Knowledge Component 2: Building Knowledge Representation
Knowledge Component 4: Information Storage
2.5/4.2 Ontology:
An Introduction
Raymond Issa, University of Florida, USA
Tomasz Arciszewski, George Mason University, USA
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Module Information
•
Intended audience
– Beginners
•
Key words
– Ontology
– Knowledge representation
– Ontology building and development tools
•
Authors:
– Raymond Issa, University of Florida, USA
– Tomasz Arciszewski, George Mason University, USA
•
Reviewers:
– Chimay Anumba, Loughborough University, UK.
– Bill O’Brien, TCCIT IC Committee Chair, University of Texas,
Austin, USA
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•
•
•
Review Board:
–
Renate Fruchter, ExCom Past Chair, Stanford University, USA
–
Carlos Caldas, TCCIT DIM Committee Chair, University of Texas Austin, USA
–
Bill O’Brian, TCCIT IC Committee Chair, University of Texas Austin, USA
–
Guillermo Salazar, TCCIT Edu Committee Chair, Worcester Polytechnic Institute, USA
–
William Rasdorf, TCCIT JCCE editor, North Carolina State University, USA
–
Chimay Anumba, Loughborough University, UK.
The ASCE GCEC Officers:
–
Tomasz Arciszewski, ExCom Chair, George Mason University, USA
–
Ian Smith, ExCom Vice-Chair, EPFL, Switzerland
–
Hani Melhem, ExCom Vice-Chair, Kansas State University, USA
The ASCE Technical Council on Computing and IT
Officers:
–
Renate Fruchter, ExCom Past Chair, Stanford University, USA
–
Kim Roddis, ExCom Chair, George Washington University, USA
–
Hani Melhem, ExCom Vice Chair, University of Florida Gainesville,
USA
–
Raymond Issa, ExCom Secretary, Kansas State University, USA
–
Ian Flood, ExCom Member at Large, University of Florida Gainesville,
USA
–
Ian Smith, ExCom Member, EU Liaison, EPFL, Switzerland
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Organization
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•
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What is an ontology?
Why do we need ontologies?
How to build an ontology?
Examples
References and reading
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What is an ontology?
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Concept
• A concept is an abstract idea, or a
symbolic description of a category of
entities, interactions, phenomena, or
relationships between them.
• It can be presented in various forms:
– Natural : Language -> English
– Formal: Mathematical A={a1,b1}
– Visual: Drawings
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Concept: a Natural Form
• A concept is a short piece of text using known
words (concepts) to describe a new concept
• Example: a concept of a truss
– A structure which:
•
•
•
•
•
Has at least three members
Has prismatic members
All members are pinned-connected
All members are two-force members
All external loading, including reactions, is applied at joints
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Concept: A Mathematical Form
A concept is a set of n symbolic attributes
(descriptors) with their values uniquely
identifying a category of entities,
interactions, phenomena, or relationships
between them
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Symbolic Attribute
• A symbolic attribute (descriptor) describes
qualitative, non-numerical features of an entity
• Example (truss), symbolic attributes and their
feasible values:
–
–
–
–
Member type: prismatic, non-prismatic
Connection type: pinned, rigid
Member loading type: two-force, multi-force
Loading application: joints, everywhere
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Numeric Attribute
• A numeric attribute (descriptor) describes
quantitative, numerical features of an entity
• Example (truss), numeric attributes and their
feasible values:
– Length: 5 feet, 6 feet, 15 feet
– Depth: 16 inches, 18 inches, 20 inches
– Weight: 20 pounds, 25 pounds, 30 pounds
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Concept of a Truss: Mathematical Form
•
•
•
•
A1, Member type = prismatic
A2, Connection type = pinned
A3, Member loading type = two-force
A4, Loading application = joints
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Concept of a Truss: Visual Form
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Ontology: a philosophical perspective
• From Greek: being and writing about
• Trying to find out what entities and what
types of entities exist
• The most fundamental branch of
metaphysics: the study of all kinds of
things that exist
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Ontology: General Definitions
• A conceptualization of a domain
• The best structure of concepts from a
given domain for effective computation
• A combination of definitions and their
relationships
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Gruber’s Definition
• Tom Gruber, a leading ontology scholar, is
a Canadian computer scientist working in
the USA at NIST
• His definition:
– An ontology is an explicit specification of
a conceptualization
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Ontology: A knowledge representation perspective
Ontology is a knowledge representation in
which the terminologies have been
structured to capture the concepts being
represented precisely enough to be
processed and interpreted by people and
machines without any ambiguity
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Why do we need
ontologies?
