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Ontology 101: An Introduction to Knowledge
Representation, the Web Ontology Language (OWL) &
Ontology Development
Elisa Kendall, Thematix & Deborah McGuinness, RPI / McGuinness Associates
6 June 2011
2. + Content management
Mars was photographed by the Hubble Space Telescope in August 2003 as 2 2
the planet passed closer to Earth than it had in nearly 60,000 years.
Image Credit: NASA, J. Bell (Cornell U.) and M. Wolff (SSI)
A sunset on Mars creates a glow due to the
presence of tiny dust particles in the
atmosphere. This photo is a combination of
four images taken by Mars Pathfinder, which
landed on Mars in 1997. Image credit:
NASA/JPL
Recent images from instruments on board the
Mars Reconnaissance Orbiter take much more
detailed, narrower views of specific features of the
Martian surface. Image credit: NASA/JPL
The Planetary Data Store (PDS) is a distributed repository of 40+ years’ imagery & data
taken by a range of instruments on many diverse missions, available for scientific
research.
Copyright © 2011 Thematix & McGuinness Associates
3. + Smart search
3 3
Provenance/sources for tracking family members in the 19th
century include early census data (often error prone), military
records, passenger & immigration lists, online documents (e.g.,
county histories, church histories, etc.)
• Historical/forensic research requires cross-domain search of a wide variety of resources
within a given geo-spatial/temporal context
• Similar capabilities are essential for business intelligence, law enforcement, government
applications – all require terminology reconciliation
Copyright © 2011 Thematix & McGuinness Associates
4. + Merchandising
4 4
Attribute 1
Attribute 2
Leg Room
Attribute 4
Attribute 5
Attribute 5
On-Off Access
“Comfort- Attribute 7
Attribute 8
Oriented” Attribute 9
Flight Media Options
Attribute 11
Attribute 12
Meal Options
Attribute 14
Attribute 15
Time of Day
Attribute 17
Attribute 18
Distance to Gate
Attribute 20
Attribute 21
Flight Characteristics
Attribute N
…
Copyright © 2011 Thematix & McGuinness Associates
5. + Search Engine Optimization
5 5
Use search engines as a front door to transaction
Dramatically increase relevance by inclusion of more
meaningful, synonymic tags
Enrich search results, adding ratings, video, prices,
avails, amenities, locality etc.
Copyright © 2011 Thematix & McGuinness Associates
6. + Tutorial Outline
6 6
Introduction to Knowledge Representation
OWL Basics
Tools & Applications
Copyright © 2011 Thematix & McGuinness Associates
7. + Part 1: Introduction to Knowledge Representation
7 7
A little background and a few definitions
Layers of abstraction & conceptual modeling
Classifying ontologies
A little methodology
Copyright © 2011 Thematix & McGuinness Associates
8. + Definitions
8 8
An ontology is a specification of a conceptualization.
– Tom Gruber
Knowledge engineering is the application of logic and ontology to the task of
building computable models of some domain for some purpose. – John Sowa
Artificial Intelligence can be viewed as the study of intelligent behavior
achieved through computational means. Knowledge Representation then is
the part of AI that is concerned with how an agent uses what it knows in
deciding what to do. – Brachman and Levesque, KR&R
Knowledge representation means that knowledge is formalized in a symbolic
form, that is, to find a symbolic expression that can be interpreted. – Klein and
Methlie
The task of classifying all the words of language, or what's the same thing, all
the ideas that seek expression, is the most stupendous of logical tasks.
Anybody but the most accomplished logician must break down in it utterly;
and even for the strongest man, it is the severest possible tax on the logical
equipment and faculty. – Charles Sanders Peirce, letter to editor B. E. Smith of
the Century Dictionary
Copyright © 2011 Thematix & McGuinness Associates
9. + What is an Ontology?
An ontology specifies a rich description of the
9 9
Terminology, concepts, nomenclature
Properties explicitly defining concepts
Relations among concepts (hierarchical and lattice)
Rules distinguishing concepts, refining definitions and relations (constraints,
restrictions, regular expressions)
relevant to a particular domain or area of interest.
Copyright © 2011 Thematix & McGuinness Associates
10. + Logic and Ontology
10 10
Predicate logic is harder to read than the original English, but is
more precise:
Every semi-trailer truck has at least 3 axles.
(∀ x)(((SemiTrailerTruck(x) ∧ (∃ y)(SemiTrailer(y) ∧ (hasPart (x,y))) ∧
(SemiTrailerTruck(x) ∧ (∃ z)(TractorUnit(z) ∧ (hasPart (x,z))))
⊃ (∃ s)(set(s) ∧ (count(s,(≥3))
∧ (∀ w)(member(w,s) ⊃ (Axle(w) ∧ hasPart(x,w))) )).
Logic is a simple language with few basic symbols.
The level of detail depends on the choice of predicates – these
predicates represent an ontology of the relevant concepts in the
domain.
Different choices of predicates represent different ontological
commitments.
* Derived from Knowledge Representation: Logical, Philosophical, and Computational Foundations,
John F. Sowa, Brooks/Cole, Pacific Grove, CA, 2000.
