Semantic technologies implementation of a use case, ontology engineering, inference, triple stores,

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Semantic technologies(implementation of a use
case, ontology engineering, inference, triple
stores,)

• Peter Fox (RPI)
• ESIP Summer Meeting
• Santa Barbara CA, 2009, July 7, 1030-1200pm

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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.

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

• Cover language representation choices, and
  knowledge engineering
• Pull apart the use case
• Tools and services
• Architecture considerations and design choices

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Languages

• OWL (1 and 2)
• RDFS ()
• SKOS
• RIF (OWL 2 RL)
• SPARQL
• OWL-S

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RDFS

• Note XMLS not an ontology language
• Changes format of DTDs (document schemas) to be
  XML
• Adds an extensible type hierarchy
• Integers, Strings, etc.
• Can define sub-types, e.g., positive integers
• RDFS is recognisable as an ontology language
• Classes and properties
• Sub/super-classes (and properties)
• Range and domain (of properties)

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However

• RDFS too weak to describe resources in sufficient
  detail
• No localized range and domain constraints
• Cant say that the range of hasChild is person
  when applied to persons and elephant when applied
  to elephants
• No existence/cardinality constraints
• Cant say that all instances of person have a
  mother that is also a person, or that persons
  have exactly 2 parents
• No transitive, inverse or symmetrical properties
• Cant say that isPartOf is a transitive property,
  that hasPart is the inverse of isPartOf or that
  touches is symmetrical
• Difficult to provide reasoning support
• No native reasoners for non-standard semantics
• May be possible to reason via First Order
  axiomatisation

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RDFS

• Worth taking a look at but there is not a lot of
  information readily available (as yet)

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OWL requirements

• Desirable features identified for Web Ontology
  Language
• Extends existing Web standards
• Such as XML, RDF, RDFS
• Easy to understand and use
• Should be based on familiar KR idioms
• Formally specified
• Of adequate expressive power
• Possible to provide automated reasoning support

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The OWL language

• Three species of OWL
• OWL full is union of OWL syntax and RDF
• OWL DL restricted to FOL fragment (¼ DAMLOIL)
• OWL Lite is easier to implement subset of OWL
  DL
• Semantic layering
• OWL DL ¼ OWL full within DL fragment
• DL semantics officially definitive
• OWL DL based on SHIQ Description Logic
• In fact it is equivalent to SHOIN(Dn) DL
• OWL DL Benefits from many years of DL research
• Well defined semantics
• Formal properties well understood (complexity,
  decidability)
• Known reasoning algorithms
• Implemented systems (highly optimized)

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OWL 1 Class Constructors
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OWL 1 axioms
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OWL 2

• http//www.w3.org/2007/OWL/wiki/OWL_Working_Group
• http//www.w3.org/2007/OWL/wiki/ImageOwl2-refcard
  _2008-09-24.pdf
• Semtech slides from the W3 OWL 2 panel ask me
  for these
• Property chaining
• Numerics

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OWL 2 RL

• http//www.w3.org/TR/owl2-profiles/
• http//ivan-herman.name/2009/04/27/simple-owl-2-rl
  -service/

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SKOS properties

• skosnote
• e.g. Anything goes.
• skosdefinition
• e.g. A long curved fruit with a yellow skin and
  soft, sweet white flesh inside.
• skosexample
• e.g. A bunch of bananas.
• skosscopeNote
• e.g. Historically members of a sheriff's retinue
  armed with pikes who escorted judges at assizes.
• skoshistoryNote
• e.g. Deleted 1986. See now Detention,
  Institutionalization (Persons), or
  Hospitalization.
• skoseditorialNote
• e.g. Confer with Mr. X. re deletion.
• skoschangeNote
• e.g. Promoted love to preferred label, demoted
  affection to alternative label, Joe Bloggs,
  2005-08-09.

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SKOS core and RDFS/OWL

• Disjoint?
• Should skosConcept be disjoint with
• rdfProperty ?
• rdfsClass ?
• owlClass ?
• DL?
• Should SKOS Core be an OWL DL ontology?
• Means not allowing flexibility in range of
  documentation props
• It is now (2008)! OWL-Full

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Summaries

• Michael Dennys Table (a bit out of date)
• http//www.xml.com/2004/07/14/examples/Ontology_Ed
  itor_Survey_2004_Table_-_Michael_Denny.pdf
• ESW Wiki http//esw.w3.org/topic/SemanticWebTools
  

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Engineering an ontology to the ground
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Editors

• Protégé (http//protégé.stanford.edu)
• SWOOP (http//mindswap.org/2004/SWOOP see also
  http//www.mindswap.org/downloads/)
• Altova SemanticWorks (http//www.altova.com/downlo
  ad/semanticworks/semantic_web_rdf_owl_editor.html)
  
• SWeDE (http//owl-eclipse.projects.semwebcentral.o
  rg/InstallSwede.html), goes with Eclipse
• Medius
• TopBraid Composer and other commercial tools
• CMAP Ontology Editor (COE) (http//cmap.ihmc.us/co
  e)

