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Ontology 101: An Introduction to Knowledge Representation, the Web Ontology Language (OWL) & Ontology Development
Elisa Kendall, Thematix Partners LLC & Deborah McGuinness, RPI / McGuinness Associates
15 October 2012
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Mars was photographed by the Hubble Space Telescope in August 2003 as 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.
Content management
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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
Smart search
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+Merchandising
4Attribute 1Attribute 2Leg RoomAttribute 4Attribute 5Attribute 5On-Off AccessAttribute 7Attribute 8Attribute 9Media OptionsAttribute 11Attribute 12Meal OptionsAttribute 14Attribute 15Time of DayAttribute 17Attribute 18Distance to GateAttribute 20Attribute 21Flight CharacteristicsAttribute N…
“Comfort-Oriented”
Flight
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+Search Engine Optimization
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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.
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+Tutorial Outline
Introduction to Knowledge Representation
OWL Basics
Tools & Applications
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+Part 1: Introduction to Knowledge Representation
A little background and a few definitions
Layers of abstraction & conceptual modeling
Classifying ontologies
A little methodology
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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
Definitions
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+What is an ontology?
An ontology specifies a rich description of the
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.
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+Logic and ontological commitment
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.
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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
Ontology-based technologies
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+KR language features
Vocabulary – a collection of symbols 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
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+Classifying logics
Logics vary from classical FOL along six dimensions: 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
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+Description logic
A family of logic-based Knowledge Representation formalisms 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)
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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
Knowledge bases, databases, and ontology
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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.
Reasoning and truth maintenance
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If all new information is “positive”, then all prior conclusions should 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.
Negation
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+Explanations and proofs
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
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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
Contextu
al
• Identify subject areas
Conceptu
al
• Define the meaning of things in the organization
Logical
• Describe the logical representation of properties
Physical
• Describe the physical means by which data is stored
Definition
• Represent the coding language on a specific development platform
Instance
• Hold the values of the properties applied to the data in a schema
Ontology
Ontology, Conceptual ER, Conceptual Business Process …
ER, Relational, XML Schema
XML, source code, scripting languages, stored procedures…
Physical KBs, databases, asset repositories…
*Layering diagram courtesy Kenn Hussey
Abstraction layers
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+Hypothetical conceptual model “EU-Rent”
Produced using Embarcadero EA/Studio Business Modeler Edition, courtesy Kenn Hussey
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+Hypothetical logical model “EU-Rent”
1
P
Z
P P
P
Rental
non-local currency
booking mode
scheduled pick-up date-time
actual pick-up date-time
scheduled return date-time
actual return date-time
credit card
valid credit card
valid driver license
estimated rental charge
actual rental charge
rental state
Rental Car
VIN
acquisition date
odometer reading
service reading
rental car state
Rental Movement
Rental Organization Unit
rental organization unit id
ISO country code
site address
organization function
Service Depot
Advance Rental
booking date-time
payment type
advance rental state
Agency
Walk-in Rental
Agent
agent id
agent name
Airport Branch
Branch
branch name
hours of operation
car storage capacity
branch type
Car Group
group name
passenger capacity
Car Model
model id
manfacturer name
model name
engine capacity
car body style
Car Movement
movement id
movement type
movement direction
Car Transfer
transfer date
transfer pick-up date-time
transfer drop-off date-time
City Branch
Local Area
company name
branch type
organization function
booking mode
movement type
Driver
driver id
ISO country code
name
current contact details
driver license
driver type
loyalty club id
points balance
driver state
has
is of
includes
is included in
is of
has
is specified by
specifies
has
is of
has
is of
is included in
includes
requests
is requested by
stores
is stored at
operates
is operated by
owns
is owned by
is of
has
is upgrade for
is upgraded to
includes
is included in
includes
included in
is assigned to
has assigned
is responsible for
is responsibility of
Produced using Embarcadero ER/Studio, courtesy Kenn Hussey
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P P
ZZZ
Z ZZ
ZZ
ZZ
1
P
Z
P
Local_Area
local_area_id (PK)(FK)
company_name
Rental
rental_id (PK)(FK)
renter_id (FK)
pick_up_branch (FK)
drop_off_branch (FK)
non_local_currency
booking_mode
scheduled_pick_up_date_time
actual_pick_up_date_time
scheduled_return_date_time
actual_return_date_time
credit_card
valid_credit_card
valid_driver_license
estimated_rental_charge
actual_rental_charge
rental_state
Rental_Car
VIN (PK)
stored_at_branch (FK)
owner_local_area (FK)
model_id (FK)
acquisition_date
odometer_reading
service_reading
rental_car_state
Rental_Movement
rental_id (PK)(FK)
Rental_Organization_Unit
rental_organization_unit_id (PK)
ISO_country_code
site_address
organization_function
Service_Depot
service_depot_id (PK)(FK)
local_area (FK)
Advance_Rental
rental_id (PK)(FK)
requested_car_model (FK)
booking_date_time
payment_type
advance_rental_state
Walk_in_Rental
rental_id (PK)(FK) Agency
branch_id (PK)(FK)
agent (FK)
Agent
agent_id (PK)
agent_name
Airport_Branch
branch_id (PK)(FK)
Branch
branch_id (PK)(FK)
local_area (FK)
branch_name
hours_of_operation
car_storage_capacity
branch_type
Car_Group
group_name (PK)
upgrade_group (FK)
passenger_capacity
Car_Model
model_id (PK)
group_name (FK)
manfacturer_name
model_name
engine_capacity
car_body_style
Car_Movement
movement_id (PK)
rental_car (FK)
group_name (FK)
receiving_branch (FK)
sending_branch (FK)
movement_type
movement_direction
Car_Transfer
transfer_id (PK)(FK)
transfer_date
transfer_pick_up_date_time
transfer_drop_off_date_time
City_Branch
branch_id (PK)(FK)
Driver
driver_id (PK)
ISO_country_code
name
current_contact_details
driver_license
driver_type
loyalty_club_id
points_balance
driver_state
includes
is included in
includes
included in
is assigned to
has assigned
has
is of
includes
is included in
is of
has
is specified by
specifies
has
is of
has
is of
is included in
includes
requests
is requested by
stores
is stored at
operates
is operated by
owns
is owned by
is of
has
is upgrade for
is upgraded to
is responsible for
is responsibility of
Produced using Embarcadero ER/Studio, courtesy Kenn Hussey
Hypothetical physical model “EU-Rent”
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+Classifying ontologies
Level of Complexity
Level of
Expre
ssiv
ity
Simple Taxonomy
Glossary
Topic Map
Concept Map
Hierarchical Taxonomy
Entity – Relationship Model
Database Schema
OO Software Model
KR System
XML Schema
Classification techniques are as diverse
as conceptual models; and generally include understanding
