Chapter 7

Report
Chapter 7
Ontology Engineering
Grigoris Antoniou
Frank van Harmelen
1
Chapter 7
A Semantic Web Primer
Lecture Outline
1.
2.
3.
4.
5.
6.
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Introduction
Constructing Ontologies Manually
Reusing Existing Ontologies
Semiautomatic Ontology Acquisition
Ontology Mapping
On-To-Knowledge SW Architecture
Chapter 7
A Semantic Web Primer
Methodological Questions
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Many of these questions for the Semantic
Web have been studied in other contexts
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How can tools and techniques best be applied?
Which languages and tools should be used in
which circumstances, and in which order?
What about issues of quality control and resource
management?
E.g. software engineering, object-oriented design,
and knowledge engineering
Chapter 7
A Semantic Web Primer
Lecture Outline
1.
2.
3.
4.
5.
6.
4
Introduction
Constructing Ontologies Manually
Reusing Existing Ontologies
Semiautomatic Ontology Acquisition
Ontology Mapping
On-To-Knowledge SW Architecture
Chapter 7
A Semantic Web Primer
Main Stages in Ontology Development
Determine scope
2.
Consider reuse
3.
Enumerate terms
4.
Define taxonomy
5.
Define properties
6.
Define facets
7.
Define instances
8.
Check for anomalies
Not a linear process!
1.
5
Chapter 7
A Semantic Web Primer
Determine Scope
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There is no correct ontology of a specific
domain
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What is included in this abstraction should be
determined by
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An ontology is an abstraction of a particular
domain, and there are always viable alternatives
the use to which the ontology will be put
by future extensions that are already anticipated
Chapter 7
A Semantic Web Primer
Determine Scope (2)
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Basic questions to be answered at this stage
are:
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What is the domain that the ontology will cover?
For what we are going to use the ontology?
For what types of questions should the ontology
provide answers?
Who will use and maintain the ontology?
Chapter 7
A Semantic Web Primer
Consider Reuse
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With the spreading deployment of the
Semantic Web, ontologies will become more
widely available
We rarely have to start from scratch when
defining an ontology
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There is almost always an ontology available from
a third party that provides at least a useful starting
point for our own ontology
Chapter 7
A Semantic Web Primer
Enumerate Terms
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Write down in an unstructured list all the relevant
terms that are expected to appear in the ontology
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Traditional knowledge engineering tools (e.g.
laddering and grid analysis) can be used to obtain
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Nouns form the basis for class names
Verbs (or verb phrases) form the basis for property names
the set of terms
an initial structure for these terms
Chapter 7
A Semantic Web Primer
Define Taxonomy
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Relevant terms must be organized in a
taxonomic hierarchy
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Ensure that hierarchy is indeed a taxonomy:
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Opinions differ on whether it is more
efficient/reliable to do this in a top-down or a
bottom-up fashion
If A is a subclass of B, then every instance of A
must also be an instance of B (compatible with
semantics of rdfs:subClassOf
Chapter 7
A Semantic Web Primer
Define Properties
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Often interleaved with the previous step
The semantics of subClassOf demands that
whenever A is a subclass of B, every
property statement that holds for instances of
B must also apply to instances of A
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It makes sense to attach properties to the highest
class in the hierarchy to which they apply
Chapter 7
A Semantic Web Primer
Define Properties (2)
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While attaching properties to classes, it
makes sense to immediately provide
statements about the domain and range of
these properties
There is a methodological tension here
between generality and specificity:
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Flexibility (inheritance to subclasses)
Detection of inconsistencies and misconceptions
Chapter 7
A Semantic Web Primer
Define Facets: From RDFS to OWL
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Cardinality restrictions
Required values
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Relational characteristics
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owl:hasValue
owl:allValuesFrom
owl:someValuesFrom
symmetry, transitivity, inverse properties,
functional values
Chapter 7
A Semantic Web Primer
Define Instances
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Filling the ontologies with such instances is a
separate step
Number of instances >> number of classes
Thus populating an ontology with instances
is not done manually
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Retrieved from legacy data sources (DBs)
Extracted automatically from a text corpus
Chapter 7
A Semantic Web Primer
Check for Anomalies
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An important advantage of the use of OWL over RDF
Schema is the possibility to detect inconsistencies
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Examples of common inconsistencies
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In ontology or ontology+instances
incompatible domain and range definitions for transitive,
symmetric, or inverse properties
cardinality properties
requirements on property values can conflict with domain
and range restrictions
Chapter 7
A Semantic Web Primer
Lecture Outline
1.
