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CSIT600f: Introduction to Semantic Web
Ontology Engineering
Dickson K.W. ChiuPhD, SMIEEE
Text: Antoniou & van Harmelen: A Semantic Web Primer
(Chapter 7)
Dickson Chiu 2005 CSIT600f 05b-2
Lecture Outline
1. Introduction2. Constructing Ontologies Manually3. Reusing Existing Ontologies4. Using Semiautomatic Methods5. On-To-Knowledge SW Architecture
Dickson Chiu 2005 CSIT600f 05b-3
Methodological Questions
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?
? Many of these questions for the Semantic Web have been studied in other contexts E.g. software engineering, object-oriented
design, and knowledge engineering
Dickson Chiu 2005 CSIT600f 05b-4
Lecture Outline
1. Introduction2. Constructing Ontologies Manually3. Reusing Existing Ontologies4. Using Semiautomatic Methods5. On-To-Knowledge SW Architecture
Dickson Chiu 2005 CSIT600f 05b-5
Main Stages in Ontology Development
1. Determine scope2. Consider reuse3. Enumerate terms4. Define taxonomy5. Define properties6. Define facets7. Define instances8. Check for anomalies
Not a linear process!
Dickson Chiu 2005 CSIT600f 05b-6
Determine Scope Many possible correct ontology of a specific
domain An ontology is an abstraction of a particular domain, and
there are always viable alternatives What is included in this abstraction should be
determined by the use to which the ontology will be put by future extensions that are already anticipated
Basic questions to be answered at this stage are: 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?
Dickson Chiu 2005 CSIT600f 05b-7
Consider Reuse
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 There is almost always an ontology
available from a third party that provides at least a useful starting point for our own ontology
Dickson Chiu 2005 CSIT600f 05b-8
Enumerate Terms
Write down in an unstructured list all the relevant terms that are expected to appear in the ontology Nouns form the basis for class names Verbs (or verb phrases) form the basis for
property names Traditional knowledge engineering tools
(e.g. laddering and grid analysis) can be used to obtain the set of terms an initial structure for these terms
Dickson Chiu 2005 CSIT600f 05b-9
Define Taxonomy
Relevant terms must be organized in a taxonomic hierarchy
Opinions differ on whether it is more efficient/reliable to do this in a top-down or a bottom-up fashion
Ensure that hierarchy is indeed a taxonomy:
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
Dickson Chiu 2005 CSIT600f 05b-10
Define Properties
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
It makes sense to attach properties to the highest class in the hierarchy to which they apply
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:
Flexibility (inheritance to subclasses) Detection of inconsistencies and misconceptions
Dickson Chiu 2005 CSIT600f 05b-11
Define Facets: From RDFS to OWL
Cardinality restrictions Required values
owl:hasValue owl:allValuesFrom owl:someValuesFrom
Relational characteristics symmetry, transitivity, inverse properties,
functional values
Dickson Chiu 2005 CSIT600f 05b-12
Define Instances
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 Retrieved from legacy data sources (DBs) Extracted automatically from a text corpus
Dickson Chiu 2005 CSIT600f 05b-13
Check for Anomalies
An important advantage of the use of OWL over RDF Schema is the possibility to detect inconsistencies In ontology or ontology+instances
Examples of common inconsistencies incompatible domain and range definitions
for transitive, symmetric, or inverse properties
cardinality properties requirements on property values can
conflict with domain and range restrictions
Dickson Chiu 2005 CSIT600f 05b-14
Lecture Outline
1. Introduction2. Constructing Ontologies Manually3. Reusing Existing Ontologies4. Using Semiautomatic Methods5. On-To-Knowledge SW Architecture
Dickson Chiu 2005 CSIT600f 05b-15
Existing Domain-Specific Ontologies
Medical domain: Cancer ontology from the National Cancer Institute in the United States
Cultural domain: 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
Dickson Chiu 2005 CSIT600f 05b-16
Integrated Vocabularies
Merge independently developed vocabularies into a single large resource
E.g., Unified Medical Language System integrating 100 biomedical vocabularies The UMLS meta-thesaurus contains 750,000
concepts, with over 10 million links between them The semantics of a resource that integrates
many independently developed vocabularies is rather low But very useful in many applications as starting
point
Dickson Chiu 2005 CSIT600f 05b-17
Upper-Level Ontologies
Some attempts have been made to define very generally applicable ontologies
CYC - with 60,000 assertions on 6,000 concepts
IEEE Standard Upperlevel Ontology (SUO)
Dickson Chiu 2005 CSIT600f 05b-18
Topic Hierarchies
Some “ontologies” do not deserve this name: 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
Dickson Chiu 2005 CSIT600f 05b-19
Linguistic Resources
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 E.g., WordNet, with over 90,000 word
senses
Dickson Chiu 2005 CSIT600f 05b-20
Ontology Libraries
Attempts are currently underway to construct online libraries of online ontologies
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
Dickson Chiu 2005 CSIT600f 05b-21
Lecture Outline
1. Introduction2. Constructing Ontologies Manually3. Reusing Existing Ontologies4. Using Semiautomatic Methods5. On-To-Knowledge SW Architecture
