Semantic Technologies for Spatial Infrastructures

An update on progress in Program 3 of the

CRCSI

Professor Geoff West

• Commenced January 2012 • Initial discussions and landscape/literature search • Consultations with 43plers, jurisdictions, agencies, CRCSI, VANZI, ANZSM • Joint ANZLIC/CRCSI meeting, April 2012 • CRCSI Conference, Brisbane, May 2012 • GSDI 2012, Quebec, 2012 • Further literature search of main journals/conferences • Generation of documents:

– Research Strategy – Addressing the milestones

• CRCSI Board – endorsement • Final approval from ANZLIC, PSMA, Program 3 Board (CRCSI), RIC (CRCSI), REC

(CRCSI) • Engagement Workshops – done • Project proposal generation – done • Projects running – now!

Research Strategy Consultation

• Vision: – “Seamless access to the right spatial knowledge at

the right time, in the right format, at the right price”

• Outcomes: – Intelligent search and discovery – Vertical and horizontal linking of data and

processes – Orchestration of web services – Crowd sourced integration – Fast and effective big data querying – Supply chain management and processing

Vision and Outcomes

User demands Core research Test beds Use cases Adoption

From Drivers to Utilisation

Artificial Intelligence

Supply Chains

Semantic Web Web-based

Search

Querying

Crowd Sourcing

Marketplaces

Orchestration

Data Integration

Licensing

PSMA

GA

GLOBES

FSDF

SLIP

LINZ

ASDI

FSDF

SLIP future

Health

Urban planning

Big data queries

Biomass business

Property models

Federal data integration

State data integration

Disaster management

Research activities

Landgate DSE LPI PSMA crowdsourcing

…

Users Catalogues

Ontologies Vocabularies

Metadata

Data & Metadata

High Performance Computing

ABS BoM GA Private Sector

…

Big Data co-located with orchestrated processing, modelling and prediction

Data Data

Data

Data

Modelling Local Orchestrated Processing Search

Querying

Visualisation Inference

Data Integration

Smarts - code

Search

Modelling

LINZ

… …

Existing open software standards and architectures e.g. SISS

Solution: Linking Data and Processes

• Don’t want data, want results – Leave data where it is, run processes in situ – Use web services – Use standards (OGC, W3C, OASIS etc.) – Utilise many online sources

• Want stability of sources – Cool URIs – Persistent URIs

• (www.handle.net, http://dx.doi.org/10.1000/182)

Principles

Semantic Web technology stack visualization (Benjamin Nowack 2011)

Elements of the Semantic Web

Spatial Information Projects

• Funding: $600k per year • Project 3.01 – Search and Discovery, Federated Models

• Research Fellow – Curtin: Dr David McMeekin • Three PhD students – Curtin • Curtin, NGIS, Amristar, PSMA, Landgate (WA), DEPI (VIC) • Co-supervision of students – CSIRO: Simon Cox, Paul Box

• Project 4.17/3.03 – Querying big data, orchestration – 3D/4D datasets • Joint P3/P4 project • Research Fellow – GA • Research Fellow – QUT • QUT, GA, DEPI (VIC), OEH (NSW), DSITIA (QLD)

• Project 3.02 – Supply chains, orchestration, crowd sourcing, licensing • Research Fellow – Curtin: Dr David McMeekin • Research Fellow – Curtin: Dr Lesley Arnold • Three PhD students – Curtin • Three PhD students – Canterbury • Curtin, Canterbury, Landgate (WA), DEPI (VIC), DNRM (QLD), LINZ, PSMA,

Omnilink, NGIS, AAM, OSP • CSIRO involvement

• Project 4.1X – Biomass Business 2: Farmer centric crowd sourcing • UNE, Canterbury

PhD Student Status – April 2014

Original plan!

Original plan!

PhD Student Status – April 2014

The Modern PhD Student!

• Good queries: – “Who should I evacuate

from the flood zone in the next 24 hours?”

– “Am I at risk of death/injury by purchasing this house?”

– “Which houses are unoccupied after the earthquake?”

• Bad query: – Give me all the data you’ve

got, I’ll work it out!

• How about:

Search and Discovery – Tristan

From nbc.com

http://sensorweb.ncsa.uiuc.edu/api/event/tornados?intersects= http://sensorweb.ncsa.uiuc.edu/data/map/state/USGS/Illinois Using Linked Data in a heterogeneous Sensor Web: challenges, experiments and lessons learned Liang Yua & Yong Liub, IJDE, Sept. 2013

• “how does solid water melting influence stream flow in the Arctic Region over the summer?”

P3.01: Search and Discovery - Tristan

Semantic-based web service discovery and chaining for building an Arctic spatial data infrastructure, Li et al (2011), Computers and Geosciences, Vol 37.

event state change

place time

• Can Google find everything? – getcapabilities WMS soil Italy – Google Search Appliance – Voyager – Omniscient – Geonetwork – Swoogle – www.opensearch.org – www.ontology.com – www.govpond.org

• Generate rdf triple stores of derived metadata?

P3.01: Search and Discovery - Chet

P3.01: Federated Data - Jeremy

Given FSDF models • Jurisdictional models examined and

generated • Federate seamlessly from jurisdictions • Leave data where it is • Linked data including fonts, colours

etc.

• Research – models and tools: – Integrate disparate and complex

multiple supply chains – Integrate crowd sourcing into

supply chains and combine with authoritative data

– Model the various crowd sourcing data suppliers

– Model business rules, people – Deal with currency, provenance

and versioning of spatial data – Link data and processes vertically

and horizontally e.g. from LGAs up to the Commonwealth and across different datasets

P3.04: Supply Chains – General

Primary suppliers

Final customers

VIRTUOSO Universal Server: •Middleware and database engine hybrid. •Combines the functionality of a traditional RDBMS, ORDBMS, virtual databases, RDF, XML, free-text, web application server and file server functionality in a single system. •RDF data can either be stored directly in Virtuoso or can be created on the fly from non-RDF relational databases based on a mapping. •SPARQL query statements can be written to fetch the resulting dataset as XML files and then the resulting datasets will be used to analyse model appropriateness and update the source geo-databases.

P3.04: Supply Chains - Latha

Supply Chains – What the Developer Wanted

Collaborative Ontology Generation- Reza

Collaborative Ontology Development for the Geosciences

Collaborative Ontology Development for the Geosciences, Reza Kalbashi et al, Trans. In GIS, 2013.

Linking Geospatial Data to Ontologies - Reza

• Investigate landscape • Model processes, activities, data and links

– UML

• Utilise Semantic Web techniques – URIs – RDF, Ontologies, Rules, Description Logics – Traditional databases, data formats

• Build Tools – Web-based

• Evaluate

Research Methodology

Project Governance

• Spatial data landscape continuously changing • Now have a large team to address many

aspects • Commonwealth and Jurisdictional projects • Use of standards:

– OGC, W3C, OASIS

• Use open software and architectures

Summary