(ontologies)! - Jian-Hua Yeh - 真理大學

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22 Οκτ 2013 (πριν από 3 χρόνια και 7 μήνες)

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知識架構的理論與發展

Jian
-
hua Yeh (
葉建華
)

真理大學資訊科學系助理教授

au4290@email.au.edu.tw

2

Outline


Ontology


The problem


What is an ontology?


Why develop an ontology?


Usage of ontology


Complexity and Processing of ontology


OWL introduction


Topic maps


Concepts

3

The
P
roblem


With the increasing complexity of our systems and our IT needs, we need to go to

human level interaction


We need to
maximize
the amount of
Semantics

we can utilize


From data and information level, we need to go to
human
semantic

level interaction

DATA

Information

Knowledge

Run84

ID=08

NULL

PARRT

ACC

ID=34

e

5

&

#

~

Q

ü

@

¥

¥

Æ



Å

Tank

¥

Noise

Human Meaning

Vehicle

Located at

Semi
-
mountainous terrain

obscured

decide

Vise maneuver


And represented semantics means multipl
e

represented
semantics,

requiring
semantic integration

4

Simple Metadata:

XML

Advancing Along the Interpretation Continuum

Human interpreted

Computer interpreted

DATA

KNOWLEDGE


Relatively unstructured


Random


Very structured


Logical

Moving to the right depends on increasing automated semantic interpretation


Info
retrieval


Web search


Text summarization


Content extraction


Topic maps


Reasoning
services


Ontology
Induction

...

Display raw
documents;

All interpretation
done by humans

Find and
correlate patterns
in raw docs;
display matches
only

Store and connect
patterns via
conceptual model
(i.e,. an ontology);
link to docs to aid
retrieval

Automatically acquire
concepts; evolve
ontologies into domain
theories; link to
institution repositories
(e.g., MII)

Richer Metadata:

RDF/S

Very Rich Metadata:

DAML+OIL

Automatically span
domain theories and
institution
repositories; inter
-
operate with fully
interpreting computer

Interpretation Continuum

5

Dimensions of Interoperability & Integration

Enterprise

Object

Data

System

Application

Component

0%

100%

3 Kinds of Integration

Interoperability Scale

Our interest lies here

Community

6

Information Semantics


Provide
semantic representation (meaning)

for our systems, our data,
our documents, our agents



Focus on machines more closely interacting at human
conceptual
level



Spans Ontologies, Knowledge Representation, Semantic Web,
Semantics in NLP, Knowledge Management



Linking notion is
Ontologies

(rich formal models)

7

The Smart Data Enterprise

Data has progressed through
four stages of increasing
intelligence

8

Triangle of Signification

Terms

Concepts

Real (& Possible)

World Referents

Sense

Reference/

Denotation

<Joe_ Montana >


Joe” + “Montana”

Syntax: Symbols

Semantics: Meaning

Pragmatics: Use

Intension

Extension

9

What is an Ontology?


Many definitions of an ontology contradict one
another.


One formal definition


A formal explicit description of concepts in a domain of
discourse (
classes
), properties of each concept describing
various features and attributes of the concept (
slots
), and
restriction on slots.

10

What is an Ontology? (2)


Another definition


The subject of
ontology

is the study of the
categories

of things that
exist or may exist in some domain.


A simple definition


Ontology is about the exact description of things and their
relationships.




An ontology is a specification of a conceptualization” [Gruber
95]

11

0

2000

1613

384
-
322 BC

Aristotle


Ontolog
y’ coined

1967

First occurrence of ontology in Information
Science

1000

1721

First occurrence in OED

Ontology Background

Timeline (Smith 2002)

12

Aristotle's Categories

13

Genus and Differentiate

14

Cyc Project: Large Ontology


Cyc contains about 100,000 concept types

15

Why Develop an Ontology?


