrestrial Ionosphere” hasLowerBoundaryUnit “km” Different line & text colors represent different data sources Fox & McGuinness Semantic Technologies May 21, 2007

15. 2: Drill Down /Focused Perusal • The Semantic Web uses Uniform Resource Identifiers (URIs) to name things • These can typically be resolved to get more information about the resource • This essentially creates a web of data analogous to the web of text created by the World Wide Web • Ontologies are represented using the same structure as content • We can resolve class and property URIs to learn about the ontology …#NeutralTemperature …#Norway Internet locatedIn measuredby ...#ISR ...#FPI type operatedby …#EISCAT ...#MilllstoneHill Fox & McGuinness Semantic Technologies May 21, 2007

16. 3: Statements about Statements • The Semantic Web allows us to make statements about statements • Timestamps • Provenance / Lineage • Authoritativeness / Probability / Uncertainty • Security classification • … • This is an unsung virtue of the Semantic Web #Danny’s #Aurora hasSource hasDateTime hascolor 20031031 Red Ontologies Workshop, APL May 26, 2006 Fox & McGuinness Semantic Technologies May 21, 2007

17. 8: Proof hasCalibration • The logical foundations of the Semantic Web allow us to construct proofs that can be used to improve transparency, understanding, and trust • Proof and Trust are on-going research areas for the Semantic Web #FlatField #Critical Dataset hasPeerReview #Solar Physics Paper “Critical Dataset has been calibrated with a flat field program that is published In the peer reviewed literature.” Fox & McGuinness Semantic Technologies May 21, 2007

18. Knowledge representation • Statements as triples: {subject-predicate-object} interferometer is-a optical instrument Fabry-Perotis-a interferometer Optical instrumenthas focal length Optical instrument is-ainstrument Instrumenthas instrument operating mode Instrument has measured parameter Instrument operating modehas measured parameter NeutralTemperatureis-atemperature Temperature is-aparameter • A query*: select all optical instruments which have operating mode vertical • An inference: infer operating modes for a Fabry-Perot Interferometer which measures neutral temperature • ISWC paper award 2006, IAAI best paper (2007), Fox et al. 2009 in Computers and Geosciences.

19. Visual discovery

20. Traversal for new patterns

21. However - Skill/ tools?

22. Summary • Get the data well structured! Be aware of the distinctions between data, information, knowledge. • Develop multi-domain KBs • Use the standards, and tools that are available • Get familiar with semantic technology but do not let it drive what you explore

23. And… • Frameworks more than systems • Leverage semantic methodologies that are shown to work/ be useful • Vocabulary development … by communities, leverage what you have and for the things that matter • Exploit late semantic binding for ABDUCTION