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Foundations I: Methodologies, Knowledge Representation
Professor Deborah McGuinness
TA － Weijing ChenOther lectures from Professor Peter Fox, Professor Joanne Luciano, grad student
Jim McCusker, and possibly others from http://tw.rpi.edu/web/People
CSCI 6962 - 01, 86933 , CSCI 4969 - 01, 87927
ITWS 6960 - 01, 87198 , ITWS 4969 - 01, 87928
Week 2, September 12, 2011
Review of reading Assignment 1• Ontologies 101, Semantic Web, e-Science,
RDFS, OWL guide
• Any comments, questions?
• One pass around room on highlights
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Contents• Review of methodologies
• Elements of KR in semantic web context
• And in e-Science
• Choices of representation, models
• Examples of KR
• Encoding and understanding representations
• Assignment 1
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Semantic Web Methodology and Technology Development Process
• Establish and improve a well-defined methodology vision for Semantic Technology based application development
• Leverage controlled vocabularies, et c.
Use Case
Small Team, mixed skills
Analysis
Adopt Technology Approach
Leverage Technology
Infrastructure
Rapid Prototype
Open World: Evolve, Iterate,
Redesign, Redeploy
Use Tools
Science/Expert Review & Iteration
Develop model/
ontology
Evaluation
KR and methodologies
• Procedural Knowledge: Knowledge is encoded in functions/procedures.
This can be viewed as hard coded and less flexible.
E.g.: function Person(X) return boolean is
if (X = ``Socrates'') or (X = ``Hillary'')
then return true else return false;
OR
function Mortal(X) return boolean is return person(X);
• Networks: A compromise between declarative and procedural schemes. Knowledge is represented in a labeled, directed graph whose nodes represent concepts and entities, while its arcs represent relationships between these entities and concepts.
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KR and methodologies
• Frames: Much like a semantic network except each node represents prototypical concepts and/or situations. Each node has several property slots whose values may be specified or inherited.
• Logic: A way of declaratively representing knowledge. For example:
– person(Socrates).
– person(Hillary).
– forall X [person(X) ---> mortal(X)]
– DL, FOL, HOL
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KR and methodologies
• Decision Trees: Concepts are organized in the form of a tree.
• Statistical Knowledge: The use of certainty factors, Bayesian Networks, Dempster-Shafer Theory, Fuzzy Logics, ..., etc.
• Rules: The use of Production Systems to encode condition-action rules (as in expert systems).
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KR and methodologies
• Parallel Distributed processing: The use of connectionist models.
• Subsumption Architectures: Behaviors are encoded (represented) using layers of simple (numeric) finite-state machine elements.
• Hybrid Schemes: Any representation formalism employing a combination of KR schemes.
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Remember, in any knowledge encoding
• Some of the knowledge is lost when it is placed into any particular representation structure, or may not be reusable (e.g. Frames)
• So, you may ask something that cannot be answered or inferred
• Knowledge evolves, i.e. changes
• Knowledge and understanding is very often context dependent (and discipline, language, and skill-level dependent, and …) 9
And, if you are used to logic• You are working mostly within the world of
logic, whereas we are trying to represent knowledge with logic and we are usually dealing with tangible objects, such as trees, clouds, rock, storms, etc.
• Because of this, we have to be very careful when translating real things into logical symbols - this can, surprisingly, be a difficult challenge.
• Consider your method of representation (yes, we do want to compute with it) 10
Thus• A person who wants to encode knowledge
needs to decouple the ambiguities of interpretation from the mathematical certainty of (any form of) logic.
• The nature of interpretation is critical in formal knowledge representation and is carefully formalized by KR scientists in order to guarantee that no ambiguity exists in the logical structure of the represented knowledge.
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Representing Knowledge With Objects
• Take all individuals that we need to keep track of and place them into different buckets based on how similar they are to each other. Each bucket is given a description based on what objects it contains.
• Since the individuals in a given bucket are at least somewhat similar, we can avoid needing to describe every inconsequential detail about each individual. Instead, properties that are common to all individuals in a bucket can just be assigned to the entire bucket at once. Properties are typically either primitive values (such as numbers or text strings) or may be references to other buckets.
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Representing Knowledge With Objects
• Some buckets will be more similar to each other than others and we can arrange the buckets into a hierarchy based on the similarity.
• If all buckets in a branch in the tree of buckets share a property, the information can be further simplified by assigning the property only to the parent bucket. Other buckets (and individuals) are said to inherit that property.