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Four Categorizes of Reasons
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•
•
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Professional
Academic
Computational
Educational
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Professional Reasons
• Modern engineering means cooperation
within and across disciplines (a structural
engineer working with a mechanical
engineer)
• Infrastructure security forces cooperation of
lawyers, security personnel, firemen,
engineers, etc., all speaking various
professional languages
• Each profession has its own professional
vocabulary
• Misunderstanding creates friction, delays,
affects productivity, and in critical situations is
simply dangerous
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Professional Reasons (continued)
Professional Tower of Babel
costs time, money, and may
be dangerous
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Academic Reasons
• Knowledge is a system of concepts,
relationships, and processes
• No knowledge system can be acquired,
built, maintained, or used without
understanding its concepts
• The key to knowledge is concept
understanding
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Academic Reasons
“Knowledge is power”
Ontologies are vital for
understanding/knowledge
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Computational Reasons
• Modern engineering is knowledge- and
computation-based
• Only knowledge- and computation-enabled
methods, processes, and tools can be used over
local networks or over the Internet
• Knowledge and computation-based tools must
be ontology-based for consistency and
integration reasons
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Computational Reasons (continued)
“Ontologies are the key to
building computational and
knowledge-based tools”
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Present Use of Ontologies
• For communication:
– Between implemented computational systems
– Between humans
– Between humans and implemented computational systems
• For computational inference:
– For internally representing and manipulating plans and planning
information
– For analyzing the internal structures, algorithms, inputs and
outputs of implemented systems in theoretical and conceptual
terms
• For reuse (and organization) of knowledge
– For structuring or organizing libraries or repositories of plans and
planning and domain information
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How to build an ontology?
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Purpose and Scope
• Identify the purpose and clarify why the
ontology is being built
• Identify the scope and determine what its
intended uses are (i.e. to be reused, shared,
used as part of Knowledge Base, etc.)
• Example: building an ontology for drywalls
– Purpose:
• a consensual ontology for a construction company and drywall
providers
- The development of a catalog of the available types of
drywalls
- Scope:
- list of relevant materials,
- list of different types of the walls according to the designated
uses, etc.
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Building the Ontology
• Ontology capture (capturing knowledge) 
Conceptualization
Conceptualization
Domain
Ontological Categories
Axioms
Semantic Relations
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Intended model, ontology,
conceptualizations and domain world
Domain World
Model of Languages & Representations
Ontology
Intended Model
Conceptualization
Ontology: Specific artifact designed with the purpose of expressing
the intended meaning of a vocabulary in terms of the nature and
structure of the entities it refers to. (Guarino 1997)
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Building an Ontology
• Identify key concepts and relationships in the domain of interest
• Produce precise and unambiguous textual definitions for concepts
and relationships
• Identify the terms that refer to concepts and their relationships
• Example:
- Drywall -> (Concept) -> Building material consisting of gypsum formed
into a flat sheet and sandwiched between two pieces of heavy paper
- Drywall -> (Associated terms) –> Wallboard, gypsum board, GWB, and
plasterboard
- Greenboard -> (Concept) -> kind of Drywall -> Water resistant wallboard
that has asphalt added which gives it a brown gypsum core
- Concrete backerboard -> (Concept) -> kind of Drywall -> Concrete
reinforced with fiberglass that is typically used as the underlayment for
ceramic tile
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Language and Knowledge Representation
Remember that natural language
definitions determine the knowledge
representation of an ontology to be
developed
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Concept Identification
Strategies
• Bottom-up: From the most specific concepts to
more abstract concepts.
