chapter 7 ontology engineering

42
Chapter 7 A Semantic Web Primer 1 Chapter 7 Ontology Engineering Grigoris Antoniou Frank van Harmelen

Upload: madaline-giles

Post on 03-Jan-2016

48 views

Category:

Documents


4 download

DESCRIPTION

Chapter 7 Ontology Engineering. Grigoris Antoniou Frank van Harmelen. Lecture Outline. Introduction Constructing Ontologies Manually Reusing Existing Ontologies Using Semiautomatic Methods On-To-Knowledge SW Architecture. Methodological Questions. - PowerPoint PPT Presentation

TRANSCRIPT

Page 1: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer1

Chapter 7Ontology Engineering

Grigoris Antoniou

Frank van Harmelen

Page 2: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer2

Lecture Outline

1. Introduction

2. Constructing Ontologies Manually

3. Reusing Existing Ontologies

4. Using Semiautomatic Methods

5. On-To-Knowledge SW Architecture

Page 3: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer3

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

Page 4: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer4

Lecture Outline

1. Introduction

2. Constructing Ontologies Manually

3. Reusing Existing Ontologies

4. Using Semiautomatic Methods

5. On-To-Knowledge SW Architecture

Page 5: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer5

Main Stages in Ontology Development

1. Determine scope2. Consider reuse3. Enumerate terms4. Define taxonomy5. Define properties6. Define facets7. Define instances8. Check for anomaliesNot a linear process!

Page 6: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer6

Determine Scope

There is no 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

Page 7: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer7

Determine Scope (2)

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?

Page 8: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer8

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

Page 9: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer9

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

Page 10: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer10

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

Page 11: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer11

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

Page 12: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer12

Define Properties (2)

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

Page 13: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer13

Define Facets: From RDFS to OWL

Cardinality restrictions Required values

– owl:hasValue – owl:allValuesFrom– owl:someValuesFrom

Relational characteristics– symmetry, transitivity, inverse properties,

functional values

Page 14: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer14

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

Page 15: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer15

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

Page 16: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer16

Lecture Outline

1. Introduction

2. Constructing Ontologies Manually

3. Reusing Existing Ontologies

4. Using Semiautomatic Methods

5. On-To-Knowledge SW Architecture

Page 17: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer17

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

Page 18: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer18

Integrated Vocabularies

Merge independently developed vocabularies into a single large resource

E.g. Unified Medical Language System integrating100 biomedical vocabularies – The UMLS metathesaurus 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

Page 19: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer19

Upper-Level Ontologies

Some attempts have been made to define very generally applicable ontologies – Mot domain-specific

Cyc, with 60,000 assertions on 6,000 concepts

Standard Upperlevel Ontology (SUO)

Page 20: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer20

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

Page 21: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer21

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

Page 22: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer22

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

Page 23: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer23

Lecture Outline

1. Introduction

2. Constructing Ontologies Manually

3. Reusing Existing Ontologies

4. Using Semiautomatic Methods

5. On-To-Knowledge SW Architecture

Page 24: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer24

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

Page 25: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer25

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

Page 26: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer26

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

Page 27: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer27

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

Page 28: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer28

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

Page 29: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer29

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

Page 30: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer30

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

Page 31: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer31

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

Page 32: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer32

Ontology Acquisition Tasks(2)

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

Page 33: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer33

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

Page 34: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer34

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

Page 35: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer35

Lecture Outline

1. Introduction

2. Constructing Ontologies Manually

3. Reusing Existing Ontologies

4. Using Semiautomatic Methods

5. On-To-Knowledge SW Architecture

Page 36: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer36

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

Page 37: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer37

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

Page 38: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer38

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)

Page 39: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer39

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

Page 40: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer40

Knowledge Maintenance and Use (2)

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

Page 41: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer41

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

Page 42: Chapter 7 Ontology Engineering

Chapter 7 A Semantic Web Primer42

On-To-Knowledge System Architecture