• Buckets may have different names: e.g. Classes, Frames, or Nodes
• BUT, once we move to (e.g.) DL, not all object rules apply, e.g. cannot override properties
• Multiple inheritance is not always obvious to people13
Re-enter Semantic Web
At its core, the Semantic Web can be thought of as a methodology for linking pieces of structured and unstructured information into commonly-shared description logics ontologies.
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Semantic Web Layers
http://www.w3.org/2003/Talks/1023-iswc-tbl/slide26-0.html, http://flickr.com/photos/pshab/291147522/
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Elements of KR in Semantic Web• Declarative Knowledge• Statements as triples: {subject-predicate-object}
interferometer is-a optical instrumentFabry-Perot is-a interferometerOptical instrument has focal lengthOptical instrument is-a instrumentInstrument has instrument operating modeInstrument has measured parameterInstrument operating mode has measured parameterNeutralTemperature is-a temperatureTemperature is-a parameter
• A query: select all optical instruments which have operating mode vertical
• An inference: infer operating modes for a Fabry-Perot Interferometer which measures neutral temperature
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Ontology Spectrum
Catalog/ID
SelectedLogical
Constraints(disjointness,
inverse, …)
Terms/glossary
Thesauri“narrower
term”relation
Formalis-a
Frames(properties)
Informalis-a
Formalinstance Value
Restrs.
GeneralLogical
constraints
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
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OWL or RDF or OWL 2 RL?
• In representing knowledge you will need to balance expressivity with implementability
• OWL (Lite, DL, Full) 1 or 2 and if OWL 2, then which profile?
• RDF and RDFS• Rules, e.g. SWRL or OWL 2 RL
• You will need to consider the sources of your knowledge
• You will need to consider what you want to do with the represented knowledge
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The knowledge base• Using, Re-using, Re-purposing, Extending,
Subsetting• Approach:
– Bottom-up (instance level or vocabularies)– Top-down (upper-level or foundational)– Mid-level (use case)
• Coding and testing (understanding)• Using tools (some this class, more over the next two
classes)• Iterating (later)• Maintaining and evolving (curation, preservation)
(later)
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‘Collecting’ the ‘data’• Part of the (meta)data information is present in tools ... but
thrown away at output e.g., a business chart can be generated by a tool: it ‘knows’ the structure, the classification, etc. of the chart,but, usually, this information is lost storing it in web data would be easy!
• Semantic Web-aware tools are around (even if you do not know it...), though more would be good: – Photoshop CS stores metadata in RDF in, say, jpg files (using XMP)– RSS 1.0 feeds are generated by (almost) all blogging systems (a
huge amount of RDF data!) • Scraping - different tools, services, etc, come around every
day: – get RDF data associated with images, for example: service to get
RDF from flickr images– service to get RDF from XMP– XSLT scripts to retrieve microformat data from XHTML files– RSS scraping in use in Virtual Observatory projects in Japan– scripts to convert spreadsheets to RDF
• SQL - A huge amount of data in Relational Databases– Although tools exist, it is not feasible to convert that data into
RDF – Instead: SQL ⇋ RDF ‘bridges’ are being developed: a query to RDF
data is transformed into SQL on-the-fly
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More Collecting• RDFa (formerly known as RDF/A) extends XHTML by: – extending the link and meta to include child elements
– add metadata to any elements (a bit like the class in microformats, but via dedicated properties)
• It is very similar to microformats, but with more rigor: – it is a general framework (instead of an ﾒagreement ﾓ on the meaning of, say, a class attribute value)
– terminologies can be mixed more easily
• GRDDL - Gleaning Resource Descriptions from Dialects of Languages
• ATOM - XML-based Web content and metadata syndication format (used with RSS)
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Foundational OntologiesDomain independent concepts and relations
physical object, process, event,…, participates,…
(Usually) Rigorously definedformal logic, philosophical principles, highly structured
ExamplesDOLCE – Descriptive Onotology for Linguistic and Cognitive
Engineering
SUMO – Suggested Upper Merged Ontology
CYC Upper Level Ontology
BFO – Basic Formal Ontology
GFO – General Formal Ontology (developed by Onto Med)
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Foundational Ontologies
PURPOSE: help integrate domain ontologies
Geophysics ontology
Marine ontology
Water ontology
Planetary ontology
Geology ontology
Struc ontology
Rock ontology
“…and then there was one…”
Foundational ontology
Courtesy: Boyan Brodaric
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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
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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
26DOLCE - Descriptive Ontology for Linguistic and Cognitive Engineering
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• Physical • Object
• SelfConnectedObject • ContinuousObject • CorpuscularObject • Collection
• Process • Abstract
• SetClass • Relation
• Proposition • Quantity
• Number • PhysicalQuantity
• Attribute
SUMO - Standard Upper Merged Ontology
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• http://www.ifomis.org/Research/IFOMISReports/IFOMIS%20Report%2005_2003.pdf
http://www.ifomis.org/Research/IFOMISReports/IFOMIS%20Report%2005_2003.pdf
BFO – Basic Formal Ontology
Snap comes from a snapshot at any given time
29Span comes from spanning time;sometimes considered a 4D description
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Using SNAP/ SPAN
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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
• Importation is unidirectional
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SWEET 2.0 Ontologies
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Using SWEET
• Plug-in (import) domain detailed modules
• Lots of classes, few relations (properties)
• Version 2.0 is re-usable and extensible
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Mix-n-Match
• The hybrid example:
– Collect a lot of different ontologies representing different terms, levels of concepts, etc. into a base form: RDF
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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
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Use Case example• Plot the neutral temperature from the Millstone-Hill
Fabry Perot, operating in the non-vertical mode during January 2000 as a time series.