Copyright © 2011 Thematix & McGuinness Associates
11. + Ontology-based Technologies
11 11
Ontologies provide a common vocabulary for use by independently
developed resources, processes, services
Agreements among organizations sharing common services can be
made with regard to their usage; the meaning of relevant concepts can
be expressed unambiguously
By composing / mapping ontologies and mediating terminology across
participating events, resources and services, independently-developed
services can work together to share information and processes
consistently, accurately, and completely
Ontologies also ensure
Valid conversations among agents to collect, process, fuse, and
exchange information
Accurate searching by ensuring context using concept definitions
and relations instead of/in addition to statistical relevance of
keywords
Copyright © 2011 Thematix & McGuinness Associates
12. + Features of KR languages
Vocabulary – a collection of symbols 12 12
Domain-independent logical symbols (e.g., ∀ or ⊃)
Domain-dependent constants, identifying individuals, properties, or
relations in the application domain or universe of discourse
Variables, whose range is governed by quantifiers
Punctuation that separates or groups other symbols
Syntax –
formation rules that determine how symbols can be combined in well-
formed expressions
rules may be stated in a linear grammar, graph grammar, or independent
abstract syntax
Semantics –
a theory of reference that determines how the constants and variables
are associated with things in the universe of discourse
a theory of truth that distinguishes true statements from false statements
Rules of Inference –
rules that determine how one pattern can be inferred from another
if the logic is sound, the rules of inference must preserve truth as
determined by the semantics
Copyright © 2011 Thematix & McGuinness Associates
13. + Logic
Logics vary from classical FOL along six dimensions: 13 13
Syntax
Subsets limit permissible operators or combinations, e.g., propositional logic
(without quantifiers), Horn-clause (excludes disjunctions in conclusions, such as
Prolog), terminological or definitional logics (containing additional restrictions,
e.g., description logics)
Proof Theory restricts or extends permissible proofs:
• Intuitionistic logic and relevance logic rule out certain extraneous information
• Non-monotonic logics allow introduction of default assumptions
• Access-limited logic restricts the number of times a proposition can be used in a proof;
Linear logic allows a proposition to be used only once
• Modal logic incorporates modal auxiliaries (◊p means p is possibly true; p means p is
necessarily true), temporal logic extends model logic to include always, sometimes
• Intensional logics express concepts such as need, ought, hope, fear, wish, believe, know,
expect, and intend
Model Theory modifies the truth value of statements in terms of some model of the
world: classical FOL is two-valued; a three-valued logic introduces unknowns;
fuzzy logic uses the same notation as FOL but with an infinite range of certainty
factors (1.0 to 0.0)
Ontology – frameworks may include support for built-in components, such as set
theory or time
Meta-language – language for encoding information about objects
Copyright © 2011 Thematix & McGuinness Associates
14. + Description logic
A family of logic-based Knowledge Representation formalisms 14 14
Descendants of semantic networks and KL-ONE
Describe domain in terms of concepts (classes), roles (relationships), and
individuals (instances)
Distinguished by
Formal semantics
• Decidable fragments of FOL
• Closely related to Propositional, Modal, and Dynamic Logics
Provision of inference services
• Sound and complete decision procedures for key problems
• Implemented systems (highly optimized)
Applications include
Configuration – product configurators, consistency checking, constraint
propagation, first significant industrial application (e.g., CLASSIC)
Ontologies – ontology engineering (design, maintenance, integration),
reasoning with ontology-based mark-up, service description and discovery
Databases – consistency of conceptual schemata, schema integration, query
subsumption (w.r.t. conceptual schemata)
Copyright © 2011 Thematix & McGuinness Associates
15. + Knowledge bases, databases, & ontology
15 15
An ontology is a conceptual model of some aspect of a particular
universe of discourse (or of a domain of discourse)
Typically, ontologies contain only “rarified” or “special” individuals,
metadata, representing elemental concepts critical to the domain
A knowledge base is a persistent repository for
Ontology & metadata representing individuals, facts, & rules about how they
can be combined or relate to one another
Metadata, facts & rules only – in some applications and frameworks the
ontology is separately maintained
Most inference engines require in-memory deductive databases for
efficient reasoning (including commercially available reasoners)
A knowledge base may be implemented in a physical, external
database, such as a relational database, but reasoning is typically
done on a subset (partition) of that knowledge base in memory
Copyright © 2011 Thematix & McGuinness Associates
16. + Reasoning & Truth Maintenance
16 16
Reasoning is the mechanism by which the assertions made in an
ontology and related knowledge base are evaluated by an inference
engine.
In classical logic, the validity of a particular conclusion is retained
even if new information is received.
This may change if some of the preconditions are actually
hypothetical assumptions invalidated by the new information.
The same idea applies for arbitrary actions – new information can
make preconditions invalid.
Generally, there are two issues that a reasoner must address:
If some conclusion is invalid, which other conclusions are also invalid?
If some action cannot be performed, which others are at risk?
The “housekeeping” associated with tracking the threads that support
answering these questions is called truth maintenance.
Copyright © 2011 Thematix & McGuinness Associates
17. + Negation
If all new information is “positive”, then all prior conclusions should 17 17
remain valid.
Problems arise if new information negates a prior assumption,
causing it to be withdrawn.
What does it mean to negate (withdraw) an assumption?
Conclusive information is not available?
The assumption cannot be proven?
The assumption is not provable using certain methods?
The assumption is not provable given a fixed quantity of time?
The answers to these questions can result in different definitions of
negation and differing interpretations by non-monotonic reasoners.
Solutions include chronological and “intelligent” backtracking
algorithms, heuristics, circumscription algorithms, justification or
assumption based retraction, and so forth, depending on the reasoner
and methods used for truth maintenance.
Reasoning efficiency is dependent, in part, on the algorithms applied
for truth maintenance.
Copyright © 2011 Thematix & McGuinness Associates
18. + Explanations and Proofs
18 18
When a reasoner draws a particular conclusion, many users and
applications want to understand why?
Primary motivations include interoperability, reuse, and trust
Understanding the provenance of the information and results is
crucial, especially when web-based information is involved
What information sources were used (source)
How recently they were updated (currency)
How reliable these sources are (authoritativeness)
Was the information directly available or derived, and if derived, how
(method of reasoning)
Methods used to explain why a reasoner reached a particular
conclusion include explanation generation and proof specification
Copyright © 2011 Thematix & McGuinness Associates
19. + Abstraction Layers
• Identify subject areas
Contextual 19 19
Ontology
Conceptual
• Define the meaning of things in the organization
• Describe the logical representation of properties Ontology, Conceptual ER, Conceptual
Logical
Business Process …
• Describe the physical means by which data is stored ER, Relational, XML Schema
Physical
XML, source code, scripting languages,
• Represent the coding language on a specific development platform stored procedures…
Definition
Physical KBs,
• Hold the values of the properties applied to the data in a schema databases, asset repositories…
Instance
*Layering diagram courtesy Kenn Hussey
Knowledge Representation / Management for Large Scale Applications
Provide broad metadata, process, service & asset management facilities (including
feedback/lessons learned…)
Enable rich cross-domain, cross-process, cross organizational modeling supported by
mapping & transformation services to provide maximum flexibility, interoperability
Leverage standards and best practices in information architecture, metadata modeling,
management, registration, and governance, and asset management & registration
Provide incremental reasoning capabilities for model validation, transformation services
Repeatable, reusable, interoperable
Copyright © 2011 Thematix & McGuinness Associates
20. + Hypothetical Conceptual Model “EU-Rent”
20 20
Produced using Embarcadero EA/Studio Business Modeler Edition, courtesy Kenn Hussey
Copyright © 2011 Thematix & McGuinness Associates
21. + Hypothetical Logical Model “EU-Rent”
21 21
Produced using Embarcadero ER/Studio, courtesy Kenn Hussey
Copyright © 2011 Thematix & McGuinness Associates
22. + Hypothetical Physical Model “EU-Rent”
22 22
Produced using Embarcadero ER/Studio, courtesy Kenn Hussey
Copyright © 2011 Thematix & McGuinness Associates
23. + Classifying ontologies
23 23
Classification techniques are as diverse
as conceptual models; and generally
include understanding
KR System
Level of Expressivity
Level of Complexity / Structure
OO Software Model
Granularity
Entity – Relationship
Target Usage, Relevance Model
Amount of Automation, Reasoning Requirements Concept Map
Prescriptive vs. Descriptive / Reliability / Level Topic Map
of Authoritativeness
Level of Expressivity
Design Methodology Database Schema
Governance XML Schema
Vocabulary Management, Metrics Hierarchical Taxonomy
Simple Taxonomy
Glossary
Level of Complexity
Copyright © 2011 Thematix & McGuinness Associates
24. + Framework of dimensions
24 24
Semantic Dimensions
Expressiveness: represents how well a KR language addresses
increasingly complex semantics
Structure: represents how well an ontology encodes semantics, with
the same or less expressivity than the KR language
Granularity: represents the level of detail specified in an ontology
Pragmatic Dimensions
Intended use: the original use case(es), or purpose for developing a
particular ontology
Automated reasoning: the extent to which the ontology is designed
to be used for automated reasoning
Prescriptive vs. Descriptive: the extent to which an ontology was
intended to be used for descriptive purposes vs. normative
prescriptive use (i.e., with high degree of concern for correctness)
Reference: http://ontolog.cim3.net/cgi-bin/wiki.pl?OntologySummit2007
Copyright © 2011 Thematix & McGuinness Associates
25. + Model dynamics
25 25
Model centric perspectives characterize the ontologies
themselves and are concerned with their structure, formalism
and dynamics.
