Ontology - PKU · Ontology Lin Zuoquan Information Science Department Peking University [email protected] lz/teaching/stm/saswws.html

OntologyLin Zuoquan

Information Science DepartmentPeking University

[email protected]://www.is.pku.edu.cn/~lz/teaching/stm/saswws.html

2Lin Zuoquan © pku/2003

OutlineIntroductionOntologyRepresentationReasoningLanguageEngineeringApplication

3

Lin Zuoquan © pku/2003

Introduction to Ontology

MotivationWhat’s Ontology


MotivationVast information is available on the WebGrowing need for

Finding relevant information (Information Extraction)Creating new knowledge out of the available information (Web Content Mining)Personalization of the web Learning about customers or individual users (Web Usage Mining)


IssuesInteroperabilityKnowledge sharing and reusing


Ontologies … For What?Lack of a shared understanding leads to poor communication

=> People, organizations and software systemsmust communicate between and amongthemselves

Disparate modeling paradigms, languages and software tools limit

=> Interoperability=> Knowledge sharing & reuse


What is being shared?Metadata

Data describing the content and meaning of resources and services.But everyone must speak the same language…

TerminologiesShared and common vocabulariesFor search engines, agents, curators, authors and users But everyone must mean the same thing…

OntologiesShared and common understanding of a domainEssential for search, exchange and discovery

Ontologies aim at sharing meaning


Online Shopper’s Problem

El Cheapo: “Where can I get the cheapest copy (including shipping cost) of Wittgenstein’s Tractatus Logicus-Philosophicus within a week?”

?Information Integration

addall.com

““OneOne--WorldWorld””MediationMediation

amazon.comamazon.com A1books.comA1books.comhalf.comhalf.combarnes&noble.combarnes&noble.com


XML-Based Mediator

MEDIATORMEDIATOR

(XML) Queries & Results

S1

Wrapper

(XML) View

S2

Wrapper

(XML) View

Sk

Wrapper

(XML) View

Integrated Global(XML) View G

Integrated ViewDefinition

G(..)← S1(..)…Sk(..)

USER/ClientUSER/ClientQuery Q ( G (SQuery Q ( G (S11,..., ,..., SSkk) )) )

wrappers implementedas web services


DB PerspectiveInformation Integration Problem

Given: data sources S1, ..., Sk (DBMS, web sites, ...) and user questions Q1,..., Qn that can be answered using the Si

Find: the answers to Q1, ..., Qn

The Database Perspective: source = “database”⇒ Si has a schema (relational, XML, OO, ...) ⇒ Si can be queried⇒ define virtual (or materialized) integrated views V over S1 ,..., Sk

using database query languages (SQL, XQuery,...)⇒ questions become queries Qi against V(S1,..., Sk)


Why develop an ontology?To make domain assumptions explicit

Easier to change domain assumptionsEasier to understand, update, and integrate legacy datadata integration

To separate domain knowledge from operational knowledgeRe-use domain and operational knowledge separately

A community reference for applicationsTo share a consistent understanding of what information means.


OntologyPhilosophy

Theory of what exists in the world

LogicTheory of what exists in the domain

Computer ScienceFormal description of shared concepts in a domainA vocabulary for the domain knowledge (AI)


Glossary(1)wordreference.com

ontology noun1 (Philosophy) the branch of metaphysics that deals with the nature of being2 (Logic) the set of entities presupposed by a theory

taxonomy noun1 a the branch of biology concerned with the classification of organisms into groups based on similarities of structure, origin, etc.b the practice of arranging organisms in this way2 the science or practice of classification [ETYMOLOGY: 19th Century: from French taxonomie, from Greek taxis order + -nomy]

thesaurus noun(plural: -ruses, -ri [-raı])1 a book containing systematized lists of synonyms and related words2 a dictionary of selected words or topics3 (rare) a treasury[ETYMOLOGY: 18th Century: from Latin, Greek: treasure]


