Developing Ontologies (and more) Peter Fox (NCAR) ESIP Winter Meeting (TIWG) January 9, 2008, Washington, D.C.

Ontology Spectrum Thesauri “narrower term” relation Frames Selected Logical Constraints (disjointness, inverse, …) Frames (properties) Formal is-a Catalog/ ID Informal is-a Formal instance General Logical constraints Terms/ glossary Value Restrs. Originally from AAAI 1999- Ontologies Panel by Gruninger, Lehmann, McGuinness, Uschold, Welty; – updated by McGuinness. Description in: www.ksl.stanford.edu/people/dlm/papers/ontologies-come-of-age-abstract.html

Ontology - declarative knowledge The triple: {subject-predicate-object} interferometer is-a optical instrument Fabry-Perot is-a interferometer Optical instrument has focal length Optical instrument is-a instrument Instrument has instrument operating mode Data archive has measured parameter SO2 concentration is-a concentration Concentration is-a parameter

Semantic Web Layers http://www.w3.org/2003/Talks/1023-iswc-tbl/slide26-0.html, http://flickr.com/photos/pshab/291147522/

Terminology Ontology (n.d.). The Free On-line Dictionary of Computing. http://dictionary.reference.com/browse/ontology An explicit
formal specification of how to represent the objects, concepts
and other entities that are assumed to exist in some area of
interest and the relationships that hold among them. Semantic Web An extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation, www.semanticweb.org Primer: http://www.ics.forth.gr/isl/swprimer/ Languages OWL 1.0 (Lite, DL, Full) - Web Ontology Language (W3C) RDF - Resource Description Framework (W3C) OWL-S/SWSL - Web Services (W3C) WSMO/WSML - Web Services (EC/W3C) SWRL - Semantic Web Rule Language, RIF- Rules Interchange Format Editors: Protégé, SWOOP, CoE, VOM, Medius, SWeDE, … Languages: OWL RDF OWL-S SWSL WSMO/WSML Many other MLs exist, e.g. ESML, GML, but these are not …. Reasoners - http://www.cs.man.ac.uk/~sattler/reasoners.html OWL - Web Ontology Language (W3C) RDF - Resource Description Framework (W3C) OWL-S/SWSL - Web Services (W3C) WSMO/WSML - Web Services (EC/W3C) SWRL - Semantic Web Rule Language PML - Proof Markup Language ODM/MOF - Ontology Definition Metamodel/Meta Object Facility (OMG) Editors: Protégé, SWOOP, Medius, SWeDE Reasoners Pellet, Racer, Medius KBS, FACT++, fuzzyDL, KAON2, MSPASS, QuOnto Query Languages SPARQL, XQUERY, SeRQL, OWL-QL, RDFQuery Other Tools for Semantic Web Search: SWOOGLE swoogle.umbc.edu Collaboration: www.planetont.org Other: Jena, SeSAME/SAIL, Mulgara, Eclipse, KOWARI Semantic wiki: OntoWiki, SemanticMediaWiki SWEET sweet.jpl.nasa.gov VSTO vsto.hao.ucar.edu, www.vsto.org MMI www.marinemetadata.org GeoSciML

OWL and RDF OWL RDF Services Rules Lite DL Full OWL-S SWSL WSML SAWSDL - (WSDL-S) Rules SWRL

Developing Ontologies Approach: Bottom-up Top-down (upper-level or foundational) Mid-level (use case) Using tools Coding and testing Iterating Maintaining and evolving (curation, preservation)

GRDDL - bottom up GRDDL - Gleaning Resource Descriptions from Dialects of Languages Pretty much = “XML/XHTML (for e.g.) into RDF via XSLT” Good support, e.g. Jena Handles microformats Active community How to categorize, use, re-use (parts of)?

Collecting RDFa extends XHTML by: ATOM (used with RSS) extending the link and meta to include child elements add metadata to any elements (a bit like the class in micro-formats, but via dedicated properties) It is very similar to micro-formats, but with more rigor: it is a general framework (instead of an “agreement” on the meaning of, say, a class attribute value) terminologies can be mixed more easily ATOM (used with RSS) GRDDL = Gleaning Resource Descriptions from Dialects of Languages

Foundational Ontologies CONTENTS General concepts and relations that apply in all domains physical object, process, event,…, inheres, participates,… Rigorously defined formal logic, philosophical principles, highly structured Examples DOLCE, BFO, GFO, SUMO, CYC, (Sowa) Courtesy: Boyan Brodaric

