Next Generation Semantic Data Environments (or Linked Data, Semantics, and Standards in Scientific Applications)

Transcription

1 Next Generation Semantic Data Environments (or Linked Data, Semantics, and Standards in Scientific Applications) Deborah L. McGuinness Tetherless World Senior Constellation Chair Professor of Computer and Cognitive Science Web Science Research Center Director Rensselaer Polytechnic Institute, Troy, NY With thanks to the extended RPI Tetherless World Team OMG Semantics : From Research to Reality: Implementing the Semantic Web March 20, 2013 Reston, VA

2 Trends: More Data & More Diversity 2 More data More open data More authoritative data More interest in and generation of metadata More enthusiast generated / maintained data More vocabularies, taxonomies, ontologies More diversity Broader human participation Trained scientists, citizens, enthusiast, indigenous, More locations mobile as well as global More sensors human, robots, implants, Real time feeds Social sources Twitter, Facebook,

3 Increasing Requirements Data and data environments should: Support usability not just by original authors Include (usable) documentation - meta data concerning collection methods, sources, recency, assumptions, Provide accessibility with transparent access policies Include schema / ontology information including mapping information used in integration along with rationales. Support queries (with usable and understandable interfaces) Document verification and curation methods, including access to tools Support AND encourage interactions; users should be able to comment, question, contribute, discuss,. Path moves from Portal -> Virtual Observatory -> Online Community Next: examples, foundations, and discussion 3

4 Enable/Empower citizens & scientists to explore pollution sites, facilities, regulations, and health impacts along with provenance Demonstrates semantic monitoring possibilities Extend to endangered species and resource mgr issues Explanations and Provenance available Semantic Environmental and Ecological Monitoring and 1. Map view of analyzed results 2. Explanation of pollution 3. Possible health effect of contaminant (from EPA) 4. Filtering by facet to select type of data 5. Link for reporting problems 6. Extended with input from USGS, with population counts for birds & fish 5 4

5 Example Workflow (SemantAqua) Publish CSV2RDF4LOD Direct derive visualize derive archive Archive CSV2RDF4LOD Enhance 5

6 Reusable Ontologies Pollution ontology describes the relationship between a regulation violation (a measurement), a polluted thing, and a polluted site Combined with other ontologies (e.g. W3C Geo) users can ask Tell me all of the polluted things within 1 mile of my location 6

7 Ontologies Water quality ontology extends pollution to describe water-related pollution Further extended by regulation ontologies to provide regulation violation inference Allows the reasoner to match specific regulations to measurements that violate them 7

8 Interface 8

9 Semantic Methodology and Semantic Application Evolution 9 SemantAqua -> SemantEco -> DataOne modularizing, broadening, provenance, interaction Originally developed for Virtual Observatories (in solar terrestrial), now in water quality, Sea ice, volcanology, mycology,. McGuinness, Fox, West, Garcia, Cinquini, Benedict, Middleton The Virtual Solar-Terrestrial Observatory: A Deployed Semantic Web Application Case Study for Scientific Research. Proc. 19 Conf. on Innovative Applications of Artificial Intelligence (IAAI-07), VSTO -> SESDI -> SPCDIS - modularizing, provenance, broadening, interaction

10 Population Sciences Grid: Interventions, Behaviors, and Policy Extensible Mashups via Linked Data Diverse datasets from NIH Exploring Interventions along with correlations with behavior changes - in this case tobacco interventions and smoking prevalance Accountable Mashups via Provenance Award winning paper on multi-dimensional analysis 10

11 An Example: Hawaii Changes in cigarette use viewed against policy changes We link states from year to year to that state across time, adding data for each year. 11

12 Ontology as API: Adding Dimensions This RDF: Creates this visual: graph dataset x axis y axis 12

13 13 Social Observatory First Responder effort (NIST funded) Social Media use is on the rise. Every day, we write: 294 billion s 2 million blog posts Over 40 Million Tweets* Finding Users First Responders, including Emergency Medical Personnel, Firefighters, and Police Officers, have active online communities on Social Media websites. How can we leverage Social Media sites to gather requirements for active First Responders? to identify stakeholders within those First Responder communities? Finding Topics

14 14 Web Data Challenge Response Enablers - HHS Award winning platform - Target questions: good hospital for my context - Prizm, DataCube Explorer,

15 Open Government Data TWC Intl Open Government Data Sets

16 Mobile, Distributed, and Context- Aware Computing

17 Rensselaer Tetherless World Constellation Web Observatory Foundations & Directions HEMES Observatories: Science, Open Government, Health and Life cience, Social Web Science Research Foundations Making Data Transparent and Actionable Provenance Semantic Methodology Social Network Analysis Semantically-Enabled Visualization Web Data "Challenge Response" Enablers Open Data Workflow Multi-Dimensional Data Portals Social Media: Reasoning on Graph Database International Open Government Data Sets Health and Human Services Data Challenge First Responder Network Semantic escience Data Portals

18 Foundations: Web Layer Cake Inference Web, Proof Markup Language, W3C Provenance Working group formal model, W3C incubator group, OWL 1 & 2 WG Edited main OWL Docs, quick reference, OWL profiles (OWL RL), Earlier languages: DAML, DAML+OIL, Classic SPARQL WG, earlier QL OWL-QL, Classic QL, Visualization APIs S2S Govt Data Inference Web IW Trust, Air + Trust DL, KIF, CL, N3Logic Ontology repositories (ontolinguag), Ontology Evolution env: Chimaera, Semantic escience Ontologies, MANY other ontologie RIF WG AIR accountability tool Govt metadata search Linked Open Govt Data SPARQL to Xquery translator RDFS materialization (Billion triple winner) Transparent Accountable Datamining Initiative (TAM

19 Inference Web: Making Data Transparent and Actionable Using Semantic Technologies How and when does it make sense to use smart system results & how do we interact with them? Knowledge Provenance in Virtual Observatories (Mobile) Intelligent Agents Intelligence Analyst Tools -> Watson Cognitive Asst -> CPOF & SIRI NSF Interops: SONET SSIII Sea Ice Hypothesis Investigation / Policy Advisors 19

20 Moving to the Next Generation 20 Some focus areas to move to the next generation: Provenance e.g., not just the sources, and dates but enough to know when to depend on something. Policy balance between sharing data, getting credit, making data accessible to all (or all willing to follow the rules Social aspects incentives, rewards, evolution, customization Distributed, Mobile, and Context-aware Education scientific method - promote creating testable hypotheses, how to verify/ replication, etc. Broadly usable semantic methodology Moving to truly integrated communities

21 Discussion Semantic foundations are being used in a wide range of areas. They are not just for semantic practioners any more Open as well as commercial software available Come join us! And if you are already there What do you want from evolving observatory / collaboratory infrastructure? What do you need from provenance and explanation infrastructures? Do you have tools, tool templates, and/or tool requirements? Do you have use cases? Are you using our (or another) semantic methodology? More info Deborah McGuinness dlm@cs.rpi.edu

22 Extra 22

23 Semantic Web (RPI) 2013 Research Innovatio n RDFa

24 What is an Ontology? Catalog/ ID Thesauri narrower term relation Formal is-a Frames General (properties) Logical constraints Terms/ glossary Informal is-a Formal instance Value Restrs. Disjointness, Inverse, part-of Ontologies Come of Age McGuinness, 2001, and From AAAI Panel 99 McGuinness, Welty, Uschold, Gruninger, Lehmann Plus basis of Ontologies Come of Age McGuinness, 2003

25 Interface 25

26 used by Core and Framework Semantics - Multi-tiered interoperability