Transformative characteristics and research agenda for the SDI-SKI step change: A Cadastral Case Study

Transcription

1 Transformative characteristics and research agenda for the SDI-SKI step change: A Cadastral Case Study Dr Lesley Arnold Research Fellow, Curtin University, CRCSI Director Geospatial Frameworks World Bank Consultant

2 Presentation Outline Limitations of SDIs Transformative Characteristics Explain methodologies case study SKI Architecture and Framework

3 The On-demand Generation Produced by La Reel House Media

4 SIRI has limitations Problem lies with how we organise our data

5 On-Demand Limitations The Query Questions are multifaceted Answers are context dependent Unable to be predicted

6 Limitations - Forecasting Demand Decoupling Point Push Model Pull Model Just in case Just enough Just in time Just in real-time Just for me Disaster Management Foundation Spatial Data Themes High Demand Data Themes and Areas Data as a Service Knowledge

7 On-Demand Limitations The SDI Data not machine-accessible Designed for Data-IN Data integration difficult

8 Limitations Web of Data Open Data Portal Spatial Data Infrastructure

9 On-Demand Limitations The Data Duplicate information Aggregated data = LCD Provenance unknown

10 On-Demand Revolution The Smart Phone Revolution in connectivity has created an On-demand Economy Access is now more important than ownership - Futurist Kevin Kelly

11 Transformative Characteristics SDI SKI When combined: Knowledge on Demand

12 Cadastre Vision A cadastral system that enables people to readily and confidently identify the location and extent of all rights, restrictions and responsibilities related to land and real property.

13 Cadastre Vision

14 Semantic Web Making Data Smart Third stage in the Evolution of the Web Web 1.0 Web 3.0 Web 2.0 One-way interaction Two-way participation Linked Data (Semantic Web)

15 Semantic Web (Web of Data) Data Provenance Domain Ontologies End-user Profiles Knowledge Core Data structure Data formats Data Core Sensor feeds Images and Videos Upper Ontologies Vocabularies Rule-bases Metadata URI/URL/URN Knowledge Inferencing WMS, WFS, WPS Process Ontologies Process Core Semantic Search Geoprocesses Natural Language Processing Semantic Queries Ranking and Rating Spatial analytics

16 Query Processing

17 SDI Data Access Focussed SKI Knowledge Discovery Focussed

18 Research Agenda

19 Query Processing The Building blocks Knowledge Data Process

20 Data: Building Blocks Resource Description Framework Subject Predicate Object Property Land Parcel locatedwithin PartOfPremise locatedwithin Premise RDF is a standard model for data interchange on the Web.

21 Data: Building Blocks Resource Description Framework URI URI Land Parcel hasparceladdress LocatedWithinParcel LocatedWithinParcel Property hasparceladdress associatedwithpremise Premise URI URI Parcel Address URI Street Address URI Premise Address RDF Schema are used to build ontologies

22 Data: Building Blocks Unique Resource Identifier Shopping Centres URI unambiguously identifies a data resource so it can be found

23 Data: Building Blocks Provenance Electronic Survey Plan Land Authority Cadastre (Data fudged) Utilities Cadastre (Inconsistent) Nationally Aggregated Cadastre (LCD) Generalised Cadastre (Land use map)

24 Knowledge: Building Blocks Vocabularies Controlled vocabularies agreed terms used to support data integration

25 Knowledge: Building Blocks Ontologies and Rules Rights, Restrictions and Responsibilities Person ownsland Land Parcel has has has Rights Restrictions Mining EPA Grants Building Water Chemical use Bores Days RULES: One bore per land parcel RULES: Street Address Ending in 1 = Sat, Wed has Parcel Address Responsibilities Heritage sites Drainage Electric Poles Ontologies set of concepts in a subject area

26 Process: Building Blocks Natural Language Processing What responsibilities are associated with this land? Refer to RRR Ontology Parcel, Property, Premise, Address Planar topology Within Current GPS location Polygon feature NLP Breaks down the query into manageable units

27 Knowledge: Building Blocks Semantic Search and Spatial Filtering Natural Language Processing Ontology Search Contextual Analysis Reasoning Engines Semantic Search understand searchers intent using contextual meanings Spatial Filtering narrowing the search by location

28 Process: Building Blocks Process Ontologies Identify data resources Shareable Heritage Sites Cadastre/Ownership Geo-reference Watering Days Land Use Identify Property Point in Polygon Spatial Intersect Process Ontology knowledge required to automatically compile, coordinate and run a series of geoprocess

29 Process: Building Blocks Geoprocesses CONFLATION FEDERATION Conflate single best dataset from multiple sources Federate joining adjacent datasets

30 What responsibilities are associated with this land Steps 1-6 Query Processing Step 1: Interpret Step 2: Retrieve Step 3: Process ONT PROV DATA START Input Query Executed Query Decomposed Semantic Search Spatial Filtering Process Ontology Process Orchestration

31 Steps 1-6 Query Processing Step 1: Interpret Step 2: Retrieve Step 3: Process ONT PROV DATA Provide More Context START Input Query Executed End-user Profile Query Decomposed Semantic Search PROV Spatial Filtering Process Ontology Process Orchestration Report, graph, map, image, video etc END Deliver Knowledge Rate Results Rank Results Portray Results Step 6: Deliver Step 5: Rank and Rate Step 4: Portray

32 Web of Data (Semantic Web) KNOWLEDGE DATA Query Processing and Spatial Analytics Open Data Portals Linked Open Data PROCESS TRUST (Ranking and Rating)

33 The SKI is not a centralised service or distinct entity. The SKI embodies the behaviour of resources available in the Web of Data.

34 Architecture

35 SKI Framework

36 Spatial Knowledge Infrastructure For more information Download the White Paper at

37 Thank you