Foundational ontology Foundational Ontologies PURPOSE: help integrate domain ontologies “…and then there was one…” Foundational ontology Geophysics ontology Marine ontology Water ontology Planetary ontology Geology ontology Struc ontology Rock ontology Courtesy: Boyan Brodaric

Foundational ontology Foundational Ontologies PURPOSE: help organize domain ontologies “…a place for everything, and everything in its place…” Foundational ontology shale rock formation lithification Courtesy: Boyan Brodaric

Problem scenario Little work done on linking foundational ontologies with geoscience ontologies Such linkage might benefit various scenarios requiring cross-disciplinary knowledge, e.g.: water budgets: groundwater (geology) and surface water (hydro) hazards risk: hazard potential (geology, geophysics) and items at threat (infrastructure, people, environment, economic) health: toxic substances (geochemistry) and people, wildlife many others… Courtesy: Boyan Brodaric

DOLCE

DOLCE + SWEET Benefits Issues full coverage rich relations home for orphans single superclasses DOLCE = SWEET < SWEET Physical-body BodyofGround, BodyofWater,… Material-Artifact Infrastructure, Dam, Product,… Physical-Object LivingThing, MarineAnimal Amount-of-Matter Substance Activity HumanActivity Physical-Phenomenon Phenomena Process State StateOfMatter Quality Quantity, Moisture,… Physical-Region Basalt,… Temporal-Region Ordovician,… Issues individuals (e.g. Planet Earth) roles (contaminant) features (SeaFloor) Courtesy: Boyan Brodaric

Conclusions Surprisingly good fit amongst ontologies so far: no show-stopper conflicts, a few difficult conflicts DOLCE richness benefits geoscience ontologies good conceptual foundation helps clear some existing problems Unresolved issues in modeling science entities modeling classifications, interpretations, theories, models,… Same procedure with GeoSciML Courtesy: Boyan Brodaric

SUMO - Standard Upper Merged Ontology Physical Object SelfConnectedObject ContinuousObject CorpuscularObject Collection Process Abstract SetClass Relation Proposition Quantity Number PhysicalQuantity Attribute

Using SNAP/ SPAN

GeoSciOnt?

Using SWEET Plug-in (import) domain detailed modules Lots of classes, few relations (properties)

Mix-n-Match The IRI example: MMI Others Collect a lot of different ontologies representing different terms, levels of concepts, etc. into a base form: RDF See Benno’s talk in session 1b. MMI Others

Blumenthal NC basic attributes CF attributes IRIDL attributes/objects CF data objects CF Standard Names (RDF object) SWEET Ontologies (OWL) Location CF Standard Names As Terms IRIDL Terms SWEET as Terms Search Terms Gazetteer Terms Blumenthal

IRI RDF Architecture Blumenthal MMI Data Servers Ontologies JPL bibliography Start Point Standards Organizations RDF Crawler Location Canonicalizer RDFS Semantics Owl Semantics SWRL Rules SeRQL CONSTRUCT Time Canonicalizer Sesame Search Queries Blumenthal Search Interface

Mid-Level: Developing ontologies Use cases and small team (7-8; 2-3 domain experts, 2 knowledge experts, 1 software engineer, 1 facilitator, 1 scribe) Identify classes and properties (leverage controlled vocab.) Start with narrower terms, generalize when needed or possible Adopt a suitable conceptual decomposition (e.g. SWEET) Import modules when concepts are orthogonal Review, vet, publish Only code them (in RDF or OWL) when needed (CMAP, …) Ontologies: small and modular

Use Case example Plot the neutral temperature from the Millstone-Hill Fabry Perot, operating in the vertical mode during January 2000 as a time series. Objects: Neutral temperature is a (temperature is a) parameter Millstone Hill is a (ground-based observatory is a) observatory Fabry-Perot is a interferometer is a optical instrument is a instrument Vertical mode is a instrument operating mode January 2000 is a date-time range Time is a independent variable/ coordinate Time series is a data plot is a data product

Class and property example Parameter Has coordinates (independent variables) Observatory Operates instruments Instrument Has operating mode Instrument operating mode Has measured parameters Date-time interval Data product

Higher level use case Find data which represents the state of the neutral atmosphere above 100km, toward the arctic circle at any time of high geomagnetic activity

Translating the Use-Case - non-monotonic? GeoMagneticActivity has ProxyRepresentation GeophysicalIndex is a ProxyRepresentation (in Realm of Neutral Atmosphere) Kp is a GeophysicalIndex hasTemporalDomain: “daily” hasHighThreshold: xsd_number = 8 Date/time when KP => 8 Specification needed for query to CEDARWEB Instrument Parameter(s) Operating Mode Observatory Date/time Return-type: data Input Physical properties: State of neutral atmosphere Spatial: Above 100km Toward arctic circle (above 45N) Conditions: High geomagnetic activity Action: Return Data