Level of Expressivity Level of Complexity / Structure Granularity Target Usage, Relevance Amount of Automation, Reasoning
Requirements Prescriptive vs. Descriptive / Reliability /
Level of Authoritativeness Design Methodology Governance Vocabulary Management, Metrics
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+Framework of dimensions
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
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+Considerations
Intended use of ontologies, including domain requirements (e.g., 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
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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
A little methodology …
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+What to look for A controlled vocabulary
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 queriesFind 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
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+ General concepts as well as domain-specific knowledge 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, SKOS for annotations, 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) – RDFa,
extensions to schema.org Richer interoperability among trading partners – extensions to messaging
agreements Service registration, description, discovery & management Augmented decision support and business rule applications Asset/artifact repository search & retrieval Automated verification
Start with canonical definitions
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+
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
IDEF5 (Integrated Definition Methods) Ontology Capture Method Analysis
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+Principles & Methods for Terminology Work (ISO 704)
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
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+Key methodology issues
Naming conventions and versioning policies are critical for –every– organization Namespace definitions for ontologies are critical, along with
policies for their management that are well understood http://<authority>/<subdomain>/<topic>/<date, in
YYYYMMDD form>/filename.extension is common practice in some organizations, with content negotiation to the latest version
Levels of hierarchy may be added for large organizations or modularized ontologies
Use of “id.name.ext” or “ontology.name.ext” vs. “www.name.ext,” for the authority component of the URL is increasing
Namespace prefixes (abbreviations) for individual modules, especially where there are multiple modules, can be important due to longevity
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+More on naming
For naming entities in an ontology – there are some rules of thumb that vary by community of practice data modelers often use underscores at word boundaries, spaces
in names – which semantic web tools may not handle well (ok in labels)
some name properties <domain><predicate><range>, others <predicate><range>, & others <predicate>, but not necessarily consistently
semantic web practitioners typically use camel case; property naming varies by domain & community of practice
for very large ontologies, some use unique identifiers to name concepts and properties, with human readable names in labels only –depends on tooling that helps people interpret the content
The key is to establish & consistently use guidelines tailored to your organization
Guidelines for classes/concepts (e.g., OWL classes, CL names, SBVR concepts), properties (e.g., OWL properties, CL relations, functions, SBVR roles), individuals (objects in UML) – so that conceptual models developed in one tool can be exported consistently to other tools & formats essentialCopyright © 2012 Thematix Partners LLC & McGuinness Associates
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+Change management Change management and traceability is not well defined at the
element level for ontologies, still a research topic Current approach to element-level versioning to support reasoning about
change is to track two parallel streams: one for additions to the ontology, one for retractions; often managed as two separate files
UML/MOF versioning suggests linking to a workspace; use of a default workspace would get the latest version, and tools such as MagicDraw© & repositories such as Adaptive can provide an indication of the differences
UML-based versioning does not support reasoning about the differences so that users can determine the impact of applying the changes addition or deletion of axioms can change downstream reasoning results,
especially where there are complex dependencies
Mechanisms that preserve the versioning detail in artifacts that are generated from such models, such as RDF/XML serialized OWL, and are compatible with the above approaches, is an ongoing topic of discussion Consider the use cases/justification for whatever level of versioning detail is
needed on a project by project basis Balance with usability/performance
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+Modularity
Guidance on modularity is limited When is any model “too big”?
Can individual parts evolve independently, and if so, shouldn’t that dictate module boundaries?
Can individual parts be used independently, and if so, shouldn’t that also dictate module boundaries?
For ontologies, particularly OWL ontologies, individuals should be managed separately from “schema” to facilitate reasoning during development (i.e., if you add an individual that creates a logical inconsistency, most reasoners won’t load the ontology at all)
Current practices in the semantic web community address modularity either statically or dynamically Static approaches include adherence to “DL-Safe” rules and
suggestions in papers by Alan Rector (University of Manchester)
Dynamic approaches include use of tools that can assist in determining whether or not an ontology is appropriately modularized, using reasoning to perform rewriting with respect to soundness and completeness for a given ontology
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+Metadata for ontologies and elements
Metadata should be standardized at both the model level and the element level for every model (not just ontologies) Model level metadata can reuse properties from the Dublin Core
Metadata Terms, from the Simple Knowledge Organization System (SKOS), ISO 11179 Metadata Registry standard with ISO 1087 Terminology support, the Ontology Metadata Vocabulary (OMV), emerging W3C work on provenance and others
Most annotations should be optional at the element level, but a minimal set, including names, labels, and formal, text definitions, is important for reusability & collaboration
Consistent use of the same annotations (properties, tags) improves readability, facilitates automated documentation generation, and enables better search over ontology repositories
Model level metadata may reuse organization-specific taxonomies to enable better search
through RDFa tagging, for example
good relations & schema.org are both well known & in use in industry today
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+
A quick intro to RDF and RDF Schema
Basic constructs: descriptions & classes
Class expressions
Properties & restricting property values
Individuals & data ranges
Miscellaneous class axioms
New features of OWL 2
A few rules of thumb Depth vs. breadth, naming, synonymous terms Class vs. property value, class vs. individual Limiting scope
Part 2: OWL Basics
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+
"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
Semantic Web
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+Guiding Principles
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
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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
Resource Description Framework (RDF)
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+
http://semtech2012.semanticweb.com/travel.owl#Conference
http://semtech2012.semanticweb.com/travel.owl#HiltonSFUnionSquare
http://semtech2012.semanticweb.com/travel.owl#heldAtGraph:
XML/RDF:<rdf:Description rdf:ID=“Conference"/> <semtech:heldAt rdf:resource="#HiltonSFUnionSquare"/> </rdf:Description>
N3: semtech:Conference semtech:heldAt semtech:HiltonSFUnionSquare .