Perspective One Extreme Other Extreme
Level of Least authoritative, broader Most authoritative, narrower,
Authoritativeness shallowly defined ontologies more deeply defined ontologies
Source of Passive (Transcendent) - Active (Immanent) - Structure
Structure Structure originates outside emerges from data or behavior
the system
Degree of Informal or primarily Formal, having rigorously
Formality taxonomic defined types, relations, and
theories or axioms
Model Dynamics Read-only, ontologies are Volatile, ontologies are fluid &
static changing
Instance Read-only, resource instances Volatile, resource instances
Dynamics are static change continuously
Copyright © 2011 Thematix & McGuinness Associates
26. + Application characteristics
26 26
Application centric perspectives are concerned with how
applications use and manipulate ontologies.
Perspective One Extreme Other Extreme
Control/Degree Externally focused, public Internally focused, private
of Manageability (little or no control) (full control)
Application Static (with periodic updates) Dynamic
Changeability
Coupling Loosely-coupled Tightly-coupled
Integration Focus Information integration Application integration
Lifecycle Usage Design Time Run Time
Copyright © 2011 Thematix & McGuinness Associates
27. + Language evaluation
27 27
KR Expressivity Requirements Reasoning Requirements
Classes
Exceptions
Slots/Attributes
Automatic Classification
Metaclasses
Number Restrictions
Complex Class Extensions (union, intersection) Inheritance
Subsumption Hierarchies Monotonic, Nonmonotonic
Value Restrictions Simple, Multiple
Behaviors, Procedural Definitions, Methods Procedural Support, Execution
Relations / Functions
Slots/Attributes
Subsumption Hierarchies Constraint Checking, Deduction
Built-in Functions, Equations, Formulae
Instances / Individuals / Facts
Axioms
Reasoning with Rules
Production Rules (forward and backward chaining)
* Derived from “Evaluating Knowledge Representation and Reasoning Capabilities of Ontology Specification Languages”,
Oscar Corcho and Asuncion Gomez-Perez, January 2002
Copyright © 2011 Thematix & McGuinness Associates
28. + Considerations
Intended use of ontologies, including domain requirements (e.g., 28 28
scientific and engineering apps require formulas, units of measure,
computations that may be challenging to represent)
Intended use of KRSs that implement them, including reasoning
requirements, questions to be answered
For distributed environments, the number and kinds of resources,
processes, services requiring ontologies – how distributed, how
unique, developed collaboratively or independently, dynamic
community participation or static
What kinds of transformations are required among processes,
resources, services to support semantic mediation
Ontology and KRS alignment / de-confliction / ambiguity resolution
requirements
Ontology and KRS composition requirements, dynamic vs. static
composition, in what environment and under what constraints
Performance, sizing, timing requirements of target environment
Copyright © 2011 Thematix & McGuinness Associates
29. + A Little Methodology …
29 29
Requirements, domain & use case analysis are critical
Develop initial source/reference material
Focus on system or application requirements
Iterative development starting with a “thread” that covers basic
capabilities can ground the work and prioritize decisions
Need to understand and communicate
Architectural trade-offs, cost & technical benefits
The nature of the information & kinds of questions that need to
be answered drive the architecture, approach, and ontology
scoping and design
Reuse standards and well-tested, available ontologies
whenever possible
Copyright © 2011 Thematix & McGuinness Associates
30. + What to look for
A controlled vocabulary
30 30
FAA & IATA airport codes, ACRISS car codes, …
A hierarchical or taxonomic structure (for query expansion)
Vehicle, Ground-based Vehicle, Wheeled Vehicle, Powered Vehicle, Automobile,
Sedan…
Knowledge supporting structured queries
Find all available hybrid SUVs or hybrid sedans that can seat four adults within a
reasonable taxi ride of Planet Hollywood, Las Vegas for 3-4 days the week of
June 20th
Efficient inference (i.e., limited expressive power) vs. increased
expressivity (potentially expensive or resource bounded
computation)
Custom reasoning for temporal relations, geospatial, dynamic
algorithm / equation evaluation, process-specific, conditional
operations
Computational tractability
Copyright © 2011 Thematix & McGuinness Associates
31. + Start with canonical definitions
General concepts as well as domain-specific knowledge 31 31
Basic starting point – cross-domain definitions
Namespace definitions, metadata, naming conventions, governance policies
Commonly used structures & vocabularies, such as domain-specific
vocabulary & messaging standards, international country & language codes
(ISO), national postal addressing or other government standards, industry
best practices
Common metadata for ontology & schema management (e.g., Dublin Core,
for documents & models, ISO 1087 for synonyms & similar relations, MIME
media types for images & multimedia, etc.)
Domain vocabularies must be prioritized, selected based on business
requirements, clear ROI
Common early targets include
Smart search (pull); customer experience & cross-sell / upsell (push)
Richer interoperability among trading partners
Service registration, description, discovery & management
Asset/artifact repository search & retrieval
Automated verification
Copyright © 2011 Thematix & McGuinness Associates
32. + IDEF5 (Integrated Definition Methods) Ontology
Capture Method Analysis
32 32
Layout a high-level architecture for ontology elements
Identify the relationships among elements – roles, domain,
interface, process, utility
For each ontology element
Describe its domain and scope, how it will be used
Identify example questions and anticipated/sample answers for the
application(s) it will support
Identify key stakeholders, ownership, maintenance, resources for
instance knowledge
Describe anticipated reuse/evolution path
Identify critical standards, resources that it must interoperate with,
dependencies
Resources
http://www.idef.com/IDEF5/html
http://www.kbsi.com/technology/methods/sbont.htm
Copyright © 2011 Thematix & McGuinness Associates
33. + Principles & Methods for Terminology Work
(ISO 704)
33 33
Methodology for describing concepts & terms
Uses ISO 1087 for terminology
Uses ISO 860 for terminology “harmonization” (alignment) methods
Basis for typical methods used for taxonomy development today
Describes how to flesh out definitions
Recommendations strategies for relating terms to one another
using standard vocabulary
ISO 1087 – great resource for language to describe kinds of
relationships, acronyms & other designations, preferred vs.
deprecated terms, etc.