Translating the Use-Case - ctd. NeutralAtmosphere is a subRealm of TerrestrialAtmosphere hasPhysicalProperties: NeutralTemperature, Neutral Wind, etc. hasSpatialDomain: [0,360],[0,180],[100,150] hasTemporalDomain: NeutralTemperature is a Temperature (which) is a Parameter Translating the Use-Case - ctd. Specification needed for query to CEDARWEB Instrument Parameter(s) Operating Mode Observatory Date/time Return-type: data Input Physical properties: State of neutral atmosphere Spatial: Above 100km Toward arctic circle (above 45N) Conditions: High geomagnetic activity Action: Return Data FabryPerotInterferometer is a Interferometer, (which) is a Optical Instrument (which) is a Instrument hasFilterCentralWavelength: Wavelength hasLowerBoundFormationHeight: Height ArcticCircle is a GeographicRegion hasLatitudeBoundary: hasLatitudeUpperBoundary: GeoMagneticActivity has ProxyRepresentation GeophysicalIndex is a ProxyRepresentation (in Realm of Neutral Atmosphere) Kp is a GeophysicalIndex hasTemporalDomain: “daily” hasHighThreshold: xsd_number = 8 Date/time when KP => 8

Tools - Using Protégé

Creating Ontologies - visual UML - new release of ODM/MOF Ontology Definition Metamodel/Meta Object Facility (OMG) for UML Provides standardized notation CMAP Ontology Editor (concept mapping tool from IHMC) Drag/drop visual development of classes, subclass (is-a) and property relationship Read and writes OWL Formal convention (OWL/RDF tags, etc.) White board, text file

Using CMAP/COE

Is OWL the only option? No… SKOS - Simple Knowledge Organization Scheme Annotations (RDFa) Atom Natural Language (read results from a web search and transform to a usable form) CL (common logic) Rabbit, e.g. ShellfishCourse is a Meal Course that (if has drink) always has drink Potable Liquid that has Full body and which either has Moderate or Strong flavour PENG (processable English)

Is OWL the only option II? No… Natural Language (NL) Read results from a web search and transform to a usable form Find/filter out inconsistencies, concepts/relations that cannot be represented Popular options CLCE (common logic controlled english) Rabbit, e.g. ShellfishCourse is a Meal Course that (if has drink) always has drink Potable Liquid that has Full body and which either has Moderate or Strong flavour PENG (processable English) Really need PSCI - process-able science

Creating Ontologies - verbal Translating use cases E.g. Find data which represents the state of the neutral atmosphere above 100km, toward the arctic circle at any time of high geomagnetic activity Can this be expressed as an ontology? CLCE, Rabbit, PENG, Sydney syntax Notice something about the next examples?

Sydney syntax If X has Y as a father then Y is the only father of X. The class person is equivalent to male or female, and male and female are mutually exclusive. equivalent to The classes male and female are mutually exclusive. The class person is fully defined as anything that is a male or a female.

PENG - Processible English If X is a research programmer then X is a programmer. Bill Smith is a research programmer who works at the CLT. Who is a programmer and works at the CLT?

CLCE - Common Logic Controlled English CLCE: If a set x is the set of (a cat, a dog, and an elephant), then the cat is an element of x, the dog is an element of x, and the elephant is an element of x. PC:~(∃x:Set)(∃x1:Cat)(∃x2:Dog)(∃x3:Elephant)(Set(x,x1,x2,x3) ∧ ~(x1∈x ∧ x2∈x ∧ x3∈x))

Use Case Provide a decision support capability for an analyst to determine an individual’s susceptibility to avian flu without having to be precise in terminology (-nyms)

Using ThManager

Services Ontologies of services, provides: What does the service provide for prospective clients? The answer to this question is given in the "profile," which is used to advertise the service. To capture this perspective, each instance of the class Service presents a ServiceProfile. How is it used? The answer to this question is given in the "process model." This perspective is captured by the ServiceModel class. Instances of the class Service use the property describedBy to refer to the service's ServiceModel. How does one interact with it? The answer to this question is given in the "grounding." A grounding provides the needed details about transport protocols. Instances of the class Service have a supports property referring to a ServiceGrounding.

