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 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 1

Review of reading Assignment 1

• Ontologies 101, Semantic Web, e-Science, RDFS, OWL guide • Any comments, questions?

• One pass around room on highlights 2

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 3

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.

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

4

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.

5

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 6

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).

7

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.

8

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, 10 we do want to compute with it)

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.

11

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.

12

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 13

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.

14

Semantic Web Layers

http://www.w3.org/2003/Talks/1023-iswc-tbl/slide26-0.html, http://flickr.com/photos/pshab/291147522/ 15

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 16

Ontology Spectrum

Catalog/ ID Thesauri “narrower term” relation Terms/ glossary Informal is-a is-a Selected Formal Frames Logical (properties) Constraints (disjointness, inverse, …) Formal instance Value Restrs.

General Logical 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

17

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 18

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) 19

‘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 is lost storing it in web data would be easy!

‘knows’ the structure, the classification, etc. of the chart,but, usually, this information • 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 20

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

microformats, but via dedicated properties) in • It is very similar to microformats, but with more rigor: – it is a general framework (instead of an ﾒ agreement ﾓ the meaning of, say, a

class

attribute value) – terminologies can be mixed more easily on • GRDDL Gleaning Resource Descriptions from Dialects of Languages • ATOM - XML-based Web content and metadata syndication format (used with RSS) 21

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) 22

Foundational Ontologies

PURPOSE: help integrate domain ontologies

“…

and then there was one

…”

Foundational ontology Geology ontology Struc ontology Rock ontology Geophysics ontology Marine ontology Water ontology Planetary ontology

Courtesy: Boyan Brodaric 23

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 24

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…

25 Courtesy: Boyan Brodaric

DOLCE -

Descriptive Ontology for Linguistic and Cognitive Engineering

26

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

• http://www.ifomis.org/Research/IFOMISRepor ts/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 28

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

Using SNAP/ SPAN

30

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SWEET 2.0 Modular Design

• Supports easy extension by domain specialists • Organized by subject (theoretical to applied)

Math, Time, Space Basic Science

• Reorganization of classes, but no significant changes to content • Importation is unidirectional

Geoscience Processes Geophysical Phenomena Applications

32

importation

SWEET 2.0 Ontologies

33

Using SWEET

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

Mix-n-Match

• The hybrid example: – Collect a lot of different ontologies representing different terms, levels of concepts, etc. into a base form: RDF 35

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 36

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 37

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 38

39

40

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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

42

Extending the KR for a purpose

GeoMagneticActivity has ProxyRepresentation

Input

ProxyRepresentation (in Physical properties: State of Realm of Neutral Atmosphere) neutral atmosphere Spatial: GeophysicalIndex is a Kp is a GeophysicalIndex hasTemporalDomain: “daily” • Above 100km hasHighThreshold:

Specification needed for query to CEDARWEB

Instrument Parameter(s) Operating Mode Observatory (above 45N) Date/time when KP => 8 Conditions: • High geomagnetic activity Action: Return Data Date/time Return-type: data 43

Translating the Use-Case -

hasPhysicalProperties: NeutralTemperature, Neutral Wind, etc.

ctd.

Specification needed for

Physical properties: State of

query to CEDARWEB

neutral atmosphere Instrument Spatial:

Instrument

Observatory hasFilterCentralWavelength: Wavelength hasLowerBoundFormationHeight: Height Date/time Conditions: Realm of Neutral Atmosphere) activity High geomagnetic Kp is a GeophysicalIndex hasTemporalDomain: “daily” Action: Return Data hasHighThreshold: xsd_number = 8 Date/time when KP => 8 ArcticCircle is a GeographicRegion Return-type: data hasLatitudeBoundary: hasLatitudeUpperBoundary: 44

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 45

46

Representing processes

47

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 48

Query

• Querying knowledge representations in OWL and/or RDF • SPARQL for RDF http://www.sparql.org/ http://www.w3.org/TR/rdf-sparql-query/ • OWL-QL (for OWL) and http://projects.semwebcentral.org/projects/owl -ql/ • XQUERY (for XML) • SeRQL (for SeSAME) • RDFQuery (RDF) • Few as yet for natural language representations 49

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 50 • Minimal properties to start, add only when needed

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) 51

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 52

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

53

Extras

54

Selected Technical Benefits

1. Integrating Multiple Data Sources 2. Semantic Drill Down / Focused Perusal 3. Statements about Statements 4. Inference 5. Translation 6. Smart (Focused) Search 7.