2.
3.
4.
5.
6.
16
Introduction
Constructing Ontologies Manually
Reusing Existing Ontologies
Semiautomatic Ontology Acquisition
Ontology Mapping
On-To-Knowledge SW Architecture
Chapter 7
A Semantic Web Primer
Existing Domain-Specific Ontologies
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Medical domain: Cancer ontology from the National
Cancer Institute in the United States
Cultural domain:
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Art and Architecture Thesaurus (AAT) with 125,000 terms
in the cultural domain
Union List of Artist Names (ULAN), with 220,000 entries on
artists
Iconclass vocabulary of 28,000 terms for describing cultural
images
Geographical domain: Getty Thesaurus of
Geographic Names (TGN), containing over 1 million
entries
Chapter 7
A Semantic Web Primer
Integrated Vocabularies
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Merge independently developed vocabularies into a
single large resource
E.g. Unified Medical Language System
integrating100 biomedical vocabularies
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The semantics of a resource that integrates many
independently developed vocabularies is rather low
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The UMLS metathesaurus contains 750,000 concepts, with
over 10 million links between them
But very useful in many applications as starting point
Chapter 7
A Semantic Web Primer
Upper-Level Ontologies
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Some attempts have been made to define
very generally applicable ontologies
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Mot domain-specific
Cyc, with 60,000 assertions on 6,000
concepts
Standard Upperlevel Ontology (SUO)
Chapter 7
A Semantic Web Primer
Topic Hierarchies
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Some “ontologies” do not deserve this name:
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simply sets of terms, loosely organized in a hierarchy
This hierarchy is typically not a strict taxonomy but
rather mixes different specialization relations (e.g.
is-a, part-of, contained-in)
Such resources often very useful as starting point
Example: Open Directory hierarchy, containing
more then 400,000 hierarchically organized
categories and available in RDF format
Chapter 7
A Semantic Web Primer
Linguistic Resources
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Some resources were originally built not as
abstractions of a particular domain, but
rather as linguistic resources
These have been shown to be useful as
starting places for ontology development
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E.g. WordNet, with over 90,000 word senses
Chapter 7
A Semantic Web Primer
Ontology Libraries
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Attempts are currently underway to construct online
libraries of online ontologies
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Rarely existing ontologies can be reused without changes
Existing concepts and properties must be refined using
rdfs:subClassOf and rdfs:subPropertyOf
Alternative names must be introduced which are better
suited to the particular domain using owl:equivalentClass
and owl:equivalentProperty
We can exploit the fact that RDF and OWL allow private
refinements of classes defined in other ontologies
Chapter 7
A Semantic Web Primer
Lecture Outline
1.
2.
3.
4.
5.
6.