Developing a service ontology Use case: find and display in the same projection, sea surface temperature and land surface temperature from a global climate model. Find and display in the same projection, sea surface temperature and land surface temperature from a global climate model. Classes/ concepts: Temperature Surface (sea/ land) Model Climate Global Projection Display …

Service ontology Climate model is a model Model has domain Climate Model has component representation Land surface is-a component representation Ocean is-a component representation Sea surface is part of ocean Model has spatial representation (and temporal) Spatial representation has dimensions Latitude-longitude is a horizontal spatial representation Displaced pole is a horizontal spatial representation Ocean model has displaced pole representation Land surface model has latitude-longitude representation Lambert conformal is a geographic spatial representation Reprojection is a transform between spatial representation ….

Service ontology A sea surface model has grid representation displaced pole and land surface model has grid representation latitude-longitude and both must be transformed to Lambert conformal for display

Best practices Ontologies/ vocabularies must be shared and reused - swoogle.umbc.edu, www.planetont.org Examine ‘core vocabularies’ to start with SKOS Core: about knowledge systems Dublin Core: about information resources, digital libraries, with extensions for rights, permissions, digital right management FOAF: about people and their organizations DOAP: on the descriptions of software projects DOLCE seems the most promising to match science ontologies Go “Lite” as much as possible, then DL and only if you have to Full - balancing expressibility vs. implementability Minimal properties to start, add only when needed

Tutorial Summary Many different options for ontology development and encoding Tools are in reasonable shape, no killer-tool Best practices DO exist PLEASE DO NOT just start coding OWL! Use case should drive the functional requirements of both your ontology and how you will ‘build’ one PARTNER with someone already familiar

More information OWL-S - http://www.w3.org/Submission/OWL-S SWSO/F/L - Semantic Web Services Ontology/Framework/Language - http://www.w3.org/Submission/SWSF/ WSMO/X/L - Web Services Modeling Ontology/Exection/Language - http://www.w3.org/Submission/WSMX/ www.wsmo.org, www.wsmx.org SAWSDL - (WSDL-S)

Other tools Reasoners Query Languages Other Tools for Semantic Web Pellet, Racer, Medius KBS, FACT++, fuzzyDL, KAON2, MSPASS, QuOnto Query Languages SPARQL, XQUERY, SeRQL, OWL-QL, RDFQuery Other Tools for Semantic Web Search: SWOOGLE swoogle.umbc.edu Collaboration: www.planetont.org Other: Jena, SeSAME/SAIL, Mulgara, Eclipse, KOWARI Semantic wiki: OntoWiki, SemanticMediaWiki Languages: OWL RDF OWL-S SWSL WSMO/WSML Many other MLs exist, e.g. ESML, GML, but these are not …. Reasoners - http://www.cs.man.ac.uk/~sattler/reasoners.html OWL - Web Ontology Language (W3C) RDF - Resource Description Framework (W3C) OWL-S/SWSL - Web Services (W3C) WSMO/WSML - Web Services (EC/W3C) SWRL - Semantic Web Rule Language PML - Proof Markup Language ODM/MOF - Ontology Definition Metamodel/Meta Object Facility (OMG) Editors: Protégé, SWOOP, Medius, SWeDE Reasoners Pellet, Racer, Medius KBS, FACT++, fuzzyDL, KAON2, MSPASS, QuOnto Query Languages SPARQL, XQUERY, SeRQL, OWL-QL, RDFQuery Other Tools for Semantic Web Search: SWOOGLE swoogle.umbc.edu Collaboration: www.planetont.org Other: Jena, SeSAME/SAIL, Mulgara, Eclipse, KOWARI Semantic wiki: OntoWiki, SemanticMediaWiki SWEET sweet.jpl.nasa.gov VSTO vsto.hao.ucar.edu, www.vsto.org MMI www.marinemetadata.org GeoSciML

Editors Protégé (http://protégé.stanford.edu) SWOOP (http://mindswap.org/2004/SWOOP) Altova SemanticWorks (http://www.altova.com/download/semanticworks/semantic_web_rdf_owl_editor.html) SWeDE (http://owl-eclipse.projects.semwebcentral.org/InstallSwede.html), goes with Eclipse Medius TopBraid Composer and other commercial tools Visual Ontology Modeler (VOM) - Sandpiper CMAP Ontology Editor (COE) (http://cmap.ihmc.us/coe)

What about Earth Science? SWEET (Semantic Web for Earth and Environmental Terminology) http://sweet.jpl.nasa.gov based on GCMD terms modular using faceted and integrative concepts VSTO (Virtual Solar-Terrestrial Observatory) http://vsto.hao.ucar.edu captures observational data (from instruments) modular using domains MMI http://marinemetadata.org captures aspects of marine data, ocean observing systems partly modular, mostly by developed project GeoSciML http://www.opengis.net/GeoSciML/ is a GML (Geography ML) application language for Geoscience modular, in ‘packages’