Subject
Predicate
Object
RDF Notation Options
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+
An RDF vocabulary that provides for identifying: classes,
subsumption (inheritance) relations for classes,
subsumption (inheritance) relations for properties,
domain and range for properties
RDF Schema (RDFS)
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+
Graph:
XML/RDF:<rdf:Description rdf:ID="Hotel"> <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“/>
RDF Schema (RDFS)
rdfs:Class
semtech:Hotel
semtech:ConferenceHotel
rdf:typerdfs:subClassOf
rdf:type
semtech:HiltonSFUnionSquare
rdf:type
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+The Web Ontology Language (OWL)
Two languages emerged in parallel to address semantic web requirements DAML-ONT, developed through 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 were adopted in October 2009
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+
a WINE
a LIQUIDa POTABLE
grape: chardonnay, ... [>= 1]sugar-content: dry, sweet, off-drycolor: red, white, roseprice: a PRICEwinery: a WINERY
grape dictates color (modulo skin)harvest time and sugar are related
General Categories
Structured Components
InterconnectionsBetween Parts
General nature of descriptions
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+
Number / Card Restrictions
ValueRestrictions
Class
Superclass
Roles /Properties
General nature of descriptions
a WINE
a LIQUIDa POTABLE
grape: chardonnay, ... [>= 1]sugar-content: dry, sweet, off-drycolor: red, white, roseprice: a PRICEwinery: a WINERY
grape dictates color (modulo skin)harvest time and sugar are related
General Categories
Structured Components
InterconnectionsBetween Parts
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+Describing classes
A class is a concept in the domain Vintage – a wine made from grapes grown in a specified year A set of characteristics (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 expression defines necessary conditions for membership (specific varietal, field, micro-climate, date picked, date bottled)
Instances of classes Daniel Gehrs 2009 Merlot Robert M. Parker, Jr,’s Wine Advocate review, dated February
28, 2002 Los Olivos, Santa Barbara County, California, USA
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+
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 is-a hierarchies are essential for classification Violation of true is-a hierarchical relationships can have unintended
consequences & and cause errors in reasoning
Class expressions should be viewed as sets, and subclasses as a subset of the superclass, in contrast with a collection of attributes as classes are sometimes specified in object oriented programming
Examples RedWine is a subclass of Wine
Every red wine is a wine; every instance of a red wine (e.g., Foxen Cabernet Franc) is an instance of wine
NapaValleyWine is a subclass of CaliforniaWine
Every wine from Napa Valley is a wine from California
Class inheritance
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+Class expressions
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6 primary kinds of class expressions in OWL 5 anonymous Definitions can be nested using set-theoretic constructs
Copyright © 2012 Thematix Partners LLC & McGuinness Associates
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+Example class hierarchy with other expressions
Example from ISO 1087 combining subclass-superclass and union expressions
Provides a more accurate representation of the relationships defined in the ISO standard than a strict is-a hierarchy would
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+Example class hierarchy with other expressions
Copyright © 2012 Thematix Partners LLC & McGuinness Associates
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+Modeling considerations for class hierarchy development
Practitioners tend to start 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 (sauvignon blanc is a subclass of white
wine, late harvest wine is a subclass of dessert wine) therefore A is a subclass of C (late harvest sauvignon blanc is a
subclass of white wine, dessert wine, & wine) Start with a more formal is-a approach, tease out whether other
expressions are more appropriate based on use cases, formal definitions when available, etc.
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+Properties
Properties describe characteristics, features, or attributes of the members of a classEvery flowering plant has a bloom color, color pattern, flower form,
petal form, etc.
