Knowledge as CI: Toward Geographic Knowledge Systems Rob Raskin NASA/JPL

Data to Information to Knowledge Data InformationKnowledge Basic ElementsBytes NumbersModelsFacts StorageFile Database GIS FTCOntology Mind VolumeHighLow DensityLowHigh ServicesSave Discover Visualize Overlay Infer Understand Predict SyntaxSemantics

What is Spatial Knowledge? Spatial facts, relations, meanings Content with context  Information is summarized or organized data Common sense Shared understanding Suitable for spatial reasoning/inference Dynamic, expandable knowledge base Requires semantics

Ontology: Knowledge as Cyberinfrastructure Context  Shared understanding of concepts Content  A dictionary readable both by computers and humans Form  A namespace (URL/URI/pointer) containing an authoritative declaration of a concept Authority  Anyone… but the best will win out  We will vote with our fingers

Enterprise-wide Ontology Smart discovery  Google does not understand meaning of terms  Neither does ArcGIS Smart integration Knowledge commons  Assist education

Why point to an ontology? Enable machine-to-machine communication Associate data with its context Remove ambiguity  Temperature anomaly:  relative to what climatological average?  Remotely sensed feature  measured how  Spring (season) or Spring (water source)

Use Case: Global Warming Query Find data which demonstrates global warming at high latitudes during summertime and plot warming rate.  Extract information from the use-case - encode knowledge  Translate this into a complete query for data - inference and integration of data from instruments, indices and models “Global warming”= Trend of increasing temperature over large spatial scales “High latitude”= Latitudes > 60 degrees “Summertime”= June-Aug (NH) and Jan-Mar (SH) “Find data”= Locate datasets using catalogs, then access and read it “Plot warming rate”= Display temperature vs time

Ontology Representations… XML Triples DBMS Visual

Ontology Representation: Triples Subject-Verb-Object representation Flood is a WeatherPhenomena GeoTIFFis aFileFormat Soil Typeis aPhysicalProperty Pacific Oceanis a Ocean Ocean has substanceWater SensormeasuresTemperature

Ontology Representation: Visual Atmosphere AtmosphereLayer Troposphere Tropopause Stratosphere isUpperBoundaryOfisLowerBoundaryOf subClassOf partOf PlanetaryLayer partOf 3DLayer subClassOf upperBoundary =50 km lowerBoundary =15 km primarySubstance =“air” sameAs= “Lower Atmosphere”

Plate tectonics - before Plate Tectonics Ontology

Ontology of an Organization

Ontology Representation: XML

Ontology Languages in XML: RDF and OWL Use of standard languages make it easy to extend (specialize) concepts developed by others  Consistent with how we learn - incrementally  Knowledge passed down to future generations  Continues web paradigm - anyone can be an author W3C languages specialize XML  Resource Description Formulation (RDF)  Standardizes: class, subclass, property, subproperty  Ontology Web Language (OWL)  Standardizes: transitive functions, inverse relations, cardinality, etc. Open world assumption  Facts not explicitly stated are not assumed to be false  In contrast, DBMS world uses closed world assumptions  Tuples are the truth, the whole truth, and nothing but the truth

SWEET Semantic Web for Earth and Environmental Terminology (SWEET) Upper-level concept space for Earth system science Includes concepts of:  Numerics  Space  Time  Earth system science  Data and information  Data and information services

Why use an Upper-Level Ontology? Many common concepts used across spatial disciplines (such as properties of the Earth surface)  Provides common definitions for terms used in multiple disciplines or communities  Provides common language in support of community and multidisciplinary activities  Provides common “properties” (relations) for tool developers Reduced burden (and barrier to entry) on creators of specialized domain ontologies  Only need to create ontologies for incremental knowledge

Non-Living Substances Living Substances Physical Processes Earth Realm Physical Properties Time Natural Phenomena Human Activities Integrative Ontologies Space Data Faceted Ontologies Units Numerics SWEET 1.0 Ontologies

SWEET Spatial Ontologies Objects  0-D, 1-D, 2-D, and 3-D Coordinate systems Relations  Above, inside, adjacent, etc. Fuzzy concepts  “near” Spatial statistics Features

SWEET 2.0 Modular Design Math, Time, Space Basic Science Geoscience Processes Geophysical Phenomena Applications importation Supports easy extension by domain specialists Organized by subject (theoretical to applied) Reorganization of classes, but no significant changes to content No longer one-to-one relation of facet to file

SWEET 2.0 New Features Organized by subject  Makes it easy for domain specialists to add new modules Smaller, modular ontologies  Unidirectional import 12 large ontologies -> 100 small ontologies

SWEET 2.0 Ontologies Abstract To Applied

Characteristic Level of Abstractions Any concept has a characteristic level of abstraction  How theoretical is it? If more than one characteristic level… repeat it  Words are commonly defined in dictionaries with multiple meanings

Community Agreements: How to Optimally Use OWL Reduce quadruples/quintuples to triples  Interval quantities  hasLowerBound, hasUpperBound, hasUnit Fuzzy concepts  “nearlySameAs”  relatedConcept connects siblings or other concepts that might be closely related  similarity matrix provides more precise support for search results ranking Ensembles  pdf representations

Collaborative Ontology Development

Ontology Development Tools: CMAP Free, downloadable tool for knowledge representation and ontology development Visual language with input/export to OWL  Supports subset of OWL language

Resources SWEET  Ontology development/sharing site 