1. Foundations I: Methodologies, Knowledge Representation
Professor Deborah McGuinness
TA－Weijing Chen
Other lectures from Professor Peter Fox, Professor Joanne Luciano, grad student Jim McCusker, and possibly others from
CSCI , , CSCI , 87927
ITWS , , ITWS , 87928
Week 2, September 12, 2011

2. Review of reading Assignment 1
Ontologies 101, Semantic Web, e-Science, RDFS, OWL guide
Any comments, questions?
One pass around room on highlights

3. 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

4. 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.
Adopt Technology Approach
Leverage Technology Infrastructure
Science/Expert Review & Iteration
Rapid Prototype
Open World: Evolve, Iterate, Redesign, Redeploy
Use Tools
Evaluation
Analysis
Use Case
Develop model/ ontology
Small Team, mixed skills

5. 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.
Adapted from Deepak Kumar (Bryn Mayr)
updated 2009

6. 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
Adapted from Deepak Kumar (Bryn Mayr)
updated 2009 6 6

7. 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).
Adapted from Deepak Kumar (Bryn Mayr)

8. 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.
Adapted from Deepak Kumar (Bryn Mayr) 8 8

9. 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 …)

10. 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)
Adapted from Conrad Barski under CC license.

11. 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.
Adapted from Conrad Barski under CC license.

12. 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.
Adapted from Conrad Barski under CC license.

13. 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 people
Adapted from Conrad Barski under CC license.

14. 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.
Adapted from Conrad Barski under CC license.

15. Semantic Web Layers

16. Elements of KR in Semantic Web
Declarative Knowledge
Statements as triples: {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
Instrument has measured parameter
Instrument operating mode has measured parameter
NeutralTemperature is-a temperature
Temperature 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

17. 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 Ontologies Panel by Gruninger, Lehmann, McGuinness, Uschold, Welty;
– updated by McGuinness.
Description in:
Originally from AAAI Ontologies Panel by Gruninger, Lehmann, McGuinness, Uschold, Welty;
– updated by McGuinness.
Description in:

18. 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

19. 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)

20. ‘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

21. 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)
GRDDL = Gleaning Resource Descriptions from Dialects of Languages

22. Foundational Ontologies
Domain independent concepts and relations
physical object, process, event,…, participates,…
(Usually) Rigorously defined
formal logic, philosophical principles, highly structured
Examples
DOLCE – 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)
Adapted from Boyan Brodaric

23. 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

24. 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

25. 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

26. DOLCE - Descriptive Ontology for Linguistic and Cognitive Engineering

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

28.
BFO – Basic Formal Ontology
Snap comes from a snapshot at any given time

29. Span comes from spanning time;
sometimes considered a 4D description

30. Using SNAP/ SPAN

32. SWEET 2.0 Modular Design Supports easy extension by domain specialists
Organized by subject (theoretical to applied)
Reorganization of classes, but no significant changes to content
Importation is unidirectional
Math, Time, Space
Basic Science
Geoscience Processes
Geophysical Phenomena
Applications
Needs for ontology package design led to SWEET 2.0
importation

33. SWEET 2.0 Ontologies

34. Using SWEET Plug-in (import) domain detailed modules
Lots of classes, few relations (properties)
Version 2.0 is re-usable and extensible

35. Mix-n-Match The hybrid example:
Collect a lot of different ontologies representing different terms, levels of concepts, etc. into a base form: RDF

36. 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

37. 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.
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

38. 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

42. 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

43. Extending the KR for a purpose
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

44. 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

45. 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 - )
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

47. Representing processes
From Volcano-Atmosphere use case

48. Is OWL/RDF the only option? No…
SKOS - Simple Knowledge Organization Scheme for Taxonomies
Annotations (RDFa) – for un- or semi-structured information sources
Atom (and RSS) – for representing syndication feeds – structured
More expressive languages IKL, CL, …
Languages aimed at different paradigms – e.g., rule languages

49. Query Querying knowledge representations in OWL and/or RDF
SPARQL for RDF and
OWL-QL (for OWL)
XQUERY (for XML)
SeRQL (for SeSAME)
RDFQuery (RDF)
Few as yet for natural language representations
JSON = JavaScript Object Notation
SeRQL is for SeSAME

50. 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

51. 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)

52. 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.