ISO 860 augments this with recommendations for vocabulary
comparison
Copyright © 2011 Thematix & McGuinness Associates
34. + Part 2: OWL Basics
A quick intro to RDF 34 34
Basic OWL constructs: classes, properties & slots
Inheritance & constraints
Class & slot inheritance
Property, slot & slot value constraints
Individuals & literals
Disjoint & equivalence relations
New features of OWL 2
A few rules of thumb
Depth vs. breadth, naming, synonymous terms
Class vs. property value, class vs. individual
Inverse properties
Limiting scope
Copyright © 2011 Thematix & McGuinness Associates
35. + Semantic Web
35 35
"The Semantic Web is an extension of the current web in which
information is given well-defined meaning, better enabling computers
and people to work in cooperation."
-- Tim Berners-Lee
Copyright © 2011 Thematix & McGuinness Associates
36. + Guiding Principles
36 36
Historically, knowledge representation and reasoning systems have
operated under closed world assumptions
Uncertainty is magnified under open-world, “wild, wild web” conditions,
making reasoning much more difficult
Semantic web languages are designed to support less certainty, to
provide “better” search results, informed answers to questions, not
absolutes
Because they are based on XML, such languages can assist businesses in
leveraging existing investment in mark-up, content, and data
To augment business intelligence/analysis and knowledge mining
To support knowledge sharing and collaboration, augment enterprise
information integration
Enrich web services and other applications
Support policy-based applications and ensure compliance with policy
at a lower cost with higher potential ROI than traditional computing
methods
Copyright © 2011 Thematix & McGuinness Associates
37. + Resource Description Framework (RDF)
37 37
Describes relationships
Uses URIs used for naming
Language has
graph based model
RDF/XML serialization (exchange syntax)
other presentation syntaxes (N3, Turtle, …)
Specification, W3C presentations, tools are available at
Semantic Web: http://www.w3.org/standards/semanticweb/
Linked Data: http://www.w3.org/standards/semanticweb/data
RDF: http://www.w3.org/standards/techs/rdf#w3c_all
New RDF “next steps” revision working group under way to
make specific enhancements, “fixes” to the standard
RDF Working Group: http://www.w3.org/2011/rdf-wg/wiki/Main_Page
Copyright © 2011 Thematix & McGuinness Associates
38. + RDF Notation Options
38 38
Subject
http://semtech2011.semanticweb.com/travel.owl#Conference
Predicate
Graph: http://semtech2011.semanticweb.com/travel.owl#heldAt
http://semtech2011.semanticweb.com/travel.owl#HiltonSFUnionSquare
Object
<rdf:Description rdf:ID=“Conference"/>
XML/RDF: <semtech:heldAt rdf:resource="#HiltonSFUnionSquare"/>
</rdf:Description>
N3: semtech:Conference semtech:heldAt semtech:HiltonSFUnionSquare .
Copyright © 2011 Thematix & McGuinness Associates
39. + RDF Schema (RDFS)
An RDF vocabulary that provides for identifying: 39 39
classes,
subsumption (inheritance) relations for classes,
subsumption (inheritance) relations for properties,
domain and range for properties
Copyright © 2011 Thematix & McGuinness Associates
40. + RDF Schema (RDFS)
40 40
semtech:Hotel
rdfs:subClassOf
Graph: rdf:type
rdfs:Class semtech:ConferenceHotel
rdf:type
rdf:type
semtech:HiltonSFUnionSquare
<rdf:Description rdf:ID="Hotel">
XML/RDF: <rdf:type rdf:resource="http://www.w3.org/2000/01/rdf-schema#Class"/>
</rdf:Description>
<rdfs:Class rdf:ID=“ConferenceHotel">
<rdfs:subClassOf rdf:resource="#Hotel"/>
</rdfs:Class>
<semtech:ConferenceHotel rdf:ID=“HiltonSFUnionSquare“/>
Copyright © 2011 Thematix & McGuinness Associates
41. + The Web Ontology Language (OWL)
Two languages emerged in parallel to address semantic web 41 41
requirements
DAML-ONT, supported by the DARPA/DAML program
OIL (Ontology Inference Layer) developed by EU & US researchers
Merged DAML+OIL was submitted to the W3C 2002, formed the
basis for the WebOnt Working Group
OWL extends RDF Schema
Has an RDFS based syntax and reuses some RDF vocabulary (e.g.,
subClassOf, domain, range)
Adds rich primitives and redefines others (transitivity, inverse, cardinality
constraints, complex class definitions)
Describes the structure of a domain in terms of classes and
properties
Uses RDFS for class/property membership assertions (ground
facts), XML Schema Datatypes
OWL specifications became W3C recommendations in February
2004
OWL 2 specifications was adopted in October 2009
Copyright © 2011 Thematix & McGuinness Associates
42. + General nature of descriptions
42 42
a WINE
a LIQUID General Categories
a POTABLE
grape: chardonnay, ... [>= 1]
sugar-content: dry, sweet, off-dry Structured
color: red, white, rose Components
price: a PRICE
winery: a WINERY
grape dictates color (modulo skin) Interconnections
harvest time and sugar are related Between Parts
Copyright © 2011 Thematix & McGuinness Associates
43. + General nature of descriptions
43 43
Class a WINE
Superclass a LIQUID General Categories
a POTABLE
Number / Card
Restrictions grape: chardonnay, ... [>= 1]
sugar-content: dry, sweet, off-dry Structured
Roles / color: red, white, rose Components
Properties price: a PRICE
winery: a WINERY
Value
Restrictions grape dictates color (modulo skin) Interconnections
harvest time and sugar are related Between Parts
Copyright © 2011 Thematix & McGuinness Associates
44. + Description development
44 44
Define domain terms and inter-relationships
Define concepts in the domain (classes, nouns)
Identify subclass/superclass relationships
Identify attributes/properties/slots (verbs)
Identify any general properties (relations, functions, verbs)
Restrict slot values
Define a representative set of individuals – for testing & evaluation,
prototype development
Define individuals (instances)
Define relationships between individuals (filling in slots)
Copyright © 2011 Thematix & McGuinness Associates
45. + Classes & class hierarchy
45 45
A class is a concept in the domain
Vintage – a wine made from grapes grown in a specified year
A class of properties (flavor, body, color, sugar…)
A class is a collection of elements with similar properties
White wine – wines made from white grapes
White table wine – wines made from white grapes that are not
appellations or regional (not “quality wine” in the EU)
A class contains necessary conditions for membership (specific
varietal, field, micro-climate, date picked, date bottled)
Instances of classes
Andrew Murray 2005 Late Harvest Viognier
Robert M. Parker, Jr,’s Wine Advocate review, dated February 28, 2002
Los Olivos, Santa Barbara County, California, USA
Copyright © 2011 Thematix & McGuinness Associates
46. + Class inheritance
46 46
Classes are organized into subclass-superclass (or
generalization-specialization) hierarchies
True subclass relationships are the basis of a formal is-a
hierarchy
Classes are “is-a” related if an instance of the subclass is an
instance of the superclass
Classes may be viewed as sets
Subclasses of a class are comprised of a subset of the
superset
Examples
RedWine is a subclass of Wine
Every red wine is a wine or every instance of a red wine (e.g.,
Marietta Old Vines Red) is an instance of wine
NapaValleyWine is a subclass of CaliforniaWine
Every wine from Napa Valley is a wine from California
Copyright © 2011 Thematix & McGuinness Associates
47. + Levels in the class hierarchy
47 47
Different modes of development
Top-down - define the most general concepts first and then
specialize them
Bottom-up - define the most specific concepts and then organize
them into more general classes
Combination (typical – breadth at the top level and depth along a
few branches to test design)
Class inheritance is transitive
A is a subclass of B (white wine, dessert wine are subclasses of
wine)
B is a subclass of C (viognier is a subclass of white wine, late
harvest wine is a subclass of dessert wine)
therefore A is a subclass of C (late harvest viognier is a subclass of
white wine, dessert wine, & wine)
Copyright © 2011 Thematix & McGuinness Associates
48. + Classes in OWL 2
48 48
6 kinds of class expressions in OWL
5 anonymous
Definitions can be nested using set-theoretic constructs
Copyright © 2011 Thematix & McGuinness Associates
49. + Properties & slots
49 49
Slots in a class definition describe attributes of members of a
class
Each wine will have color, sugar content, flavor, body, etc.