• Top-down: Define most abstract concepts first
and then define them into more specific
concepts
• Middle-out: First the core of the basic terms,
and then specifying and generalizing them as
required
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Coding
Committing to the representation of the ontology
(e.g. the terms that define the representation of
the ontology such as class, relationship, entities
etc.) and then writing the code
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Languages
• As a form of knowledge representation, the selection of
the language should depend on what is needed in terms
of expressiveness capabilities and reasoning
• Expressiveness: How concepts are built in terms of
attributes, relations, axioms, among other components
• Reasoning: Refer to the main features of the inference
engine attached to each language (e.g. simple or
multiple inheritance, exception)
• Example of traditional ontology languages:
-
Ontolingua (Based on KIF - Knowledge Interchange Format)
OKBC (Open Knowledge Base Connectivity)
RDF (Resource Description Framework)
OIL (Ontology Interchange Language / Ontology Inference Language)
OWL (W3C Web Ontology) It is intended to share and publish ontologies on
the web
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Ontology Tools
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•
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Development tools. Integrated suites that can
be used to build a new ontology from scratch.
Evaluation tools. Used to evaluate the content
of ontologies ant their related technologies.
Merging and alignments tools. Used to solve
the problem of merging and aligning different
ontologies in the same domain.
Querying tools and inference engines. Allow
querying ontologies easily and performing
inferences with them.
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Conceptualizations and Ontology
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Examples
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Ad-hoc Ontology
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Ad-hoc Ontology
• Ontologies acquire knowledge about the world and frame that
knowledge into categories and add terminology and constrains
them with axioms from traditional logic.
• The nodes in Figure represent the Concepts.
• Figure shows some specializations of the root, represented by
Concept 1
• {Functional Areas, Administration and Buildings} are represented by
Concepts 2, 3, and 4 respectively.
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Ad-hoc Ontology
• The concept Project Manager, Concept 7, has instances {Bill
O’Connell, General Manager, New Hall UF} with fixed attributes
{Name, Hierarchy, Project In Charge}
• The instance Project Manager O’Connell has the values Name=Bill
O’Connell, Hierarchy = General Manager, Project in Charge=New
Hall UF
• A set of relations among concepts is {is a, part of, has}. These
relations are generally denoted as PartOf(Project Manager,
Administration) and they describe associated defined relations
within classes, inheritance relations, and instances of properties.
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Construction Business Ontology
• The nodes in the ontology
represent concepts that have
levels of specializations from their
parent concept.
• The
relationships
among
concepts,
for
example,
Isa(CPVC, Plastic Pipe Fittings)
among the Concepts 11 and 9,
• The ‘is a’ relation corresponds to
a semantic link between two
concepts
• The ‘has’ relation corresponds to
a semantic link that constrain the
subsumption relation to a
directional relation of containment
• The relations of the concepts are
Subsumption = It is relation of implication which relates
similar to following down the
more specific to more general concepts
hierarchy of a taxonomy. A
Taxonomy = Conserve a hierarchy through generalization/ taxonomy is a central component
specializations relations
of an ontology
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Construction Business Ontology (continued)
• Subsumption relation as in a taxonomy
 ‘Pipes and Tubes’
 subsume
 ‘Plastic Pipes and Fittings’
- The semantic properties of ‘Pipes and
Tubes’ subsume the semantic properties
of ‘Plastic Pipes and Fittings’
•
•
Generalization/specialization relationship
between roles as in a taxonomy
The role ‘has’ expresses = RELATION OF
CONTAINMENT
– Roles in the simplest case are represent
relationships, and are unidirectional.