23
Introduction
Constructing Ontologies Manually
Reusing Existing Ontologies
Semiautomatic Ontology Acquisition
Ontology Mapping
On-To-Knowledge SW Architecture
Chapter 7
A Semantic Web Primer
The Knowledge Acquisition Bottleneck
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Manual ontology acquisition remains a timeconsuming, expensive, highly skilled, and
sometimes cumbersome task
Machine Learning techniques may be used
to alleviate
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knowledge acquisition or extraction
knowledge revision or maintenance
Chapter 7
A Semantic Web Primer
Tasks Supported by Machine Learning
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Extraction of ontologies from existing data on the
Web
Extraction of relational data and metadata from
existing data on the Web
Merging and mapping ontologies by analyzing
extensions of concepts
Maintaining ontologies by analyzing instance data
Improving SW applications by observing users
Chapter 7
A Semantic Web Primer
Useful Machine Learning Techniques
for Ontology Engineering
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Clustering
Incremental ontology updates
Support for the knowledge engineer
Improving large natural language ontologies
Pure (domain) ontology learning
Chapter 7
A Semantic Web Primer
Machine Learning Techniques for
Natural Language Ontologies
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Natural language ontologies (NLOs) contain lexical
relations between language concepts
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The state of the art in NLO learning looks quite
optimistic:
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They are large in size and do not require frequent updates
A stable general-purpose NLO exist
Techniques for automatically or semi-automatically
constructing and enriching domain-specific NLOs exist
Chapter 7
A Semantic Web Primer
Machine Learning Techniques for
Domain Ontologies
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They provide detailed descriptions
Usually they are constructed manually
The acquisition of the domain ontologies is
still guided by a human knowledge engineer
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Automated learning techniques play a minor role
in knowledge acquisition
They have to find statistically valid
dependencies in the domain texts and suggest
them to the knowledge engineer
Chapter 7
A Semantic Web Primer
Machine Learning Techniques for
Ontology Instances
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Ontology instances can be generated
automatically and frequently updated while
the ontology remains unchanged
Fits nicely into a machine learning framework
Successful ML applications
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Are strictly dependent on the domain ontology, or
Populate the markup without relating to any
domain theory
General-purpose techniques not yet available
Chapter 7
A Semantic Web Primer
Different Uses of Ontology Learning
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Ontology acquisition tasks in knowledge engineering
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Ontology maintenance tasks
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Ontology creation from scratch by the knowledge engineer
Ontology schema extraction from Web documents
Extraction of ontology instances from Web documents
Ontology integration and navigation
Updating some parts of an ontology
Ontology enrichment or tuning
Chapter 7
A Semantic Web Primer
Ontology Acquisition Tasks
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Ontology creation from scratch by the knowledge
engineer
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Ontology schema extraction from Web documents
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ML assists the knowledge engineer by suggesting the most
important relations in the field or checking and verifying the
constructed knowledge bases
ML takes the data and meta-knowledge (like a metaontology) as input and generate the ready-to-use ontology
as output with the possible help of the knowledge engineer
Chapter 7
A Semantic Web Primer
Ontology Acquisition Tasks(2)
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Extraction of ontology instances from Web
documents
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This task extracts the instances of the ontology
presented in the Web documents and populates
given ontology schemas
This task is similar to information extraction and
page annotation, and can apply the techniques
developed in these areas
Chapter 7
A Semantic Web Primer
Ontology Maintenance Tasks
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Ontology integration and navigation
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Updating some parts of an ontology that are
designed to be updated
Ontology enrichment or tuning
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Deals with reconstructing and navigating in large
and possibly machine-learned knowledge bases
This does not change major concepts and
structures but makes an ontology more precise
Chapter 7
A Semantic Web Primer
Potentially Applicable Machine
Learning Algorithms
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Propositional rule learning algorithms
Bayesian learning
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First-order logic rules learning
Clustering algorithms
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generates probabilistic attribute-value rules
They group the instances together based on the
similarity or distance measures between a pair of
instances defined in terms of their attribute values
Chapter 7
A Semantic Web Primer
Lecture Outline
1.
2.
3.
4.
5.
6.
35
Introduction
Constructing Ontologies Manually
Reusing Existing Ontologies
Semiautomatic Ontology Acquisition
Ontology Mapping
On-To-Knowledge SW Architecture
Chapter 7
A Semantic Web Primer
Ontology Mapping
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A single ontology will rarely fulfill the needs
of a particular application; multiple ontologies
will have to be combined
This raises the problem of ontology
integration (also called ontology alignment or
ontology mapping)
Current approaches deploy a whole host of
different methods; we distinguish linguistic,
statistical, structural and logical methods
Chapter 7
A Semantic Web Primer
Linguistic methods
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The most basic methods try to exploit the
linguistic labels attached to the concepts in
source and target ontology in order to discover
potential matches
This can be as simple as basic stemming
techniques or calculating Hamming distances, or
it can use specialized domain knowledge (e.g.