Classes of properties intrinsic: properties of the plant itself such as the optimal sunlight,
soil conditions, expected height, and so forth extrinsic: properties imposed externally such as the grower and price whole-part relations geospatial, mereonomic relations: what microclimates are this plant
well suited to, where in a particular historic garden will you find it
Data and object properties simple attributes typically have primitive data values (e.g., strings,
numbers) complex properties refer to other entities (e.g., an individual grower,
such as Monrovia, or organization such as the American Orchid Society)
OWL reasoning requires strict separation of data and object properties
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+Properties in OWL 2
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+Domain & range properties
In OWL and many other KR languages, relations (properties) are strictly binary
The domain & range represent the source & target arguments, respectively, for the property
Domain – the class (or classes) that may have the property Wine is the domain of the property hasWineColor
Range – the class (or classes) defining valid property values everything that fulfills the hasWineColor property 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
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+Class & property inheritance
A subclass inherits all the properties from its super classIf 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 properties and restrictions from all of themLatin 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
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+Class expressions that restrict property values
Number restrictions describe or limit the number of possible values a particular property can have 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)
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+Simple number restriction examples
Creating a class of individuals that have exactly one color & a class of individuals that have more than one color
Note that the relationship between SingleColoredThing and the restriction class is modeled as an equivalence relationship, meaning that membership in the restriction class is a necessary and sufficient condition for being a SingleColoredThing
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<owl:ObjectProperty rdf:about="&re;hasColor"> <rdfs:range rdf:resource="&re;Color"/> <rdfs:domain rdf:resource="&re;ColoredThing"/> </owl:ObjectProperty> <owl:Class rdf:about="&re;Color"/> <owl:Class rdf:about="&re;ColoredThing"/> <owl:Class rdf:about="&re;MultiColoredThing"> <owl:equivalentClass> <owl:Restriction> <owl:onProperty rdf:resource="&re;hasColor"/> <owl:minCardinality rdf:datatype="&xsd;nonNegativeInteger">2</owl:minCardinality> </owl:Restriction> </owl:equivalentClass> </owl:Class> <owl:Class rdf:about="&re;SingleColoredThing"> <owl:equivalentClass> <owl:Restriction> <owl:onProperty rdf:resource="&re;hasColor"/> <owl:cardinality rdf:datatype="&xsd;nonNegativeInteger">1</owl:cardinality> </owl:Restriction> </owl:equivalentClass> </owl:Class>
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+Qualified cardinality number restriction example
OWL 2 provides the capability to further qualify cardinality by specific classes and data ranges
Patterns that reuse a smaller number of significant properties, building up more complex class expressions that limit the set of valid values are common, useful in complex classification systems
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+Qualified cardinality number restriction example
<owl:ObjectProperty rdf:about="&re;hasCharacteristic"> <rdfs:range rdf:resource="&re;Characteristic"/> </owl:ObjectProperty> <owl:Class rdf:about="&re;BloomColor"> <rdfs:subClassOf rdf:resource="&re;Characteristic"/> <rdfs:subClassOf rdf:resource="&re;Color"/> </owl:Class> <owl:Class rdf:about="&re;Characteristic"/> <owl:Class rdf:about="&re;Color"/> <owl:Class rdf:about="&re;FloweringPlantType"> <rdfs:subClassOf> <owl:Restriction> <owl:onProperty rdf:resource="&re;hasCharacteristic"/> <owl:onClass rdf:resource="&re;BloomColor"/> <owl:minQualifiedCardinality rdf:datatype="&xsd;nonNegativeInteger">1</owl:minQualifiedCardinality> </owl:Restriction> </rdfs:subClassOf> </owl:Class>
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+Class expressions that restrict possible values by type
Universal (allValuesFrom) and existential (someValuesFrom) quantification allValuesFrom is the OWL equivalent for (x), and restricts the possible
values for a property to members of a particular class or data range someValuesFrom is the OWL equivalent for (x), and restricts the
possible values for a property to at least one member of a particular class or data range
Specific value (hasValue) restrictions a single data value (e.g., the color property for a RedWine must be filled
with the value “red”) an individual member of a class (e.g., Winery is the value restriction on
the hasMaker property on the class Wine)
Enumerations – lists of allowable individuals or data elements
Combinations of the above that include both restrictions and boolean class expressions (union – logical or, intersection – logical and, complement – logical not)
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+Class expression restricting all values for a given property
Azaleas are members of the set of flowering plants whose bloom color must be either an RHSColor (Royal Horticultural Society) or ASAColor (Azalea Society of America)
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* courtesy Azalea Society of America
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+And the OWL for that …
<owl:Class rdf:about="&re;Azalea"> <rdfs:subClassOf rdf:resource="&re;FloweringPlantType"/> <rdfs:subClassOf> <owl:Restriction> <owl:onProperty rdf:resource="&re;hasBloomColor"/> <owl:allValuesFrom> <owl:Class> <owl:unionOf rdf:parseType="Collection"> <rdf:Description rdf:about="&re;ASAColor"/> <rdf:Description rdf:about="&re;RHSColor"/> </owl:unionOf> </owl:Class> </owl:allValuesFrom> </owl:Restriction> </rdfs:subClassOf> </owl:Class>
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* courtesy Azalea Society of America
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+Another typical pattern combining restrictions & boolean connectives
Use of equivalence expressions like this, describing RHSFloweringPlantType as a flowering plant and something whose bloom colors must be from the set of colors defined by the Royal Horticultural Society is a common pattern
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+And the corresponding OWL for that …
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<owl:Class rdf:about="&re;RHSFloweringPlantType"> <owl:equivalentClass> <owl:Class> <owl:intersectionOf rdf:parseType="Collection"> <rdf:Description rdf:about="&re;FloweringPlantType"/> <owl:Restriction> <owl:onProperty rdf:resource="&re;hasBloomColor"/> <owl:allValuesFrom rdf:resource="&re;RHSColor"/> </owl:Restriction> </owl:intersectionOf> </owl:Class> </owl:equivalentClass> </owl:Class>
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+Class expression restricting some & exact values for a given property
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+And the OWL for that …
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Copyright © 2012 Thematix Partners LLC & McGuinness Associates
<owl:Class rdf:about="&re;Azalea"> <rdfs:subClassOf rdf:resource="&re;FloweringPlantType"/> <rdfs:subClassOf> <owl:Restriction> <owl:onProperty rdf:resource="&re;hasBloomColor"/> <owl:allValuesFrom> <owl:Class> <owl:unionOf rdf:parseType="Collection"> <rdf:Description rdf:about="&re;ASAColor"/> <rdf:Description rdf:about="&re;RHSColor"/> </owl:unionOf> </owl:Class> </owl:allValuesFrom> </owl:Restriction> </rdfs:subClassOf> <rdfs:subClassOf> <owl:Restriction> <owl:onProperty rdf:resource="&re;hasBloomColorPattern"/> <owl:someValuesFrom rdf:resource="&re;ASAColorPattern"/> </owl:Restriction> </rdfs:subClassOf> </owl:Class>
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+... <owl:Class rdf:about="&re;SingleColoredAzalea"> <owl:equivalentClass> <owl:Class> <owl:intersectionOf rdf:parseType="Collection"> <owl:Restriction> <owl:onProperty rdf:resource="&re;hasBloomColorPattern"/> <owl:hasValue rdf:resource="&re;Solid"/> </owl:Restriction> <owl:Restriction> <owl:onProperty rdf:resource="&re;hasBloomColor"/> <owl:cardinality rdf:datatype="&xsd;nonNegativeInteger">1</owl:cardinality> </owl:Restriction> </owl:intersectionOf> </owl:Class> </owl:equivalentClass> <rdfs:subClassOf rdf:resource="&re;Azalea"/> </owl:Class> <owl:NamedIndividual rdf:about="&re;Solid"> <rdf:type rdf:resource="&re;ColorPattern"/> </owl:NamedIndividual>
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+Individuals and data ranges
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 of (or type of) the individual
Specify property values for the individual Property values should conform to the constraints such as range, value
type, cardinality restrictions, etc.