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Protégé

• http//protege.stanford.edu/
• http//protegewiki.stanford.edu/index.php/Protege-
  OWL
• Please check version compatibility when choosing
  your development otions. E.g. 3.4 v. 4.0
• Do you have plugins you like? They may not yet be
  available in 4.0
• e.g. Prompt not compatible with version 4.0)

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Triple Stores

• Jena (http//jena.sourceforge.net/)
• SeSAME/SAIL (http//www.openrdf.org/)
• KOWARI (http//www.kowari.org/) -gt
• Mulgara (http//www.mulgara.org/)
• Redland (http//librdf.org/index.html)
• Oracle (!)
• Many others (relational, object-relational)

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Software development tools

• Protégé, w/ plug-ins - some better than others
• SWOOP (OWL analyzer species validator,
  partitioner)
• Jena (http//jena.sourceforge.net/)
• ELMO ()
• Eclipse (full integrated development environment
  for Java http//www.eclipse.org/)
• Top Quadrant suite
• Sandsoft (Sandpiper Software)
• see Semantic Technologies 2007/8/9

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Implementing semantics

• Query
• Reasoning
• Rules
• A combination?
• Use cases are the key to guide you

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Reasoners (aka Inference engines)

• Pellet
• Racer (and Racer Pro)
• SHER (IBM) http//www.alphaworks.ibm.com/tech/sher
  
• Medius KBS
• FACT
• fuzzyDL
• KAON2
• MSPASS
• QuOnto
• Jess (for Rules)

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Implementation Basics

• Review your documented use case with team and
  experts
• Go into detail of your ontology test it using
  the tools you have
• We will look at the use case document and examine
  the actors, process flow, artifacts, etc.
• You will start to develop a design and an
  architecture (more on architecture and middleware
  next week)
• Keep in mind that it is more flexible to place
  the formal semantics between/ in your interfaces,
  i.e. between layers and components in your
  architecture, i.e. between users and
  information to mediate the exchange

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Actors

• The initial analysis will often have many human
  actors
• Begin to see where these can be replaced with
  machine actors may require additional
  semantics, i.e. knowledge encoding
• If you are doing this in a team, take steps to
  ensure that actors know their role and what
  inputs, outputs and preconditions are expected of
  them
• Often, you may be able to run the use case
  (really the model) before you build anything

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Process flow

• Each element in the process flow usually denotes
  a distinct stage in what will need to be
  implemented
• Often, actors mediate the process flow
• Consider the activity diagram (and often a state
  diagram) as a means to turn the written process
  flow into a visual one that your experts can
  review
• Make sure the artifacts and services have an
  entry in the resources section
• Often the time you may do some searching

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Preconditions

• Often the preconditions are very syntactic and
  may not be ready to fit with your
  semantically-rich implementation
• Some level of modeling of these preconditions may
  be required (often this will not be in your first
  pass knowledge encoding which focuses on the main
  process flow, goal, description, etc.)
• Beware of using other entities data and services
  policies, access rights, registration, and cost

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Artifacts

• Add artifacts that the use case generates to the
  resources list in the table
• It is often useful to record which artifacts are
  critical and which are of secondary importance
• Be thinking of provenance and the way these were
  produced, i.e. what semantics went into them and
  produce suitable metadata or annotations
• Engage the actors to determine the names of these
  artifacts and who should have responsibility for
  them (usually you want the actors to have
  responsibility for evolution)

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Reviewing the resources

• Apart from the artifacts and actor resources, you
  may find gaps
• Your knowledge encoding is also a resource, make
  it a first class citizen, i.e. give it a
  namespace and a URI
• Sometimes, a test-bed with local data is very
  useful as you start the implementation process,
  i.e. pull the data, maybe even implement their
  service (database, etc.)

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Back to the knowledge encoding

• Declarative in CL, OWL (probably OWL-DL), RDF,
  SKOS?
• Need rules?
• Need query?
• Science expert review and iteration
• Means you need something that they can review,
  with precise names, properties, relations, etc.
• The knowledge engineering stage is much like a
  software engineering process

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Knowledge engineering

• Mostly choose OWL-DL (and OWL 2)
• We may need to go to OWL 2 for numerical
  comparisons and if so, separate your OWL 1 from
  OWL 2 representations
• The interplay between tools like Protégé and CMAP
  maybe very important in implementing a knowledge
  base that has just enough

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Implementation Basics

• Review documented use case now
• Go into detail of the ontology
• Now we will look at the use case document and
  examine the actors, process flow, artifacts, etc.
• Start thinking of a design and an architecture
• Semantics between/ in your interfaces

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Roles and skill-sets

• Facilitator changes slightly for implementation
  - sometime the facilitator becomes chief
  architect, sometimes steps back
• Domain experts are needed for expert review
  (domain literate, know resources data,
  applications, tools, etc)
• You are the modeler (to extract objects, triples)
• You are likely to play the role of a software
  engineer (architecture, technology) but you can
  also ask someone for help with this
• Document, document, document
• It is social a team effort

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Summary

• By now, the reality of going into complete detail
  for the knowledge representation should be
  apparent
• Keeping it simple is also very important as you
  begin to implement
• Being prepared to iterate is really essential
• Now is the time to validate your ontology with
  domain experts and your team, use the tools
• The next stage is to choose your technology
  components and build and test