the difference between Diabetes Melitus type I
and Diabetes Melitus type II is not a negligible
difference to be removed by a small Hamming
distance)
Chapter 7
A Semantic Web Primer
Statistical Methods
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Some methods use instance data, to determine
correspondences between concepts
A significant statistical correlation between the
instances of a source concept and a target
concept, gives us reason to believe that these
concepts are strongly related
These approaches rely on the availability of a
sufficiently large corpus of instances that are
classified in both the source and the target
ontologies
Chapter 7
A Semantic Web Primer
Structural Methods
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Since ontologies have internal structure, it
makes sense to exploit the graph structure of
the source and the target ontologies and try
to determine similarities, often in coordination
with other methods
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If a source target and a target concept have similar linguistic
labels, then the dissimilarity of their graph neighborhoods
could be used to detect homonym problems where purely
linguistic methods would falsely declare a potential mapping
Chapter 7
A Semantic Web Primer
Logical Methods
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The most specific to mapping ontologies
A serious limitation of this approach is that
many practical ontologies are semantically
rather lightweight and thus don’t carry much
logical formalism with them
Chapter 7
A Semantic Web Primer
Ontology-Mapping Techniques
Conclusion
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Although there is much potential, and indeed
need, for these techniques to be deployed for
Semantic Web engineering, this is far from a
well-understood area
No off-the-shelf techniques are currently
available, and it is not clear that this is likely
to change in the near future
Chapter 7
A Semantic Web Primer
Lecture Outline
1.
2.
3.
4.
5.
6.
42
Introduction
Constructing Ontologies Manually
Reusing Existing Ontologies
Semiautomatic Ontology Acquisition
Ontology Mapping
On-To-Knowledge SW Architecture
Chapter 7
A Semantic Web Primer
On-To-Knowledge Architecture
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Building the Semantic Web involves using
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the new languages described in this course
a rather different style of engineering
a rather different approach to application integration
We describe how a number of Semantic Web-related
tools can be integrated in a single lightweight
architecture using Semantic Web standards to
achieve interoperability between tools
Chapter 7
A Semantic Web Primer
Knowledge Acquisition
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Initially, tools must exist that use surface
analysis techniques to obtain content from
documents
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Unstructured natural language documents:
statistical techniques and shallow natural language
technology
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Structured and semi-structured documents:
wrappers induction, pattern recognition
44
Chapter 7
A Semantic Web Primer
Knowledge Storage
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The output of the analysis tools is sets of concepts,
organized in a shallow concept hierarchy with at best
very few cross-taxonomical relationships
RDF/RDF Schema are sufficiently expressive to
represent the extracted info
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Store the knowledge produced by the extraction tools
Retrieve this knowledge, preferably using a structured query
language (e.g. RQL)
Chapter 7
A Semantic Web Primer
Knowledge Maintenance and Use
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A practical Semantic Web repository must provide
functionality for managing and maintaining the
ontology:
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There must be support for both
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change management
access and ownership rights
transaction management
Lightweight ontologies that are automatically generated
from unstructured and semi-structured data
Human engineering of much more knowledge-intensive
ontologies
Chapter 7
A Semantic Web Primer
Knowledge Maintenance and Use (2)
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Sophisticated editing environments must be able to
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The ontologies and data in the repository are to be
used by applications that serve an end-user
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Retrieve ontologies from the repository
Allow a knowledge engineer to manipulate it
Place it back in the repository
We have already described a number of such applications
Chapter 7
A Semantic Web Primer
Technical Interoperability
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Syntactic interoperability was achieved because all
components communicated in RDF
Semantic interoperability was achieved because all
semantics was expressed using RDF Schema
Physical interoperability was achieved because
All communications between components were
established using simple HTTP connections
Chapter 7
A Semantic Web Primer
On-To-Knowledge System Architecture
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Chapter 7
A Semantic Web Primer

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