Enumerated classes & data ranges are commonly used to specify the complete set of valid values for a particular property
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+Example enumerated class of individuals
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+
Subsumption (necessary) A ⊆ B where B
is a class description
partial or primitive class
Definition (necessary and sufficient) C ≡ D where D
is a class description
complete or defined class
A
B
C
D
Class axioms
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Courtesy Evan Wallace, NIST
Copyright © 2012 Thematix Partners LLC & McGuinness Associates
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+Meta-properties – global cardinality restrictions
Functional
Inverse Functional
Domain
Domain
Range
Range
Courtesy Evan Wallace, NIST
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+Disjoint classes
A
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
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+New features in OWL 2
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
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+More new features
Extended datatype handling 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
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+Rules of Thumb
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
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+Class hierarchy analysis
Siblings should be specified at roughly the same level of generality -- compare to section and subsections in a book
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+More on class definition analysis
Classes that have single subclasses may be incompletely defined, unless additional children are specified in subordinate modules
Subclasses of a class typically have more detailed definitions Additional properties Constraints/restrictions on inherited properties Participate in further qualified relations Compare to bullets in a bulleted list
If a class has a large number of subclasses, it may be useful to define intermediate levels For wine, for example there are natural groupings around
wine color If no natural classification exists, the long list may be
appropriate
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+
A “wine” is not a subclass of “wines”
A particular vintage should be classified as an instance of the class Wines
Class names should be either all singular or all plural
Synonymous names for the same concept should be modeled as alternate designations, not as unique classes (in the same ontology)
Many systems & standards facilitate synonymous term representation (e.g., ISO 1087)
OWL allows defining necessary and sufficiency condition definitions thereby allowing synonym definitions to be “first class” terms (as appropriate, through equivalence / same as relations)
MariettaOldVinesRed
Class
Instance
instance-of
Inheritance, naming, synonyms
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+
Are concepts with distinct property values used in restrictions on other properties?
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
Class vs. property value
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+Class vs. individual
Individuals are the most specific entities in an ontology
If concepts form a natural hierarchy, represent them as classes
If they might have individuals, represent them as classes
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+Limiting scope
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, …)
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+
Ontology-powered applications (both light weight and richer applications) Wine Agent Semantically-enabled Environmental Monitoring Population Science Health Data Exploration Scientific Observations Virtual Observatories Cognitive Assistant, Learning, and Explanation
Technical Underpinnings Understanding, Trust, & Proof: InferenceWeb Semantic Methodology Checking: Syntax, Consistency, Evaluation Services Linked Data
Questions
Part 3: Putting It All Together
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+Ontology-enhanced applications
Simple ‘ontologies’, taxonomy driven applications Controlled, shared vocabularies (content management, faceted search) –
e.g., PopSciGrid 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 – Wine Agent, Environmental portal
More expressive ontologies provide more options for Configuration, e.g., AT&T PROSE/Questar Data Integration, e.g., Virtual Observatories (VSTO, …)
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Semantic Sommelier…ontology-driven, context-aware, mobile app
Semantically-enabled advisors utilize: Ontologies Reasoning Social Mobile Provenance Context
Patton & McGuinness.et. al
tw.rpi.edu/web/project/Wineagent
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+TW Wine Agent – semantic interoperability
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
InferenceWeb for explaining Wine Agent’s recommendations
Wine Agent receives a meal description and retrieves a selection of matching wines available on the Web, using an ensemble of standards and tools
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+Social Semantic Web data publishing
Collaborative wine recommendations
Using Semantic MediaWiki to manage users and user-contributed food/wine pairings recommendations
Using semantic form to add OWL instance data
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+Semantic Query:food hierarchy & recommendation
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+ Semantic Sommelier
Previous versions used ontologies to infer descriptions of wines for meals and query for winesNew version uses
Context: GPS location, local restaurants and wine lists, user preferencesSocial input: Twitter, Facebook, Wiki, mobile, …
Source variability in quality, contradictions exist, Maintenance is an issue… however new models emerging
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+Extensibility
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 and/or imported wine lists
In process:
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
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+Observations from the Wine Agent
Background knowledge is reasonably simple and built in OWL (includes foods and wine and pairing information similar to the original Living with CLASSIC paper, CLASSIC tutorial, OWL Guide, Ontology Engineering 101, …)
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 sites like 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 reviews or particular restaurants or wine stores)
Services work could allow automatic “matchmaking” instead of hand coded linkages with web resources
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+ SemantEco/SemantAqua Enable/Empower citizens
& scientists to explore pollution sites, facilities, regulations, and health impacts along with provenance.
Demonstrates semantic monitoring possibilities.
Map presentation of analysis
Explanations and Provenance available
1
2 3
http://was.tw.rpi.edu/swqp/map.html and http://aquarius.tw.rpi.edu/projects/semantaqua
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1. Map view of analyzed results2. Explanation of pollution 3. Possible health effect of contaminant (from EPA)4. Filtering by facet to select type of data5. Link for reporting problems
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+System Architecture
access
Virtuoso
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+Ontology
Extends existing best practice ontologies, e.g. SWEET, OWL-Time.
Includes terms for relevant pollution concepts
Can be used to conclude: “any water source that has a measurement outside of its allowable range” is a polluted water source.
Portion of the SemantEco and SemantAqua ontologies.
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+Ontology
Regulation Ontology models the federal and
state water quality regulations for drinking water sources
We can recognize pollution, e.g. “any water source that contains 0.01 mg/L of Arsenic or more is a polluted water source.”