Types of properties
“intrinsic” properties: flavor and color of wine
“extrinsic” properties: name and price of wine
parts: ingredients in a recipe
relations to other objects: producer of wine (winery)
Data and object properties
simple (datatype) contain primitive values (strings, numbers)
complex properties may contain other objects (e.g., a winery
instance)
Copyright © 2011 Thematix & McGuinness Associates
50. + Properties in OWL 2
50 50
Copyright © 2011 Thematix & McGuinness Associates
51. + Class & slot inheritance
51 51
A subclass inherits all the slots from its super class
If a wine has a name and flavor, a red wine also has a name and flavor
If a class has multiple super classes, it inherits slots and
restrictions from all of them
Latin is both an individual language and an ancient language. It inherits “has
attested literature: true” from ancient language and “has indigenous
name: lingua latina” from individual language
Copyright © 2011 Thematix & McGuinness Associates
52. + Property (slot) constraints / restrictions
Number restrictions describe or limit the number of possible values
a particular property can have 52 52
A language must be associated with at least one English name and at least
one French name
A language may be associated with zero or more Indigenous names
Cardinality – similar meaning to classical set theory, measures the
number of elements in the set (restriction class)
Cardinality – cardinality N means the class defined by the property
restriction must have exactly N values (individual or literal values)
Minimum cardinality - 1 means that there must be at least one value
(required), 0 means that the value is optional
Maximum cardinality - 1 means that there can be at most one value
(single-valued), N means that there can be up to N values (N > 1, multi-
valued)
Copyright © 2011 Thematix & McGuinness Associates
53. + Slot value constraints
53 53
Slot value type – defines the set of possible values for the
property
String: a string of characters (“McGinley Vineyard”)
Number: an integer or a float (3.5 acres)
Boolean: a true/false flag
Enumerated type: a list of allowed values (red, white, rose)
Filler: a single value (e.g., the color slot for a RedWine must be
filled with the single value “red”)
Object type – a class defined in an ontology (e.g., Winery is the
value restriction on the hasMaker slot on the class Wine)
Copyright © 2011 Thematix & McGuinness Associates
54. + Domain & range properties
54 54
In OWL and many other KR languages, relations (properties, slots) are
strictly binary
The domain & range represent the source & target arguments,
respectively, for the property
Domain of a slot – the class (or classes) that may have the slot − Wine
is the domain of the slot hasWineColor
Range of a slot – the class (or classes) to which slot values belong −
everything that fills the hasWineColor slot is an instance of the
enumerated class {red, white, rose}
Some KR languages that inherently support n-ary relations, such as
CL, do not make this distinction
More flexible, intuitively more like mathematics, where functions have
ranges (or return types) but not all relations are functions
Requires additional relations to specify argument order, which can be
critical for ontology alignment
Copyright © 2011 Thematix & McGuinness Associates
55. + Property inheritance
A subclass inherits all the slots of its superclass(es) 55 55
A subclass can add constraints to “narrow” the set of allowed
values
Make the cardinality range smaller
Replace a class in the range with a subclass
Copyright © 2011 Thematix & McGuinness Associates
56. + Individuals
56 56
An Individual (instance, object in other paradigms)
Any class that an individual is a member of, or is an individual of, is a type
of the individual
Any superclass of a class is an ancestor (or type) of the individual
Specify slot values for the individual
Slot values should conform to the constraints such as range, value type,
cardinality restrictions, etc.