Semantic Interoperability


Generalized database integration


Virtual Enterprises


e
-
commerce


Information Retrieval


Decoupling user vocabulary from data vocabulary


Query answering over document sets


Natural Language Processing

16

Different Uses of Ontologies


Application ontologies (run time)


Offer terminological services, checking constraints between
terms


Limited expressivity (stringent computational reqs)


Reference ontologies (develop. time)


Establish consensus about meaning of terms (in general)


Higher expressivity (less stringent computational reqs)


Mutual understanding more important than mass
interoperability


Understanding disagreements


Establish trustable mappingsamong application ontologies

17

Ontology Structure Levels


The term
ontology

has been used to describe models with different degrees
of structure (Ontology Spectrum)


Less structure:

Taxonomies

(Semio taxonomies,
Yahoo

hierarchy, biological
taxonomy),
Database Schemas

(many) and metadata schemes (ICML, ebXML,
WSDL)


More Structure:

Thesauri

(
WordNet
, CALL, DTIC),
Conceptual Models

(OO models,
UML)


Most Structure:

Logical Theories

(
Ontolingua
, TOVE, CYC, Semantic Web)


Ontologies are usually expressed in a logic
-
based language


Enabling detailed, sound, meaningful distinctions to be made among the classes,
properties, & relations


More expressive meaning but maintain “computability”


Using ontologies, tomorrow's applications can be "intelligent”



Work at the human conceptual level

18

E
-
commerce

Area of

Interest

Mostly This

Middle Ontology

(Domain
-
spanning

Knowledge)

Most General Thing

Upper Ontology

(Generic Common

Knowledge)

Products/Services

Processes

Organizations

Locations

Lower Ontology

(individual domains)

Metal Parts

Art Supplies

Lowest Ontology

(sub
-
domains)

Washers

But Also This!

Ontology: General Picture at Object Level

19

Complexity of Ontology

20

Ontology Processing

21

Steps:


Determine the domain and scope of ontology


Consider reusing existing ontologies


Enumerate important terms in the ontology


Define classes and the class hierarchy


Define the properties of the classes ─ slots


Define the facets of the slots
(cardinality, value
-
type)


Create instances

How to Build an Ontology

22

Building an ontology is not a goal in itself.

Communication between people

Interoperability between software agents

Reuse of domain knowledge

Make domain knowledge explicit

Analyze domain knowledge

Benefits of Building Ontologies

23

The benefits:

Modularisation


Bridging Scales and context
with Ontologies

Genes

Species

Protein

Function

Disease

Protein coded by

gene in humans

Function of

Protein coded by

gene in humans

Disease caused by abnormality in

Function of

Protein coded by

gene in humans

Gene in humans

24

Thesaurus vs. Ontology

Concepts


Semantic’ Relations:


Equivalent =


Used For (Synonym)
UF


Broader Term BT


Narrower Term NT


Related Term RT

Thesaurus

Ontology

Term

Semantics


(Weak)

Logical
-
Conceptual


Semantics


(Strong)

Semantic Relations:


Subclass Of


Part Of


Arbitrary Relations


Meta
-
Properties on
Relations

Terms
: Metal working machinery, equipment and
supplies, metal
-
cutting machinery, metal
-
turning
equipment, metal
-
milling equipment, milling insert,

turning insert, etc.

Relations
:

use, used
-
for, broader
-
term, narrower
-
term, related
-
term

Controlled Vocabulary

Terms

Real (& Possible)

World Referents

Entities
:

Metal working machinery, equipment and
supplies, metal
-
cutting machinery, metal
-
turning
equipment, metal
-
milling equipment, milling insert,
turning insert, etc.

Relations
: subclass
-
of; instance
-
of; part
-
of; has
-
geometry; performs, used
-
on;etc.

Properties
:

geometry; material; length; operation;
UN/SPSC
-
code; ISO
-
code; etc.

Values
: 1; 2; 3; “2.5 inches”; “85
-
degree
-
diamond”;
“231716”; “boring”; “drilling”; etc.