Portion of Cal. Regulation Ontology.
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+Querying
With OWL reasoning during query:
SELECT DISTINCT ?site ?lat ?lng
WHERE { ?site a pol:PollutedSite ;
geo:lat ?lat ; geo:long ?lng . }
OWL reasoning hides the complexity of the relationships allowing the developer (or user) to ask simple questions without requiring deep knowledge of environmental regulations
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+Reflections
Uses semantic technologies along with simple ontologies and provenance-encoding tools
Was generated by students in a regular term class, then extended to be the basis of a masters degree (Wang), and the foundation of 3 other class projects
Small background extensible ontology
Now being expanded to include endangered species, migration, health effects, etc.
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+ A Community-Driven Scientific Observations Network to
achieve Semantic Interoperability of Environmental and Ecological Data
Build a community-sanctioned, evolving data model for observational data
Build a network of practitioners Build repository of motivating use cases All to enable interoperability among existing data
resources, supporting cross-disciplinary synthetic research in the environmental sciences
https://sonet.ecoinformatics.org
Schildhauer1, Bowers2, Gries3, McGuinness4, Dibner5, Madin6, Jones1, Bermudez7
1NCEAS UC Santa Barbara, 2Gonzaga University 3NTL/LTER and Univ. of Wisconsin, 4McGuinness & Associates, 5OGC Interoperability Institute, 6Macquarie University, 7Southeastern Universities Research Association
SONet: Scientific Observations Network
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+Population Sciences Grid Goals
Convey complex health-related information to consumer and public health decision makers for community health impact
Leverage the growing evidence base for communicating health information on the Internet
Inform the development of future research opportunities effectively utilizing cyberinfrastructure for cancer prevention and control
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+Computer Science Perspective on PopSciGrid Goals
How can semantic technologies be used to integrate, present, and analyze data for a wide range of users?
Can tools allow lay people to build their own demos and support public usage and accurate interpretation?
How do we facilitate collaboration and “viral” applications?
Within PopSciGrid: Which policies (taxation, smoking bans, etc) impact health and health
care costs? What data should be displayed to help scientists and lay people evaluate
related questions? What data might be presented so that people choose to make (positive)
behavior changes? What does the data show? why should someone believe that? What are appropriate follow ups?
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+PopSciGrid Workflow
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CSV2RDF4LODDirect
SemDiff
Archive
CSV2RDF4LODEnhance
Visualize
derive derive
integrate
derive
arch
ive
Publish
Ban coverage
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+PopSciGrid: Conn
Extensible Mashups via Linked Data Diverse datasets from NIH Potentially linking to other content (e.g. “unemployment rate”)Accountable Mashups via Provenance Annotate datasets used in demos Feedback users’ comment to gov contact (e.g. %) Annotation capabilities in process
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http://logd.tw.rpi.edu/demo/trends_in_smoking_prevalence_tobacco_policy_coverage_and_tobacco_prices
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+PopSciGrid II
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+ PopSciGrid II
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+Reflections
Successful but….
What if we could allow data experts to build their own demos?
What if we could allow non-subject matter experts to function as subject-literate staff?
What if team members could interchange roles (and thus make contributions in other areas)?
What technological infrastructure is required?
Claim: all of this is being done now – but not at scale
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+ Updates and Motivations from a Computer Science Perspective
Old:
Raw conversions
Per-dataset vocabularies
Custom queries
Custom data management code
Limited use because of Google Visualization licenses
State-level data
New:
Enhanced conversions
Vocabulary reuse
Generic queries
Re-usable data management code
Unlimited use of new open source visualization toolkit
State and county-level data
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+RDF Data Cube Vocabulary
For publishing multi-dimensional data, such as statistics, on the web in such a way that it can be linked to related data sets and concepts using RDF.
Compatible with the cube model that underlies SDMX (Statistical Data and Metadata eXchange).
Also compatible with: SKOS, SCOVO, VoiD, FOAF,
Dublin Core Terms
Integrated with the LOGD data conversion infrastructure
Integrated with other tooling like Stats2RDF
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County average life expectancy(Summary Measures of
Health)
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+Reflections
Uses semantic technologies along with simple ontologies and provenance-encoding tools
Provides an operational specification for other policy exploration tools – initially tobacco data and related policies, now also physical education and nutrition policies in elementary, junior and senior high school (CLASS - http://class.cancer.gov/About.aspx ).
Similar model being used in the Tetherless SemantEco / SemantAqua / SemantAir Portal family
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Why did I present wine, water, and linked data applications?
Wine advisor shows semantic technology in action making actionable recommendations
Water application shows a “typical” semantic integration web 3.0 application
Both of these styles are needed for many semantic applications and these features are realizable today
Next – they depend on Understandable and Actionable applications Semantic web stack Data availability – linked data cloud is growing Ontologies Semantic methodologies
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+
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.