• Plot the neutral temperature from the Millstone-Hill Fabry Perot, operating in the non-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– Non-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
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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
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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
• Find data which represents the state of the neutral atmosphere above 100km, toward the arctic circle at any time of
high geomagnetic activity
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Extending the KR for a purpose
Input
Physical properties: State of neutral atmosphere
Spatial:
• Above 100km
• Toward arctic circle (above 45N)
Conditions:
• High geomagnetic activity
Action: Return Data
Specification needed for query to CEDARWEB
Instrument
Parameter(s)
Operating Mode
Observatory
Date/time
Return-type: data
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
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Translating the Use-Case - ctd.
Input
Physical properties: State of neutral atmosphere
Spatial:
Above 100km
Toward arctic circle (above 45N)
Conditions:
High geomagnetic activity
Action: Return Data
Specification needed for query to CEDARWEB
Instrument
Parameter(s)
Operating Mode
Observatory
Date/time
Return-type: data
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
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
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Knowledge representation - visual
• UML – Universal Modeling Language– Ontology Definition Metamodel/Meta Object
Facility (OMG) for UML– Provides standardized notation
• CMAP Ontology Editor (concept mapping tool from IHMC - http://cmap.ihmc.us/coe )– 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
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Representing processes
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Is OWL/RDF the only option? No…
• SKOS - Simple Knowledge Organization Scheme for Taxonomies http://www.w3.org/2004/02/skos/
• Annotations (RDFa) – for un- or semi-structured information sources http://www.w3.org/TR/xhtml-rdfa-primer/ http://rdfa.info
• Atom (and RSS) – for representing syndication feeds – structured http://tools.ietf.org/html/rfc4287
• More expressive languages IKL, CL, … • Languages aimed at different paradigms – e.g., rule
languages
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Query• Querying knowledge representations in OWL and/or RDF
• SPARQL for RDF http://www.sparql.org/ and http://www.w3.org/TR/rdf-sparql-query/
• OWL-QL (for OWL) http://projects.semwebcentral.org/projects/owl-ql/
• XQUERY (for XML)• SeRQL (for SeSAME)• RDFQuery (RDF)• Few as yet for natural language representations
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Best practices (some)• Ontologies/ vocabularies must be shared and reused - swoogle.umbc.edu, bioportal, OOR
• 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 – SIOC: about communities– DOAP: on the descriptions of software projects– DOLCE seems the most promising to match science ontologies
• Go “Lite” as much as possible, then increasing logic - balancing expressibility vs. implementability
• Minimal properties to start, add only when needed
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Summary• The science of knowledge representation has, throughout its
history, consisted of a compromise between pragmatism, scientific rigor, and accessibility to domain experts
• Many different options for ontology development and encoding, i.e. knowledge representation
• Sometimes, your choice of representation may need to change based on language and tools availability/ capability…
• Balancing expressivity and implementability means we favor an object-type, e.g. DL representation (but also suggests the need for a meta-representation: e.g. KIF – Knowledge Interchange Format)
• Next class (3) – ontology engineering• Use cases should drive the functional requirements of both
your ontology and how you will ‘build’ one (see class 4)
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Upcoming Logistics
– Next week – Jim McCusker on ontologies. He will do some hands on workshop walking you through building an ontology
– Following week – Peter Fox on use cases. He will introduce the format and also give examples.