Axioms/Rules:

If milling
-
insert(X) & operation(Y) &
material(Z)=HG_Steel & performs(X, Y, Z), then
has
-
geometry(X, 85
-
degree
-
diamond).

Logical Concepts

25

weak semantics

strong semantics

Is Disjoint Subclass of
with transitivity
property

Modal Logic

Logical Theory

Thesaurus


Has Narrower Meaning Than

Taxonomy

Is Sub
-
Classification of

Conceptual Model


Is Subclass of

DB Schemas, XML Schema

UML

First Order Logic

Relational

Model, XML

ER

Extended ER

Description Logic

DAML+OIL, OWL

RDF/S

XTM

Ontology Spectrum: One View

Syntactic Interoperability

Structural Interoperability

Semantic Interoperability

Source: Obrst, L. 2004

26

Logical Theory

Thesaurus


Has Narrower Meaning Than

Taxonomy

Is Sub
-
Classification of

Conceptual Model


Is Subclass of

Is Disjoint Subclass of
with transitivity
property

weak semantics

strong semantics

DB Schemas, XML Schema

UML

Modal Logic

First Order Logic

Relational

Model, XML

ER

Extended ER

Description Logic

DAML+OIL, OWL

RDF/S

XTM

Ontology Spectrum: One View (cont.)

Problem: Very General

Semantic Expressivity: Very High

Problem: Local

Semantic Expressivity: Low

Problem: General

Semantic Expressivity: Medium

Problem: Local

Semantic Expressivity: High

Syntactic Interoperability

Structural Interoperability

Semantic Interoperability

Source: Obrst, L. 2004

27

Semantic Web Wedding Cake

28

Emerging XML Stack Architecture for the
Semantic Web + Grid + Agents


Semantic Brokers


Intelligent Agents


Advanced Applications


Use, Intent: Pragmatics


Trust:

Proof +
Security

+
Identity


Reasoning/Proof

Methods


OWL
: Ontologies


RDF Schema
: Ontologies


RDF
: Instances (assertions)


XML Schema
: Encodings of Data
Elements & Descriptions, Data Types,
Local Models


XML
: Base Documents


Grid & Semantic Grid:

New System
Services, Intelligent QoS

Sem
-
Grid Services

Water, LISP?

Syntax: Data

Structure

Semantics

Higher Semantics

Reasoning/Proof

XML

XML Schema

RDF/RDF Schema

OWL

Inference Engine

Trust

Security/Identity

Use, Intent

Pragmatic Web

Intelligent Domain Services, Applications

Agents, Brokers, Policies

29

Where We Are

We Are Here

30


What Problems Do Ontologies Help Solve?



Heterogeneous database problem


Different organizational units, Service Needers/Providers have radically different
databases


Different
syntactically:

what’s the format?


Different
structurally:

how are they structured?


Different
semantically:

what do they mean?


They all speak different languages (access, description, schemas, meaning)


Integration: rather than N
2

problem, with single, adequate Ontology reduces to N


Enterprise
-
wide system interoperability problem


Currently: system
-
of
-
systems, vertical stovepipes


Ontologies act as conceptual model representing enterprise consensus semantics


Relevant document retrieval/question
-
answering problem


What is the meaning of your query?


What is the meaning of documents that would satisfy your query?


Can you obtain only meaningful, relevant documents?


31

OWL: Web Ontology Language


OWL is built on top of RDF


OWL is for processing information on the web


OWL was designed to be interpreted by computers


OWL was not designed for being read by people


OWL is written in XML


OWL has three sublanguages


OWL is a web standard

32

Why OWL?


OWL is a part of the "Semantic Web Vision"
-

a future
where:


Web information has exact meaning


Web information can be processed by computers


Computers can integrate information from the web

33

Origins of OWL

DAML

DAML+OIL

DAML = DARPA Agent Markup
Language

OIL = Ontology Inference Layer

OWL is now on track to

become a W3C Recommendation!