Foundations: Trust & Understanding
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+Foundations: Explanations, Proof Analysis
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
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+
Explanation via Graph
Explanation via Summary
Explanation via Annotation
Foundations: Inference Web (IW)
End Users
End-User Interaction
services
Distributed
PML data
Data Access & Data Analysis
Services
Validate published PML data
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
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+Foundations: Proof Markup Language (PML)
A new kind of linked data on the Web
Modularized & extensible Provenance: annotate provenance properties Justification: encodes provenance relations Trust: add trust annotation
Semantic Web based
Enterprise Web
Enterprise Web
World Wide Web
D D
PMLdata
PMLdata
DD
D
PMLdata
PMLdata
…
PMLdata
D
D PMLdata
PMLdata
D
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+Technical Architecture
Data Access
edit
PML (provenance, justification, trust)
IW Registrar
PML databasecall
generate
Proofs (N3, KIF)
PML Translator
editWeb ServiceInterface
generate
Edit Interface
PML APItranslate
PML Web Documents
is-stored-as
parse
IW Search
harvests & indexesharvests
subscribes
reads
Computation Tools(validation, conflict detection,
abstraction)
PML API
Presentation Tools(IW Browser)
uses
PML Java Objects
searches
domain expertsmachine agents
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Making Systems Actionable with Knowledge Provenance
Mobile Wine Agent
GILA
Combining Proofs in TPTP
CALO
Knowledge Provenance
in Virtual Observatorie
s
Intelligence Analyst Tools
NOW including Data-govCopyright © 2012 Thematix Partners LLC & McGuinness Associates
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+Example Proof Tree Browser
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+
Source: fbi_01.txt (in NL)Source Usage: span from 01
to 78
This extractor decided that Person_fbi-
01.txt_46 is a Person and not Occupation (in
logical format – e.g. KIF)
Same conclusion from multiple
extractors
Conflicting conclusion from
one extractor (with annotations)
Explaining Extracted Entities
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+
From CIA
From FBI
From intercepts
Trustworthiness of Ramazi report
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SPARQL to Xquery translator RDFS materialization(Billion triple winner)
Govt metadata searchLinked Open Govt Data
SPARQL WG, earlier QL –OWL-QL, Classic’ QL, …
OWL 1 & 2 WG Edited main OWL Docs, quick reference, OWL profiles (OWL RL),
Earlier languages: DAML, DAML+OIL, Classic
RIF WGAIR accountability tool
DL, KIF, CL, N3Logic
Inference Web, Proof Markup Language, W3C Provenance Working group formal model, W3C incubator group, …
Inference Web IW Trust, Air + Trust
Visualization APIsS2S
Govt Data
Ontology repositories (ontolinguag),Ontology Evolution env:Chimaera, Semantic eScience Ontologies, MANY other ontologies
Transparent AccountableDatamining Initiative (TAMI)
Foundations: Web Layer Cake
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Foundations: Linked Data Cloud
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+Foundations: The Tetherless World Constellation Linked Open Government Data Portal
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Create
TWC LOGD
ConvertQuery/Access
LOGDSPARQL Endpoint
Enhance
• RDF• RSS• JSON• XML• HTML• CSV• …
Community Portal
Data.gov deployment
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Semantic Web Methodology
Originally developed for VSTO, now in SSIII, SESDI, SESF, OOI …McGuinness, Fox, West, Garcia, Cinquini, Benedict, Middleton The Virtual Solar-Terrestrial Observatory: A Deployed Semantic Web Application Case Study for Scientific Research. Proc. 19
Conf. on Innovative Applications of Artificial Intelligence (IAAI-07),
http://www.vsto.org
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+
A distributed, scalable education and research environment for searching, integrating, and analyzing observational, experimental, and model databases – aimed at PHD researchers
Subject matter covers the fields of solar, solar-terrestrial and space physics
Provides virtual access to specific data, model, tool and material archives containing items from a variety of space- and ground-based instruments and experiments, as well as individual and community modeling and software efforts bridging research and educational use
3 year NSF-funded project; initial deployment in year 1, multiple deployments by year 2; year 3 outreach and broadening
While aimed at one interdisciplinary area, it also serves as a replicable prototype for interdisciplinary virtual observatories
NSF follow-on for provenance extension (Semantic Provenance Capture in Data Ingest Systems) and generalization – Semantic eScience Framework
Virtual Solar Terrestrial Observatory – Setting for initial semantic methodology design
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+
November 9, 2006
Virtual Observatory (VSTO)
General: Find data subject to certain constraints and plot appropriately
Specific: Plot the observed/measured Neutral Temperature as recorded by the Millstone Hill Fabry-Perot interferometer while looking in the vertical direction at any time of high geomagnetic activity in a way that makes sense for the data.
from vsto.org
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Partial exposure of Instrument class hierarchy
Semantic filtering by domain or instrument hierarchy
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+Provenance aware faceted search
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Additional work on provenance is being incorporated
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+Cognitive Assistant that Learns & Organizes
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DARPA IPTO funded program
Personal office assistant, tasked with: Noticing things in the cyber and physical environments Aggregating what it notices, thinks, and does Executing, adding/deleting, suspending/resuming tasks Planning to achieve abstract objectives Anticipating things it may be called upon to do or respond to Interacting with the user Adapting its behavior in response to past experience, user
guidance
22 participating organizations
Led to other efforts including Siri (later acquired by Apple and in new iPhone
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+ Working with a Cognitive Assistant
CALO users need to Understand system behavior and responses Trust system reasoning and actions
To believe and act on recommendations from CALO, users need ways of exploring how and why the system acted, responded, recommended, and reasoned the way it did.
Additional wrinkle: CALO knowledge, behavior, and assumptions are constantly changing through several forms of machine learning.
A unified framework for explaining behavior and reasoning is essential for users to trust and adopt cognitive assistants.
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+ ICEE Architecture
Collaboration Agent
Justification Generator
Task Manager (TM)
TM Wrapper
Explanation Dispatcher
Task State Database
TM Explainer
KM Explainer
Knowledge Manager (KM)
Constraint Explainer
Constraint Reasoner
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+The Use-Ask-Understand-Update Cycle
Use Ask
Understand / AcceptUpdate
CALO explanation joint withGlass, Wolverton, Chang, Ding
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+Foundations: Consistency checking
Requirements are typically application specific
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/
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+
Valid RDF/XML Documents(Valid OWL Syntax Representation)
Valid OWL (FOL consistency checked,
deductive closure)
Pellet OWL DL Reasoner
Valid OWL (DL consistency checked, concept
satisfiability checked)
Ontology Registry & Library
Example process
Ontologies should be checked to ensure consistency, limit potential for invalid conclusions
Application Environment
TW Instance Data Evaluation
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+
A new ontology A new instance data
Wine ontology
wine:EarlyHarvest wine:LateHarvest
wine:Wine
rdfs:subClassOf rdfs:subClassOf
owl:disjointWith
wine:BadWineClass
rdfs:subClassOf rdfs:subClassOf
wi:BadWineInstance
rdf:type rdf:type
Case1: semantic inconsistencycaused by a new class.