http://tw.rpi.edu/web/Courses/SemanticeScience/2011
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Assignment for Week 2
–Reading: –Semantic Web for the Working Ontologist–Alternate reading: Pizza Tutorial
• Assignment 1:
Representing Knowledge and Understanding Representations
HW1: http://tw.rpi.edu/media/latest/SeS2011_HW.pdf
HW2: http://tw.rpi.edu/media/latest/SeS2011_HW2.pdf
Extras
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Selected Technical Benefits1. Integrating Multiple Data Sources2. Semantic Drill Down / Focused Perusal3. Statements about Statements4. Inference5. Translation6. Smart (Focused) Search7. Smarter Search … Configuration8. Proof and Trust
Updated material reused from “The Substance of the Web”. McGuinness and Dean. Semantic Web Applications for National Security. May, 2005. http://www.schafertmd.com/swans/agenda.html
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1: Integrating Multiple Data Sources
• The Semantic Web lets us merge statements from different sources
• The RDF Graph Model allows programs to use data uniformly regardless of the source
• Figuring out where to find such data is a motivator for Semantic Web Services
#Ionosphere #magnetic
“100”“TerrestrialIonosphere”
name
hasCoordinates
hasLowerBoundaryValue
Different line & text colors represent different data sources
hasLowerBoundaryUnit
“km”
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2: Drill Down /Focused Perusal
• The Semantic Web uses Uniform Resource Identifiers (URIs) to name things
• These can typically be resolved to get more information about the resource
• This essentially creates a web of data analogous to the web of text created by the World Wide Web
• Ontologies are represented using the same structure as content– We can resolve class and
property URIs to learn about the ontology
InternetInternet
…#NeutralTemperature
...#ISR
…#Norway
…#EISCAT
measuredby
type
locatedIn
...#FPI
...#MilllstoneHill
operatedby
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3: Statements about Statements• The Semantic Web allows us to
make statements about statements– Timestamps
– Provenance / Lineage
– Authoritativeness / Probability / Uncertainty
– Security classification
– …
• This is an unsung virtue of the Semantic Web
#Aurora
Red
#Danny’s
20031031
hascolor
hasSource
hasDateTime
Ontologies Workshop, APL May 26, 2006
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4: Inference
• The formal foundations of the Semantic Web allow us to infer additional (implicit) statements that are not explicitly made
• Unambiguous semantics allow question answerers to infer that objects are the same, objects are related, objects have certain restrictions, …
• SWRL allows us to make additional inferences beyond those provided by the ontology
#VerticalMeans
#Interferometer#Millstone Hill
hasOperatingMode
hasInstrument
hasTypeofDatahasMeaasuredData
OperatesInstrument
isOperatedBy
Measures
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5: Translation
• While encouraging sharing, the Semantic Web allows multiple URIs to refer to the same thing
• There are multiple levels of mapping– Classes– Properties– Instances– Ontologies
• OWL supports equivalence and specialization; SWRL allows more complex mappings
#precipitation
ont1:Precipitation VO:Scientist
name ont1:EduLevel
#precipitation
ont2:Rain EduVO:K-12
name ont2:EduLevel
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6: Smart (Focused) Search
• The Semantic Web associates 1 or more classes with each object
• We can use ontologies to enhance search by:– Query expansion– Sense disambiguation– Type with restrictions– ….
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7: Smarter Search / Configuration
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GEONGRID Ontology Search and Data Integration Example
Uses emerging web standards to enable smart web applications
Given an upper-level domain choice •Ecology
Illustrate or list contained concepts/hierarchy
•VegetationCover, TreeRings, etc. Retrieve some specific options from web
•Maps, tree-ring data,
•
Info: https://portal.geongrid.org:8443/gridsphere/gridsphere
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8: Proof
• The logical foundations of the Semantic Web allow us to construct proofs that can be used to improve transparency, understanding, and trust
• Proof and Trust are on-going research areas for the Semantic Web: e.g., See PML and Inference Web
#FlatField#CriticalDataset
#SolarPhysicsPaper
hasCalibration
hasPeerReview
“Critical Dataset has been calibrated with a flat field program that is publishedIn the peer reviewed literature.”
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Inference Web Framework for explaining reasoning tasks by storing,
exchanging, combining, annotating, filtering, segmenting, comparing, and rendering proofs and proof fragments provided by multiple distributed reasoners.
• OWL-based Proof Markup Language (PML) specification as an interlingua for proof interchange
• IWExplainer for generating and presenting interactive explanations from PML proofs providing multiple dialogues and abstraction options
• IWBrowser for displaying (distributed) PML proofs • IWBase distributed repository of proof-related meta-data such
as inference engines/rules/languages/sources• Integrated with theorem provers, text analyzers, web
services, …
http://iw.rpi.edu
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Files/WWW Toolkit
Proof Markup Language (PML)
CWM (NSF TAMI)
JTP(DAML/NIMD)
SPARK(DARPA CALO)
UIMA(DTO NIMD
Exp Aggregation)
IW Explainer/Abstractor
IWBase
IWBrowser
IWSearch
Trust
Justification
Provenance
N3
KIF
SPARK-L
Text Analytics
IWTrust
provenanceregistration
search enginebased publishing
Expert friendlyVisualization
End-user friendly visualization
Trust computationSemantic Discovery Service
(DAML/SNRC)
OWL-S/BPEL
Framework for explaining question answering tasks by • abstracting, storing, exchanging, • combining, annotating, filtering, segmenting, • comparing, and rendering proofs and proof fragments provided by question answerers.