Copyright © 2011 Thematix & McGuinness Associates
57. + OWL Individuals
57 57
Graco Garmin
Chevrolet
Sony
BMW
Copyright © 2011 Thematix & McGuinness Associates
58. + OWL Statements
58 58
Graco Garmin
Chevrolet
Sony
BMW
a Mini
Cooper
a Camaro LT
Convertible
Copyright © 2011 Thematix & McGuinness Associates
59. + OWL ObjectProperty
59 59
Graco Garmin
Chevrolet
Sony
<owl:ObjectProperty rdf:ID="builtBy">
BMW
<rdfs:range rdf:resource="#Enterprise"/>
<rdfs:domain rdf:resource="#DurableGood"/>
<owl:inverseOf rdf:resource="#hasBuilt"/>
</owl:ObjectProperty>
a Mini
Cooper
a Camaro LT
Convertible
Copyright © 2011 Thematix & McGuinness Associates
60. + OWL ObjectProperty
60 60
range Graco Garmin
Chevrolet
Sony
BMW
<owl:ObjectProperty rdf:ID="builtBy">
<rdfs:range rdf:resource="#Enterprise"/>
<rdfs:domain rdf:resource="#DurableGood"/>
<owl:inverseOf rdf:resource="#hasBuilt"/>
</owl:ObjectProperty>
a Mini
Cooper
a Camaro LT
Convertible
Copyright © 2011 Thematix & McGuinness Associates
61. + OWL ObjectProperty
61 61
range
Graco Garmin
Chevrolet
Sony
<owl:ObjectProperty rdf:ID="builtBy"> BMW
<rdfs:range rdf:resource="#Enterprise"/>
<rdfs:domain rdf:resource="#DurableGood"/>
<owl:inverseOf rdf:resource="#hasBuilt"/>
</owl:ObjectProperty>
a Mini
domain Cooper
a Camaro LT
Convertible
Copyright © 2011 Thematix & McGuinness Associates
62. + Inverse Properties
62 62
domain Graco Garmin
Sony Chevrolet
<owl:ObjectProperty rdf:ID="hasBuilt"> BMW
<rdfs:range rdf:resource="#DurableGood"/>
<rdfs:domain rdf:resource="#Enterprise"/>
<owl:inverseOf rdf:resource="#builtBy"/>
</owl:ObjectProperty>
a Mini
range Cooper
a Camaro LT
Convertible
Copyright © 2011 Thematix & McGuinness Associates
63. + OWL Class
63 63
Graco Garmin
Chevrolet
<owl:Class
Sony
rdf:ID="ManufacturingEnterprise"/>
BMW
<owl:Class rdf:ID="DiscreteManufacturingEnterprise">
<rdfs:subClassOf>
<owl:Class rdf:about="#ManufacturingEnterprise"/>
</rdfs:subClassOf>
</owl:Class>
Copyright © 2011 Thematix & McGuinness Associates
64. + Class Axioms
64 64
A ⊆ B where B
Subsumption (necessary)
B
is a class description
partial or primitive class A
Definition (necessary and sufficient)
C ≡ D where D
D
is a class description
complete or defined class
C
Courtesy Evan Wallace, NIST
Copyright © 2011 Thematix & McGuinness Associates
65. +
Meta-Properties – Global Cardinality Restriction
65 65
Range
Functional
Domain
Range
Inverse Functional
Courtesy Evan Wallace, NIST
Domain
Copyright © 2011 Thematix & McGuinness Associates
66. + Class Descriptions – Property Restriction
66 66
property P
Individual
of Class C
Quantified property restriction (type)
Universally quantified – allValuesFrom
Existentially quantified - someValuesFrom
hasValue property restriction (value)
Property cardinality restriction (# of values)
Courtesy Evan Wallace, NIST
Copyright © 2011 Thematix & McGuinness Associates
67. + Disjoint classes
A 67 67
B
Classes are disjoint if they cannot have common instances
Disjoint classes cannot have any common subclasses
If winery and wine are disjoint, then there is no instance that is
both a winery and a wine; there is no class that is both a
subclass of winery and a subclass of wine
Disjointness is often used to aid consistency checking
Disjointness is also helpful in teasing out subtle distinctions
among classes across multiple ontologies
Equivalence is also often used to identify the same concepts
across ontologies that may be named differently, or to name
classes defined through class axioms
Copyright © 2011 Thematix & McGuinness Associates
68. + New features in OWL 2
68 68
New property constructs
Reflexive, irreflexive, & asymmetric properties
Property chains
Disjoint properties
Keys
Self-reflexive properties
Qualified cardinality restrictions
Negative property assertions
New class axioms
Disjoint unions
Disjoint classes
Copyright © 2011 Thematix & McGuinness Associates
69. + More new features
Extended datatype handling
69 69
Better support for XML Schema Datatypes
New datatypes for OWL specifically (owl:real, owl:rational, rdf:PlainLiteral)
Custom datatype definition & use of xsd facets / datatype restrictions
New data range restrictions
Intersections, unions, complements
Support for punning
Limited support for a particular element to be defined as both a class &
individual, for example
Improved support for annotations (e.g., metadata about ontologies,
provenance, transformation detail, etc.)
See http://www.w3.org/TR/owl2-new-features/ &
http://www.w3.org/TR/owl2-quick-reference/ for detail
Current work specifically focused on provenance:
http://www.w3.org/2011/prov/wiki/Main_Page
Copyright © 2011 Thematix & McGuinness Associates
70. + Rules of Thumb
70 70
Cycles are common in many KR
systems, though rarely “a good thing”
Cycles are disallowed by some tools
because they prohibit “code
generation”, export -- including
RDF/OWL
Classes A, B, and C have equivalent
sets of instances
By many definitions, A, B, and C are
equivalent
Use owl:equivalentClass instead of
creating cycles
Copyright © 2011 Thematix & McGuinness Associates
71. + Siblings in the class hierarchy
71 71
All siblings should be specified at
roughly the same level of generality
Compare to section and subsections in
a book
Copyright © 2011 Thematix & McGuinness Associates
72. + Class specification
72 72
If a class has only one child, there
may be a modeling problem – often a
sign that a definition is incomplete
If the only Red Burgundy we have is
Côtes d’Or, why introduce the
subclass?
Subclasses of a class usually have
Additional properties
Additional slot restrictions
Participate in different relationships
Compare to bullets in a bulleted list
Copyright © 2011 Thematix & McGuinness Associates
73. +Creating levels & subclasses
73 73
If a class has a large number of
subclasses, it may be useful to
define intermediate levels
For example, in the domain of
wines, there are natural
groupings around wine color
However, if no natural
classification exists, the long list
may be appropriate
Copyright © 2011 Thematix & McGuinness Associates
74. + Inheritance, naming, synonyms
74 74
A “wine” is not a subclass of “wines”
A particular vintage should be classified as
an instance of the class Wines
Class
Class names should be either
all singular
all plural
instance-of
Synonym names for the same concept are not
Instance different classes
MariettaOldVinesRed
Many systems, metadata standards support
synonymous terms as part of a class definition
OWL allows defining necessary and
sufficiency condition definitions thereby
allowing synonym definitions to be “first
class” terms
Copyright © 2011 Thematix & McGuinness Associates
75. + Class vs. property value
75 75
Do concepts with different slot values become restrictions for
different slots?
How important is the distinction for the domain?