Dickson Chiu 2005 CSIT600f 05b-22
The Knowledge Acquisition Bottleneck
Manual ontology acquisition remains a time-consuming, expensive, highly skilled, and sometimes cumbersome task
Machine Learning techniques may be used to alleviate knowledge acquisition or extraction knowledge revision or maintenance
Dickson Chiu 2005 CSIT600f 05b-23
Tasks Supported by Machine Learning
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
Dickson Chiu 2005 CSIT600f 05b-24
Useful Machine Learning Techniques for Ontology Engineering
Clustering Incremental ontology updates Support for the knowledge engineer Improving large natural language
ontologies Pure (domain) ontology learning
Dickson Chiu 2005 CSIT600f 05b-25
Machine Learning Techniques for Natural Language Ontologies
Natural language ontologies (NLOs) contain lexical relations between language concepts They are large in size and do not require
frequent updates The state of the art in NLO learning
looks quite optimistic: A stable general-purpose NLO exist Techniques for automatically or semi-
automatically constructing and enriching domain-specific NLOs exist
Dickson Chiu 2005 CSIT600f 05b-26
Machine Learning Techniques for Domain Ontologies
They provide detailed descriptions Usually they are constructed manually The acquisition of the domain
ontologies is still guided by a human knowledge engineer
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
Dickson Chiu 2005 CSIT600f 05b-27
Machine Learning Techniques for Ontology Instances
Ontology instances can be generated automatically and frequently updated while the ontology remains unchanged
Fits nicely into a machine learning framework
Successful ML applications Are strictly dependent on the domain
ontology, or Populate the markup without relating to any
domain theory General-purpose techniques not yet
available
Dickson Chiu 2005 CSIT600f 05b-28
Different Uses of Ontology Learning
Ontology acquisition tasks in knowledge engineering Ontology creation from scratch by the
knowledge engineer Ontology schema extraction from Web
documents Extraction of ontology instances from Web
documents Ontology maintenance tasks
Ontology integration and navigation Updating some parts of an ontology Ontology enrichment or tuning
Dickson Chiu 2005 CSIT600f 05b-29
Ontology Acquisition Tasks
Ontology creation from scratch by the knowledge engineer
ML assists the knowledge engineer by suggesting the most important relations in the field or checking and verifying the constructed knowledge bases
Ontology schema extraction from Web documents ML takes the data and meta-knowledge (like a meta-ontology)
as input and generate the ready-to-use ontology as output with the possible help of the knowledge engineer
Extraction of ontology instances from Web documents 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
Dickson Chiu 2005 CSIT600f 05b-30
Ontology Maintenance Tasks
Ontology integration and navigation Deals with reconstructing and navigating in
large and possibly machine-learned knowledge bases
Updating some parts of an ontology that are designed to be updated
Ontology enrichment or tuning This does not change major concepts and
structures but makes an ontology more precise
Dickson Chiu 2005 CSIT600f 05b-31
Potentially Applicable Machine Learning Algorithms
Propositional rule learning algorithms Bayesian learning
generates probabilistic attribute-value rules First-order logic rules learning Clustering algorithms
They group the instances together based on the similarity or distance measures between a pair of instances defined in terms of their attribute values
Dickson Chiu 2005 CSIT600f 05b-32
Lecture Outline
1. Introduction2. Constructing Ontologies Manually3. Reusing Existing Ontologies4. Using Semiautomatic Methods5. On-To-Knowledge SW Architecture
Dickson Chiu 2005 CSIT600f 05b-33
On-To-Knowledge Architecture
Building the Semantic Web involves using 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
Dickson Chiu 2005 CSIT600f 05b-34
Knowledge Acquisition
Initially, tools must exist that use surface analysis techniques to obtain content from documents Unstructured natural language documents:
statistical techniques and shallow natural language technology
Structured and semi-structured documents: wrappers induction, pattern recognition
Dickson Chiu 2005 CSIT600f 05b-35
Knowledge Storage
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 Store the knowledge produced by the
extraction tools Retrieve this knowledge, preferably using a
structured query language (e.g. RQL)
Dickson Chiu 2005 CSIT600f 05b-36
Knowledge Maintenance and Use
A practical Semantic Web repository must provide functionality for managing and maintaining the ontology:
change management access and ownership rights transaction management
There must be support for both Lightweight ontologies that are automatically generated from
unstructured and semi-structured data Human engineering of much more knowledge-intensive
ontologies Sophisticated editing environments must be able to
Retrieve ontologies from the repository Allow a knowledge engineer to manipulate it Place it back in the repository
The ontologies and data in the repository are to be used by applications that serve an end-user
We have already described a number of such applications
Dickson Chiu 2005 CSIT600f 05b-37
Technical Interoperability
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