OIL

OWL

RDF

All were influenced by RDF

34

OWL Sublanguages


OWL has three sublanguages:


OWL Lite


OWL DL (includes OWL Lite)


OWL Full (includes OWL DL)

35

OWL is Different from RDF


OWL and RDF are much of the same thing, but OWL
is a stronger language with greater machine
interpretability than RDF.


OWL comes with a larger vocabulary and stronger
syntax than RDF.

36

An OWL Example

37

Where is the Technology Going



The Semantic Web is very exciting, and now just starting off in the same grassroots mode
as the Web did 10 years ago ... In 10 years it will in turn have revolutionized the way we do
business, collaborate and learn.”


Tim Berners
-
Lee, CNET.com interview, 2001
-
12
-
12


We can look forward to:


Semantic Integration/Interoperability, not just data interoperability


Applications with trans
-
community semantics


Device interoperability in the ubiquitous computing future: achieved through
semantics & contextual awareness


True realization of intelligent agent interoperability


Intelligent semantic information retrieval & search engines


Next generation electronic commerce/business & web services


Semantics beginning to be used once again in NLP: information extraction
becomes
knowledge extraction


Key to all of this is effective & efficient use of explicitly represented semantics
(ontologies)!


38

What do we want the future to be?


2100 A.D: models, models, models


There are no human
-
programmed programming languages


There are only Models

Ontological Models

Knowledge Models

Belief Models

Application Models

Presentation Models

Target Platform Models

Transformations,
Compilations

Executable Code

I
N
F
R
A
S
T
R
U
C
T
U
R
E

39

40

Topic Map: Knowledge Management Concept in Digital Libraries


41

Topic Maps Introduction


Goal: organize information for navigation


Topic Maps are the online equivalent of printed
indexes


A powerful way to manage link information, such as
glossaries, cross
-
references, thesauri, catalogs, they
enable the merging of structured, unstructured
information.

42

Different Levels of Information Organization


Metadata


Thesauri


Taxonomies


Topic Maps

43

Objects and Their Metadata


What is metadata?


Metadata as a finding aid


Subjects and precision

44

Subject
-
based Classification


Controlled vocabularies


Taxonomies


Thesauri


Faceted classification


Ontologies


Other subject
-
based techniques

45

Topic Maps Concepts


Topic


A topic is a multi
-
headed link, that points to all its
occurrences


Topic occurrence


A topic type is a category to which one given topic instance
belong("person", "city", "product"…,etc)


Topic name: base name, display name, sort name

46

Topic Maps Concepts (2)


Types


is
-
a relationships


Occurrences


Relate topics to the information they are relevant to

47

Topic Maps Concepts (3)


Association


Topics can be related together through some association
expressing given semantic


Describes relationships


Facet


Multiple facets can be applied to view the topic in different
ways

48

Example

49

Example: Shakespeare’s Plays

50

XTM Element Types


<topicRef>: Reference to a Topic element


<subjectIndicatorRef>: Reference to a Subject Indicator


<scope>: Reference to Topic(s) that comprise the Scope


<instanceOf>: Points to a Topic representing a class


<topicMap>: Topic Map document element


<topic>: Topic element


<subjectIdentity>: Subject reified by Topic


<baseName>: Base Name of a Topic


<baseNameString>: Base Name String container


<variant>: Alternate forms of Base Name


<variantName>: Container for Variant Name


<parameters>: Processing context for Variant


<association>: Topic Association


<member>: Member in Topic Association


<roleSpec>: Points to a Topic serving as an Association Role


<occurrence>: Resources regarded as an Occurrence


<resourceRef>: Reference to a Resource


<resourceData>: Container for Resource data


<mergeMap>: Merge with another Topic Map

51

The Comparison


Traditional classifications in topic maps


Merging metadata and classification


Benefits and costs


Searching


Schemas


Identity and merging

52

Conclusion


53

Questions?