53. Assignment for Week 2 Assignment 1:
Reading:
Semantic Web for the Working Ontologist
Alternate reading: Pizza Tutorial
Assignment 1:
Representing Knowledge and Understanding Representations
HW1:
HW2:

54. Extras

55. Selected Technical Benefits
Integrating Multiple Data Sources
Semantic Drill Down / Focused Perusal
Statements about Statements
Inference
Translation
Smart (Focused) Search
Smarter Search … Configuration
Proof and Trust
Updated material reused from “The Substance of the Web”. McGuinness and Dean. Semantic Web Applications for National Security. May,

56. 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
hasCoordinates
#magnetic
name
hasLowerBoundaryValue
“100”
“Terrestrial
Ionosphere”
NOTE _ NOTES NEED TO BE UPDATED TO NEW CONTENT: In this example about country information, the different line colors represent different data sources merged because of the use of common URIs.
Key difference improvement with rdf over pure xml
***not sure what coordinates magnetic means – talk here
*** I am not sure I would put 100km since that appears to be a string but it is really a number with units.
It would be simpler to either have a straight number or for simplicity (particulary on example 1), not to do something with a number
hasLowerBoundaryUnit
“km”
Different line & text colors
represent different data sources

57. 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
…#NeutralTemperature
…#Norway
Internet
measuredby
locatedIn
...#ISR
For example, we can resolve the URI of the organization that someone worksFor to learn more about it. Notice also the link to the university, which is a type of organization in the class in the ontology.
*** I could use acronyms expanded on the notes page
...#FPI
type
operatedby
…#EISCAT
...#MilllstoneHill

58. 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
#Danny’s
#Aurora
hasSource
hasDateTime
hascolor
Red
The example is taken from the CIA World Factbook, where many values have an associated timestamp, uncertainty, or notes. The statement itself becomes the subject of these other statements.
Ontologies Workshop, APL May 26, 2006

59. 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
#Millstone Hill
#Interferometer
OperatesInstrument
hasInstrument
isOperatedBy
Measures
hasOperatingMode
hasTypeofData
hasMeaasuredData
#VerticalMeans
Example: given the 2 statements in black and an ontology about families, we can infer the 5 additional statements in red. hasUncle requires the use of the Semantic Web Rule Language (SWRL)
***we need to talk here. Does this really say that verticalMeans hasOperatingMode which is interferometer we are asserting black and inferrin red. This also says that verticalMeans measure interferometer and hasTypeOfData millstone hill. It looks like arrows are turned around.

60. 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
name
ont1:EduLevel
ont1:Precipitation
VO:Scientist
#precipitation
In this example, we translate the classes properties and instances used from one ontology to another.
Fips - Federal Information Processing Standards
Iso – international standards organization
name
ont2:EduLevel
ont2:Rain
EduVO:K-12

61. 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 ….
***I cold use the english description for this slide

62. 7: Smarter Search / Configuration

63. 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:
I need notes on slide 6 on

66. 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
hasCalibration
#FlatField
#Critical
Dataset
hasPeerReview
#Solar
Physics
Paper
For example, Bob is allowed to access the W3C member web site if he can prove that he is an employee of a member company.
*** what are we trying to prove? That the critical dataset has been peer reviewed?
This picture says flatfield has calibration that is critical dataset which has peer review that is solar physics paper.
“Critical Dataset has been calibrated
with a flat field program that is published
In the peer reviewed literature.”

67. 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, …

68. Semantic Discovery Service
Inference Web Infrastructure (McGuinness, et.al., )
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
provenance
registration
search engine
based publishing
Expert friendly
Visualization
End-user friendly
visualization
Trust computation
Semantic 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.
Pacific Northwest Division

69. An abstracted explanation
SW Questions & Answers
Users can explore extracted entities and relationships, create new hypothesis, ask questions, browse answers and get explanations for answers.
A context for
explaining
the answer
A question
An answer
An abstracted explanation
(this graphical interface done by Batelle supported by Stanford KSL)

70. 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

71. 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

72. 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

73. 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

74. 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

75. 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))

76. 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)

79. Building SKOS
ThManager
Protégé (4) plugin for SKOS

80. 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)

81. 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.

82. 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?

83. 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

84. 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.

85. 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.

86. Semantic Web with Rules
Metalog
RuleML
SWRL
RIF
OWL 2 RL
WRL
Cwm
Jess - rules engine

87. 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 …

88. 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 ….

89. 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