Smarter Search … Configuration 8. 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

55

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 hasCoordinates #Ionosphere #magnetic name hasLowerBoundaryValue “Terrestrial Ionosphere” hasLowerBoundaryUnit “km” “100” Different line & text colors 56 represent different data sources

2: Drill Down /Focused Perusal

• The Semantic Web uses Uniform Resource Identifiers (URIs) to name things …#NeutralTemperature • 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 measuredby Internet ...#FPI …#Norway type operatedby locatedIn ...#ISR ...#MilllstoneHill …#EISCAT 57

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 hasSource #Danny’s hasDateTime hascolor 20031031 Red Ontologies Workshop, APL May 26, 2006 58

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 OperatesInstrument #Millstone Hill #Interferometer hasInstrument isOperatedBy hasTypeofData Measures hasOperatingMode hasMeaasuredData #VerticalMeans 59

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 name ont2:EduLevel ont2:Rain EduVO:K-12 60

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 – ….

61

7: Smarter Search / Configuration

62

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 63

64

65

8: Proof

• The logical foundations of the Semantic Web allow us to construct proofs that can be used hasCalibration #Critical #FlatField Dataset to improve transparency, understanding, and trust hasPeerReview • Proof and Trust are on going research areas for #Solar Physics Paper the Semantic Web: e.g., See PML and Inference Web “Critical Dataset has been calibrated with a flat field program that is published In the peer reviewed literature.” 66

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, … 67

http://iw.rpi.edu

Inference Web Infrastructure (McGuinness, et.al., 2004 http://www.ksl.stanford.edu/KSL_Abstracts/KSL-04-03.html

)

Semantic Discovery Service

OWL-S/BPEL

(DAML/SNRC)

N3

CWM (NSF TAMI) JTP (DAML/NIMD)

KIF

Files/WWW Proof Markup Language (PML) Trust Toolkit IWTrust IW Explainer/ Abstractor IWBrowser

Trust computation End-user friendly visualization Expert friendly Visualization

SPARK (DARPA CALO)

SPARK-L

Justification IWSearch UIMA (DTO NIMD

Text Analytics

Exp Aggregation) Provenance IWBase

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.

search engine based publishing provenance registration 68

SW Questions & Answers

Users 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 An abstracted explanation 69 (this graphical interface done by Batelle supported by Stanford KSL)

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 70

DOLCE Physical-body

DOLCE + SWEET

= SWEET < SWEET  BodyofGround, BodyofWater,… Material-Artifact Physical-Object Infrastructure, Dam, Product,… LivingThing, MarineAnimal Substance Amount-of-Matter Activity HumanActivity  Phenomena Physical-Phenomenon Process Process StateOfMatter State Quality Quantity, Moisture,… Basalt,… Physical-Region Ordovician,… Temporal-Region Courtesy: Boyan Brodaric

Benefits

full coverage rich relations home for orphans single superclasses

Issues

individuals (e.g. Planet Earth) roles (contaminant) features (SeaFloor) 71

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

72 Courtesy: Boyan Brodaric

CF attributes NC basic attributes IRIDL attributes/objects SWEET Ontologies (OWL) CF data objects Location CF Standard Names (RDF object) CF Standard Names As Terms IRIDL Terms SWEET as Terms Gazetteer Terms Search Terms Blumenthal 73

Data Servers

IRI RDF Architecture

Ontologies bibliography Start Point MMI JPL Standards Organizations Sesame RDF Crawler RDFS Semantics Owl Semantics SWRL Rules SeRQL CONSTRUCT Search Queries Search Interface Location Canonicalizer Time Canonicalizer Blumenthal 74

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:Elep hant)(Set(x,x1,x2,x3)

∧

~(x1

∈

x

∧

x2

∈

x

∧

x3

∈

x))

75

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) 76

77

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Building SKOS

• ThManager • Protégé (4) plugin for SKOS 79

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) 80

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.

81

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) ∧ uncle(?x,?z) brother(?y,?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 83

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) ∧ hasUncle(?x1,?x3) hasBrother(?x2,?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 Student s are Person s, 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 86

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

temperature from a and

land surface global climate model

.

• Classes/ concepts: – Temperature – Surface (sea/ land) – Model – Climate – Global – Projection – Display … 87

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 • ….

88

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 89