– 1-1/4” PVC, Plastic Pipe (13)
 HAS
-
 ‘Crew’
 ‘Daily Output’
The role has expresses as the
generalization of all the annotated roles.
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Construction Business Ontology (continued)
• Generalization/specialization
relationship between roles as in a
taxonomy
• The role ‘instance of’ expresses = the
values for each concept that default
values are for any attributes
 1-1/4” PVC, Plastic Pipe (13)
 Instance of
‘Q-1’
 ‘42’
 ‘L.F.’
 ‘2.45’
- There is a direct correspondence of
the the default values to the
annotated roles by following the order.
This is possible by applying rules of
inferences that any ontology language
provides
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Building Openings Ontology
• The Figure represents a
taxonomy of openings
concepts.
• The nodes represent
concepts within a
category at any level.
• The Figure shows
opening types classified
according to their
attributes and their
functionality.
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Building Openings Ontology
• An aluminum metallic screen door,
stainless-steel metallic skylight
• An ontology is not necessary a
hierarchical tree. It can resemble
any a-cyclical structure, as it is
shown in the Figure.
– The child Metallic is subsumed
by
Door
Screen
Frame
Skylight
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Summary
• The course defines the concept of
ontologies and why they are needed. In
addition the process of building ontologies
is explained and several examples are
used to illustrate the process.
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References and Readings
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References and Readings
• Davies J. N., Fensel D. and Van Harmelen F. (2003). Towards
the Semantic Web: Ontology-Driven Knowledge Management,
John Wiley & Sons.
• Farquhar A., Fikes, R., Pratt W. and Rice J.(1995). “Collaborative
Ontology Construction for Information Integration,” Stanford
University WWW Archive, source: http://www-kslsvc.stanford.edu:5915/.
• Fensel D. (2004). Ontologies – A Silver Bullet for Knowledge
Management and Electronic Commerce, Springer.
• Gruber T.R. (1993). A translation Approach to Portable Ontology
Specification, Journal of Knowledge Acquisition, (5), pp.199-220.
• Gruninger M. and Lee J. (2002). “Ontology, Applications and
Design,” Communications of the ACM, (45)2, pp. 39-41.
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References and Readings (continued)
• Lubell J. “XML Representation of Process Descriptions”,
http://ats.nist.gov/psl/xml/process-descriptions.html
• Mutis I. (2007). "A Conceptual Framework For Interpretation Of
Construction Domain Concept Representations," Ph.D.
Dissertation, University of Florida, Gainesville.
• Gomez-Perez A., Corcho O. and Fernandez-Lopez, M. (2005).
Ontological Engineering, 1st Ed., Springer, London.
• Guarino N. (1997). Understanding, building and using ontologies.
International Journal Human-Computer Studies, (46), pp. 293-310.
• Guarino N. (1998). Formal Ontology and Information Systems,
FOIS’98, Trento, Italy, IOS Press. 12 p
• Oguejiofor E., Kicinger R., Popovici E., Arciszewski T., and
DeJong K.(2004). “Intelligent Tutoring Systems: An Ontology
Based Approach,” International Journal of Computing in
Architecture, Engineering, and Construction, (2)2.
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References and Readings (continued)
• Ugwu O.O., Anumba C.J., Thorpe A. (2001). “Ontology
Development for Agent-Based Collaborative Design,” Journal of
Engineering Construction and Architectural Management, (8)3, pp.
211-224.
• Uschold, M., King, M., Moralee, S., and Zorgios, Y. (1998). "The
Enterprise Ontology." The Knowledge Engineering Review, 13(1),
31 - 39.
• Sowa, J. F. (1999). Knowledge Representation: Logical,
Philosophical, and Computational Foundations, Brooks Cole
Publishing Co, Pacific Grove, CA.
• Staab, S. Studer, R., (Editors), (2004), “Handbook on Ontologies”,
Springer, 1st edition.
• W3C (2004). Web Ontology Language (OWL). World Wide Web
Consortium (W3C). www.w3.org/. Accessed April 2006.
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