Case2: semantic inconsistencycaused by a new instance.
Inconsistencies caused by distributed OWL data
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+TW OWL instance-data evaluation
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/
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+Some issues in OWL instance data
Missing property value (MPV)
Excessive property value (EPV)
EPVMPVW
Wine
rdf:type
rdfs:subClassOf
W
Wine
rdf:type
Red
White
hasColor
hasColor
hasColor
owl:Restriction
"1"
owl:cardinality
owl:onProperty
rdf:typehasMaker
owl:Restriction
"1"
owl:cardinality
owl:onProperty
rdf:type
rdfs:subClassOf
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Foundations: Linked Data Cloud
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+Foundations: The Tetherless World Constellation Linked Open Government Data Portal
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Create
TWC LOGD
ConvertQuery/Access
LOGDSPARQL Endpoint
Enhance
• RDF• RSS• JSON• XML• HTML• CSV• …
Community Portal
Data.gov deployment
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+
Introduced Semantic Technology Foundations Provided numerous examples of functioning systems
with value propositions, many users, etc. Foundational technology is available from many sources
Questions
Summary
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Questions?143
Elisa KendallPartner, Thematix Partners LLC(650) 960-2456ekendall @ thematix.comhttp://thematix.com
Deborah McGuinnessTetherless World Constellation Chair, Professor of Computer Science and Cognitive ScienceRensselaer Polytechnic Institute andCEO, McGuinness Associates(518) 276-4404dlm @ cs.rpi.eduhttp://tw.rpi.edu
Copyright © 2012 Thematix Partners LLC & McGuinness Associates
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+Acronym Soup
CL – ISO 24707 Common Logic: a family of first order logic languages, including Conceptual Graphs & Common Logic Interchange Format – a successor to the Knowledge Interchange Format (KIF), http://cl.tamu.edu/
DAML – DARPA Agent Mark-up Language, one of the primary languages leading to the development of OWL, http://www.daml.org/
DARPA – Defense Advanced Research Projects Agency, http://www.darpa.mil/
DL – Description Logics: a subset of first order logic, for which tractable & complete reasoning systems are available
Linked Data and RDFa, http://www.w3.org/standards/techs/rdfa#w3c_all
MDA – Model-Driven Architecture, http://www.omg.org/mda/
ODM – Ontology Definition Metamodel, http://www.omg.org/spec/ODM/1.0/
OWL – W3C Web Ontology Language, OWL 2 specifications dated 27 October 2009, http://www.w3.org/standards/techs/owl#w3c_all
OWL-S – a set of OWL ontology components that extend the W3C OWL specifications to support Semantic Web Services, http://www.daml.org/services/owl-s/1.1/
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+More Acronym Soup
PML – Proof Markup Language – Provenance Interlingua, www.inference-web.org
PRR – Production Rules Representation, http://www.omg.org/spec/PRR/1.0/
RIF – Rule Interchange Format, http://www.w3.org/standards/techs/rif#w3c_all
RDF – Resource Description Framework, http://www.w3.org/TR/rdf-concepts/
Semantic Annotations for WSDL and XML (SAWSDL), http://www.w3.org/standards/techs/sawsdl#w3c_all
Simple Knowledge Organization System (SKOS), http://www.w3.org/standards/techs/skos#w3c_all
SPARQL – Semantic Web Query Language, http://www.w3.org/standards/techs/sparql#w3c_all
Semantic Web Rule Language (SWRL), http://www.w3.org/Submission/SWRL/
WSDL – Web Services Description Language
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+Extra
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SPARQL to Xquery translator RDFS materialization(Billion triple winner)
Govt metadata searchLinked Open Govt Data
SPARQL WG, earlier QL –OWL-QL, Classic’ QL, …
OWL 1 & 2 WG Edited main OWL Docs, quick reference, OWL profiles (OWL RL),
Earlier languages: DAML, DAML+OIL, Classic
RIF WGAIR accountability tool
DL, KIF, CL, N3Logic
Inference Web, Proof Markup Language, W3C Provenance Working group formal model, W3C incubator group, …
Inference Web IW Trust, Air + Trust
Visualization APIsS2S
Govt Data
Ontology repositories (ontolinguag),Ontology Evolution env:Chimaera, Semantic eScience Ontologies, MANY other ontologies
Transparent AccountableDatamining Initiative (TAMI)
Foundations: Web Layer Cake
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Semantic Web Methodology
Originally developed for VSTO, now in SSIII, SESDI, SESF, OOI …McGuinness, Fox, West, Garcia, Cinquini, Benedict, Middleton The Virtual Solar-Terrestrial Observatory: A Deployed Semantic Web Application Case Study for Scientific Research. Proc. 19
Conf. on Innovative Applications of Artificial Intelligence (IAAI-07),
http://www.vsto.org
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+ Inference Web: Making Data Transparent and Actionable Using Semantic Technologies How and when does it make sense to use smart system
results & how do we interact with them?
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Knowledge Provenance in
Virtual Observatories
Hypothesis Investigation
/ Policy Advisors
(Mobile) Intellige
nt Agents
Intelligence Analyst Tools
NSF Interops:SONETSSIII – Sea Ice
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+The Semantic Web enables…
New models of intelligent services
E-commerce solutions
M-commerce
Web assistants
…
• Semantic Technologies: ready for use• Tools & tutorials available; deep apps
future planning may benefit from consultants• Context-aware, semantic apps are the future
New forms of web assistants/agents that act on a human’s behalf requiring less from humansand their communication devices…
More info: dlm @ cs.rpi.edu
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+Semantic Water Quality Portal / SemantAqua
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+SemantAqua Workflow
Archive
CSV2RDF4LODEnhance
derive derive
integrate arch
ive
Publish
CSV2RDF4LODDirect visualize
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