Class definitions for most domains should be fairly stable – i.e.,
they should not change frequently once the definitions are
established and individuals created
Copyright © 2011 Thematix & McGuinness Associates
76. + Class vs. individual
76 76
Individual instances are the most specific objects in an ontology
If concepts form a natural hierarchy, represent them as classes
If they will have instances below them, represent them as classes
Copyright © 2011 Thematix & McGuinness Associates
77. + Limiting scope
77 77
An ontology should not contain all the possible information
about the domain
No need to specialize or generalize more than the application
requires
No need to include all possible properties of a class
• Only the most salient properties
• Only the properties that the application requires
Ontologies of wine, food, and their pairings probably will not
include details such as:
Bottle size (half bottle, full bottle, magnum, …)
Label color
Wine bottle color (green, amber, …)
Copyright © 2011 Thematix & McGuinness Associates
78. + Part 3: Putting It All Together
78 78
Syntax Checking
Consistency Checking
Instance Data Analysis & Evaluation Services
Applications
Lighter Weight
Richer Applications
Examples – showing a combination of semantic web
components in action: TW Wine Agent
Understanding, Trust & Proof: InferenceWeb
eScience Applications: Virtual Solar Terrestrial Observatory,
SONet, & PopSciGrid
Leveraging Explanations in a Cognitive Assistant
Questions
Copyright © 2011 Thematix & McGuinness Associates
79. +
Syntax checking
For RDF & OWL Ontologies 79 79
RDF syntax checking, graph visualization
• W3C RDF Validator (http://www.w3.org/RDF/Validator/)
• Jena API & Toolkit (http://jena.sourceforge.net/)
OWL syntax checking, OWL dialect
determination
• OWL Consistency Checker
(http://clarkparsia.com/pellet/, also provides full OWL 2
DL reasoning capabilities)
• OWL 2 Validator (Univ. of Manchester,
http://owl.cs.manchester.ac.uk/validator/ )
• Protégé OWL (http://protege.stanford.edu/)
• Jena API & Toolkit (http://jena.sourceforge.net/)
Every tool provides unique capabilities;
sophisticated projects may require multiple
approaches
Tools listed are open source, commercial
options are also available
Copyright © 2011 Thematix & McGuinness Associates
80. + Consistency checking
Requirements are typically application specific 80 80
RDF vocabularies should be checked by an RDF validator or rule engine
Jena Semantic Web Framework - http://jena.sourceforge.net/
Pychinko, MindSwap’s Rete-based RDF-friendly rule engine – (CWM clone)
http://www.mindswap.org/~katz/pychinko/
OWL Ontologies should be run through a consistency checking reasoner
Pellet (open source, originally from Mindswap, supported by Clark & Parsia, LLC)
– http://clarkparsia.com/pellet/,
RacerPro – http://www.racer-systems.com/
FaCT++ – http://owl.man.ac.uk/factplusplus/
HermiT OWL Reasoner – http://www.hermit-reasoner.com/
KAON2 infrastructure for managing OWL-DL, SWRL, and F-Logic ontologies –
http://kaon2.semanticweb.org/
VIStology's ConsVISor OWL Consistency checker –
http://68.162.250.6:8080/consvisor/
OWL instance data may also be run through checking tools
TW Instance data evaluation - http://onto.rpi.edu/demo/oie/
Copyright © 2011 Thematix & McGuinness Associates
81. + Example process
81 81
Pellet OWL DL Reasoner
Valid OWL
(DL consistency checked, concept
satisfiability checked)
TW Instance Data
Evaluation
Valid OWL
(FOL consistency checked,
deductive closure)
Valid RDF/XML Documents
Ontology Registry (Valid OWL Syntax Representation)
& Library Application Environment
Ontologies should be checked to ensure consistency,
limit potential for invalid conclusions
Copyright © 2011 Thematix & McGuinness Associates
82. + Inconsistencies caused by distributed OWL data
82 82
Wine ontology
wine:Wine
rdfs:subClassOf rdfs:subClassOf
wine:EarlyHarvest wine:LateHarvest
owl:disjointWith
A new ontology A new instance data
rdfs:subClassOf rdfs:subClassOf rdf:type rdf:type
wine:BadWineClass wi:BadWineInstance
Case1: semantic inconsistency Case2: semantic inconsistency
caused by a new class. caused by a new instance.
Copyright © 2011 Thematix & McGuinness Associates
83. + TW OWL instance-data evaluation
83 83
Motivation
Objective metrics are needed to evaluate if semantic web instance
data is ready for practical use
Next generation Chimaera for more diverse and more instance-
oriented applications
Challenges
Criteria for “ready for use” vary across applications
Existing syntax and logical-consistency checking is not enough for
applications using some standard practical assumptions (e.g.
closed world, unique names, …)
Solution
Extensible & customizable service-oriented architecture
Integrated environment for evaluating issues beyond standard
syntactic correctness and logical consistency
See http://onto.rpi.edu/demo/oie/
Copyright © 2011 Thematix & McGuinness Associates
84. + Some issues in OWL instance data
84 84
owl:cardinality owl:cardinality "1"
"1"
owl:onProperty owl:onProperty
hasMaker hasColor
rdf:type rdf:type
owl:Restriction owl:Restriction
rdfs:subClassOf rdfs:subClassOf
Wine Wine hasColor Red
W
W rdf:type
MPV rdf:type EPV hasColor White
Missing property value (MPV)
Excessive property value (EPV)
Copyright © 2011 Thematix & McGuinness Associates
85. + “Pluggable” instance evaluation services
<?xml version="1.0"?>
<!DOCTYPE rdf:RDF [
<!ENTITY rdf "http://www.w3.org/1999/02/22-rdf-syntax-ns#" >
<!ENTITY wine "http://tw.rpi.edu/2008/04/wine-lite.owl#" > ]>
85 85
<rdf:RDF xmlns:wine = "&wine;" xmlns:rdf = "&rdf;">
<!-- A wine instance missing a value for hasMaker.
Therefore, we need to report a warning
indicating a missing property value.
-->
<wine:Zinfandel rdf:about="#W1">
<wine:hasColor rdf:resource="&wine;Red"/>
<wine:hasFlavor rdf:resource="&wine;Moderate"/>
<wine:hasSugar rdf:resource="&wine;Dry"/>
<wine:hasBody rdf:resource="&wine;Full"/>
</wine:Zinfandel>
<!--
A wine instance missing a value for hasColor;
however, the value for hasColor for all
instances of Zinfandel has been defined
in the wine ontology. Therefore, we don't
need to report a warning indicating a
missing property value.
-->
<wine:Zinfandel rdf:about="#W3">
<wine:hasFlavor rdf:resource="&wine;Moderate"/>
<wine:hasSugar rdf:resource="&wine;Dry"/>
<wine:hasBody rdf:resource="&wine;Full"/>
<wine:hasMaker>
<wine:Winery rdf:about="#Elyse" />
</wine:hasMaker>
</wine:Zinfandel>
</rdf:RDF>
Copyright © 2011 Thematix & McGuinness Associates
86. + Ontology-enhanced applications
Simple ‘ontologies’, taxonomy driven applications 86 86
Controlled, shared vocabularies (content management, faceted search)
Web site organization & navigation, expectation setting
Browsing & search engine optimization via mapping to tagged structures such as
the new schema.org from Yahoo!, Google & Bing, Good Relations ontology – see
http://purl.org/goodrelations/
“Smarter” search support (query expansion such as FindUR, e-Cyc; SPARQL for
linked data applications)
More expressive ontologies provide more options for
Configuration, e.g., AT&T PROSE/Questar
Data Integration, e.g., Virtual Observatories (VSTO, …)
Copyright © 2011 Thematix & McGuinness Associates
87. Semantic Sommelier
+
87 87
Semantically-enabled
advisors, utilizes
ontologies
reasoning
social
mobile
provenance
context
Copyright © 2011 Thematix & McGuinness Associates
88. + TW Wine Agent – semantic interoperability
Wine Agent receives a meal description and retrieves a 88 88
selection of matching wines available on the Web, using
an ensemble of standards and tools
RDF & OWL − for encoding wine/food listings and pairing
recommendations
Semantic MediaWiki − for publishing user-contributed
recommendations*
Twitter and Facebook − for posting recommendations
Pellet − for deriving knowledge from wine ontology and
recommendations
SPARQL − for querying wine/food listings with
recommendations
Inference Web − for explaining TW Wine Agent’s intelligent
behavior
Copyright © 2011 Thematix & McGuinness Associates
89. + Social Semantic Web data publishing
89 89
Collaborative wine recommendations
Using Semantic MediaWiki to
manage users and user-contributed
food/wine pairings
recommendations
Using semantic form to add OWL
Copyright © 2011 Thematix & McGuinness Associates instance data
90. + Semantic Query:
food hierarchy & recommendation
90 90
Copyright © 2011 Thematix & McGuinness Associates
91. + Wine Agent processing
91 91
Given a description of a meal
Combine wine ontology and the
OWL data published at Wiki
Use Pellet and SPARQL to state a
premise (the meal) and query
the knowledge base for a
suggestion for a wine
description or a set of wine
instances
Use Inference Web to explain
results (descriptions, instances,
provenance, reasoning engines,
etc.)