Inference Web Infrastructure (McGuinness, et.al., 2004 http://www.ksl.stanford.edu/KSL_Abstracts/KSL-04-03.html )
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SW Questions & AnswersUsers can explore extracted entities and relationships, create new
hypothesis, ask questions, browse answers and get explanations for answers.
A question
An answer
A context for explaining the answer
(this graphical interface done by Batelle supported by Stanford KSL)
An abstracted explanation
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Summary• Semantics are a very key ingredient for progress in
informatics and escience• A sustained involvement of key inter-disciplinary
team members is very important -> leads to incentives, rewards, etc. and a balance of research and production
• This is what we will be teaching you in this class
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DOLCE + SWEETDOLCE = 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 Process
State StateOfMatter
Quality Quantity, Moisture,…
Physical-Region Basalt,…
Temporal-Region Ordovician,…
Benefitsfull coverage
rich relations
home for orphans
single superclasses
Issuesindividuals (e.g. Planet Earth)
roles (contaminant)
features (SeaFloor)
Courtesy: Boyan Brodaric
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Conclusions
Surprisingly good fit amongst ontologiesso 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,…
Courtesy: Boyan Brodaric
Same procedure with GeoSciML
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CF attributes
SWEET Ontologies(OWL)
Search Terms
CF Standard Names(RDF object)
IRIDL Terms
NC basic attributes
IRIDLattributes/objects
SWEET as Terms
CF Standard NamesAs Terms
Gazetteer Terms
CF data objects
Location
Blumenthal
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Data ServersOntologies
MMI
JPL
StandardsOrganizations
Start Point
RDF Crawler
RDFS SemanticsOwl SemanticsSWRL Rules
SeRQL CONSTRUCT
Search Queries
LocationCanonicalizer
TimeCanonicalizer
Sesame
Search Interface
bibliography
IRI RDF Architecture
Blumenthal
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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))
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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)
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Building SKOS• ThManager
• Protégé (4) plugin for SKOS
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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 but that’s another story (research project)
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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 toThe classes male and female are
mutually exclusive. The class person is fully defined as anything that is a male or a female.
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PENG - Processible English
1. If X is a research programmer then X is a programmer.
2. Bill Smith is a research programmer who works at the CLT.
3. Who is a programmer and works at the CLT?
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Rules (aka ‘Logic’)• OWL is based on Description Logic• OWL DL follows it precisely• There are things that DL cannot express (though there are things that are difficult to express with rules and easy in DL...)– A well known examples is Horn rules (eg, the ‘uncle’ relationship): (P1 ∧ P2 ∧ ...) → C
– e.g.: parent(?x,?y) ∧ brother(?y,?z) ⇒ uncle(?x,?z)
– Or, for any X, Y and Z: if Y is a parent of X, and Z is a brother of Y then Z is the uncle of X
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Examples from http://www.w3.org/Submission/SWRL/
• A simple use of these rules would be to assert that the combination of the hasParent and hasBrother properties implies the hasUncle property. Informally, this rule could be written as:– hasParent(?x1,?x2) ∧ hasBrother(?x2,?x3) ⇒ hasUncle(?x1,?x3)
• In the abstract syntax the rule would be written like:– Implies(Antecedent(hasParent(I-variable(x1) I-variable(x2)) hasBrother(I-variable(x2) I-variable(x3)))Consequent(hasUncle(I-variable(x1) I-variable(x3))))
• From this rule, if John has Mary as a parent and Mary has Bill as a brother then John has Bill as an uncle.
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Examples• An even simpler rule would be to assert that Students are Persons, as in– Student(?x1) ⇒ Person(?x1).Implies(Antecedent(Student(I-variable(x1)))Consequent(Person(I-variable(x1))))
– However, this kind of use for rules in OWL just duplicates the OWL subclass facility. It is logically equivalent to write instead• Class(Student partial Person) or • SubClassOf(Student Person)
– which would make the information directly available to an OWL reasoner.
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Semantic Web with Rules• Metalog• RuleML• SWRL• RIF• OWL 2 RL• WRL• Cwm• Jess - rules engine
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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 …
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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• ….
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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