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E.g. use cases and implementation

• Apple orchard sensor network
• Semantic mediawiki
• Stargazer
• OWL-DL, Jena, ELMO, Pellet
• Diet exchange portal
• RDF, Perl, SPARLQL, re-use
• BCO-DMO faceted search

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Inventory

• Refer to the Resources
• Files
• Databases
• Catalogs
• Existing UI
• Services
• User database/ security
• Logging
• Backup/ archive

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Limited interoperability

• WSCommon
• Web Coverage Service
• Web Feature Service
• Web Mapping Service

Geo App2
App3
Geo App1
DBn
DB2
DB3

DB1
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The Astronomy approach data-types as a service
Limited interoperability

• VOTable
• Simple Image Access Protocol
• Simple Spectrum Access Protocol
• Simple Time Access Protocol

VO App2
VO App3
VO App1
VO layer
DBn
DB2
DB3

DB1
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Web Serv.
API
Web Portal
Query, access and use of data
Semantic mediation layer Ontology - capturing
concepts of Parameters, Instruments, Date/Time,
Space, Event, Feature, Data Product (and
associated classes, properties) and Service
Classes. Maps queries to underlying data.
Generates access requests for metadata, data.
Allows queries, reasoning, analysis, new
hypothesis generation, testing, explanation, etc.
Data as Service
Metadata, schema, data
DB
DB2
DB3

DB1
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Implementing

• Lets take an example
• VSTO
• Representative but does not exercise all semantic
  web capabilities

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Web Service
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Additional middleware

• Web server, Tomcat are essential (Axis)
• MySQL (or similar) is very handy to have
• OPeNDAP for data access and transport

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Web Service
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Infrastructure

• Protégé-OWL-API
• http//protege.stanford.edu/plugins/owl/api/index.
  html
• http//protege.stanford.edu/plugins/owl/api/guide.
  html
• Jena (Java API for RDF and OWL)
• http//protege.stanford.edu/plugins/owl/jena-integ
  ration.html
• http//jena.sourceforge.net/
• Migrate to other triple stores when needed

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Using Protégé

• Load VSTO into Protégé 3.4beta
• Generate Java-OWL classes from Tool menu
• Review other tools for generating code stubs

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Examine some of the code

• Java Factory class
• Code stubs and myclass
• VSTO code base browse it

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Jena
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Infrastructure

• Reasoner DIG/Pellet
• http//protegewiki.stanford.edu/index.php/ProtegeR
  easonerAPI
• SPARQL
• http//www.w3.org/2001/sw/DataAccess/tests/impleme
  ntations
• Spring (Application Framework - optional)
• http//www.springframework.org/
• Eclipse (IDE)
• http//www.eclipse.org/

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Software development

• Junit (generated in Eclipse)
• http//smi-protege.stanford.edu/repos/protege/owl/
  trunk/junit.properties.template
• Faceted browsing mspace, jspace

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Metadata

• Migrate metadata into ontologies instances,
  choose how you will populate them
• Manual okay to start with sufficient annotation
• Scripted preferred
• rdfs_comment essential
• Choose what you will not, cannot move
• W3 Recommendations like GRDDL are very useful

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Services

• If you are going to put up services, include an
  end-point and a link to your WSDL (or SAWSDL)
• At this point, developing a full services
  ontology, e.g. in OWL-S may be beyond the initial
  implementation

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Semantic Web Services
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Semantic Web Services
OWL document returned using VSTO ontology - can
be used both syntactically or semantically
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Result/ outcome

• Refer to the use case document
• Check the expected outcome and see if the test
  (to verify outcome) is complete
• Document all variations, note alternate flows
• Document in sufficient detail that someone else
  could come along and re-produce your work
• Include URLs for access, etc.

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Summary

• Architectural design needs to take into account
  existing resources that you will leverage
• Keeping it simple is also very important as you
  begin to implement
• Take time to learn the tools and the supporting
  APIs look at existing examples and working code
• Being prepared to iterate is really essential

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Tutorial Summary

• Many different options for ontology querying -
  none are standard
• RDF query is most advanced
• Inference needs and choice will depend on
  descriptive requirements (e.g. DL, Full, RDF,
  etc.)

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Reference material
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Terminology

• Ontology (n.d.). The Free On-line Dictionary of
  Computing. http//dictionary.reference.com/browse/
  ontology
• An explicit formal specification of how to
  represent the objects, concepts and other
  entities that are assumed to exist in some area
  of interest and the relationships that hold among
  them.
• Semantic Web
• An extension of the current web in which
  information is given well-defined meaning, better
  enabling computers and people to work in
  cooperation, www.semanticweb.org
• Primer http//www.ics.forth.gr/isl/swprimer/

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

• The triple subject-object-predicate
• interferometer is-a optical instrument
• Fabry-Perot is-a interferometer
• Optical instrument has focal length
• Optical instrument is-a instrument
• Instrument has instrument operating mode
• Data archive has measured parameter
• SO2 concentration is-a concentration
• Concentration is-a parameter
• 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