Copyright © 2011 Thematix & McGuinness Associates
92. + Wine Agent for iPhone
92 92
Client application which talks to a SW
service
Make requests for dishes and wines
using auto-generated interfaces
Make recommendations to the system
for others
Copyright © 2011 Thematix & McGuinness Associates
93. + Dynamic interfaces as function of ontology
93 93
owl:Functional
or
rdfs:range == owl:maxCardinality == 1
owl:Class
rdfs:range != owl:Class U
rdfs:Literal
rdfs:range ==
rdfs:Literal Otherwise
Copyright © 2011 Thematix & McGuinness Associates
94. + Descriptions
94 94
Resulting descriptions are
converted to classes (for creating
recommendations) or to instances
(for obtaining recommendations)
When going to a class, all “Is A”
statements become
rdfs:subClassOf and all other
properties become owl:Restrictions
Instances are realized by the
reasoner to identify appropriate
recommendations
Copyright © 2011 Thematix & McGuinness Associates
95. + Getting the Recommendation
95 95
Recommendations are made up of
two classes: 1 dish, 1 wine
When the instance is classified,
the agent returns the matching
recommendations
Tapping a particular
recommendation causes the wine
agent to look for items belonging
to the matching pair
Copyright © 2011 Thematix & McGuinness Associates
96. + Extensibility
96 96
Wine Agent Ontology is extensible
“Menu file” in RDF
Restaurants can introduce new
classes and instances to provide
better matches for users
Searches can be restricted to
imported menus
Copyright © 2011 Thematix & McGuinness Associates
97. + Future Work
97 97
Group recommendations using multiple iPhones/iPod
touches
Recommendations based on personal dietary needs and
preferences
‘iPellet’, an OWL Reasoner designed for iPhone, for
offline reasoning
Copyright © 2011 Thematix & McGuinness Associates
98. + Observations from the Wine Agent
98 98
Background knowledge is reasonably simple and built in OWL
(includes foods and wine and pairing information similar to the OWL
Guide, Ontology Engineering 101, CLASSIC Tutorial, …)
Background knowledge can be used for simple query expansion over
wine sources to retrieve for example documents about red wines
(including zinfandel, syrah, …)
Background knowledge used to interact with structured queries such
as those possible on wine.com
Constraints allows a reasoner like Pellet to infer consequences of the
premises and query.
Explanation system (Inference Web) can provide provenance
information such as information on the knowledge source
(McGuinness’ wine ontology) and data sources (such as wine.com)
Services work could allow automatic “matchmaking” instead of hand
coded linkages with web resources
Copyright © 2011 Thematix & McGuinness Associates
99. + Trust & Understanding
99 99
If users (humans and agents) are to use, reuse, and integrate system
answers, they must trust them.
System transparency supports understanding and trust.
Even simple “lookup” systems benefit from providing information
about their sources.
Systems that manipulate information (with sound deduction or
potentially unsound heuristics) benefit from providing
information about their manipulations.
Goal: Provide interoperable infrastructure that supports explanations of
sources, assumptions, and answers as an enabler for trust.
Copyright © 2011 Thematix & McGuinness Associates
100. + Explanations, Proof Analysis
100
100
Framework for explaining question answering tasks
Stores and manages meta-information about proofs and
explanations through a distributed repository
Uses the Proof Markup Language (PML) for proof interchange
(OWL-based)
Services include proof presentation, strategy-based views,
filtering, trust, combination, expansion, checking, search, and
other capabilities
Integrated browsing and display of PML documents from
diverse sources
Rewriting capabilities for improved understanding
Multi-modal dialogue options including alternative strategies
for presenting explanations, visualizations, and summaries
Copyright © 2011 Thematix & McGuinness Associates
101. + Inference Web (IW)
101
101
End
Users
End-User Data Access &
Validate published PML data
Interaction Data Analysis
services Services
Explanation via Graph
Distributed
Explanation via Summary PML data
Explanation via Annotation Access published PML data
Inference Web is a semantic web-based knowledge provenance management
infrastructure:
• PML for encoding and interchange provenance metadata in distributed environment
• Interactive explanation services for end-users
• Data access and analysis services for enriching the value of knowledge provenance
Copyright © 2011 Thematix & McGuinness Associates
102. + Proof Markup Language (PML)
A new kind of linked data on the Web 102
102
World Wide Web D
PML
PML data
Enterprise Web data
PML PML PML
data D PML
data data data
D Enterprise Web
D D D D PML
D data
…
Modularized & extensible
Provenance: annotate provenance properties
Justification: encodes provenance relations
Trust: add trust annotation
Semantic Web based
Copyright © 2011 Thematix & McGuinness Associates
103. + Technical Architecture
IW Registrar
call Web Service Edit edit 103
103
PML database
Interface Interface
machine agents generate domain experts
generate
edit
PML API
translate
Proofs PML Translator
(N3, KIF)
PML (provenance, justification, trust)
Computation Tools
(validation, conflict detection, abstraction)
is-stored-as
Data Access
Presentation Tools
(IW Browser) PML Java PML parse PML Web
Objects API Documents
reads uses harvests & indexes
searches harvests
IW Search subscribes
Copyright © 2011 Thematix & McGuinness Associates
104. Making Systems Actionable
using Knowledge Provenance
Mobile CALO
Wine Agent
104
104
Combining
Proofs in TPTP
Intelligence Analyst Tools
Knowledge
Provenance
in Virtual
GILA NOW including Data-gov Observatories
Copyright © 2011 Thematix & McGuinness Associates 104
105. + Example Proof Tree Browser
105
105
Copyright © 2011 Thematix & McGuinness Associates
106. + Explaining Extracted Entities
Source: fbi_01.txt (in NL) 106
106
Same conclusion Source Usage: span from 01 to
from multiple 78
extractors
Conflicting
conclusion from one
extractor (with
annotations)
This extractor decided
that Person_fbi-01.txt_46
is a Person and not
Occupation (in logical
format – e.g. KIF)
Copyright © 2011 Thematix & McGuinness Associates
107. + Trustworthiness of Ramazi report
From FBI
107
107
From intercepts
From CIA
Copyright © 2011 Thematix & McGuinness Associates