Glossary(2)concept noun1 an idea, esp. an abstract idea; example: the concepts of biology2 (Philosophy) a general idea or notion that corresponds to some class of entities and that consists of the characteristic or essential features of the class3 (Philosophy) a the conjunction of all the characteristic features of something b a theoretical construct within some theory c a directly intuited object of thought d the meaning of a predicate4 [modifier] (of a product, esp. a car) created as an exercise to demonstrate the technical skills and imagination of the designers, and not intended for mass production or sale[ETYMOLOGY: 16th Century: from Latin conceptum something received or conceived, from concipere to take in, conceive]

glossary noun (plural: -ries); an alphabetical list of terms peculiar to a field of knowledge with definitions or explanations. Sometimes called: gloss[ETYMOLOGY: 14th Century: from Late Latin glossarium; see gloss2]


Origin and HistoryOntology ....

a philosophical discipline, branch of philosophy that deals with the nature and the organisation of reality

Science of Being (Aristotle, Metaphysics, IV, 1)Tries to answer the questions:

What is being?What are the features common to all beings?


PhilosophyOntology as a philosophical discipline, which deals with the nature and the organization of reality:

Ontology as such is usually contrasted with Epistemology, which deals with the nature and sources of our knowledge [a.k.a. Theory of Knowledge].Aristotle defined ontology as the science of being as such: unlike the special sciences, each of which investigates a class of beings and their determinations, ontology regards all the species of being qua being and the attributes which belong to it qua being" (Aristotle, Metaphysics, IV, 1).

In this sense Ontology tries to answer to the question: What is being?


Logic, the existential quantifier is a notation for

asserting that something exists. But logic itself has no vacablary for describing the things that exists. “to be is to be the value of a quantified variable.”(Quine 1992)

∃


Semantics• Humans require words (or at least symbols) to communicate efficiently. The mapping

of words to things is indirect. We do it by creating concepts that refer to things.• The relation between symbols and things has been described in the form of the

meaning triangle:

“Jaguar“

Concept

Ogden, C. K. & Richards, I. A. 1923. "The Meaning of Meaning." 8th Ed. New York, Harcourt, Brace & World, Inc


Human and machine communication• ... Machine

Agent 1

Things

HumanAgent 2

Ontology Description

MachineAgent 2

exchange symbol,e.g. via nat. language

‘‘JAGUAR“

Internalmodels

Concept

Formalmodels

exchange symbol,e.g. via protocols

MA1HA1 HA2 MA2

Symbol

commit commit

a specific domain, e.g.animals

commit commitOntology

Formal Semantics

HumanAgent 1

MeaningTriangle

[Maedche et al., 2002]


Computer ScienceAn ontology is ...

an explicit specification of a conceptualization [Gruber93]a shared understanding of some domain of interest [Uschold, Gruninger96]

Some aspects and parameters:a formal specification (reasoning and “execution”)... of a conceptualization of a domain (community)... of some part of world that is of interest (application)

Provides:A common vocabulary of termsSome specification of the meaning of the terms (semantics)A shared understanding for people and machines


Artificial IntelligenceKR (Knowledge Representation)

Representation languageKnowledge baseReasoning

Various Applications


Some different uses of the word “Ontology”1. Ontology as a philosophical discipline2. Ontology as a an informal conceptual system3. Ontology as a formal semantic account4. Ontology as a specification of a “conceptualization”5. Ontology as a representation of a conceptual systemvia a logical theory

5.1 characterized by specific formal properties5.2 characterized only by its specific purposes

6. Ontology as the vocabulary used by a logical theory7. Ontology as a (meta-level) specification of a logical theory[Guarino’95] http://ontology.ip.rm.cnr.it/Papers/KBKS95.pdf


DefinitionAn ontology is an explicit specification of a

conceptualization A conceptualization is an abstract,

simplified view of the world that we want to represent If the specification medium is a formal

language, the ontology defines a representational foundation


What is a Conceptualization?

Conceptualization [Genesereth, Nilsson]: universe of discourse (domain) D = {a,b,c,d,e}relations = {on/2, above/2, clear/1, table/1} (functions & predicates)

Compare (A) and (B): world_A: {on(a,b), on(b,c), on(d,e), table(c), table(e)}world_B: {on(a,b), on(c,d), on(d,e), table(b), table(e)}two different conceptualizations? or rather two different states of the same conceptualization?

(A)(A) (B)(B)

Meaning is NOTcaptured by

extensional relations / a single state ofaffairs


Ontologies vs ConceptualizationsGiven a logical language L ...

... a conceptualization is a set of models of L which describes the admittable(intended) interpretations of its non-logical symbols (the vocabulary)... an ontology is a (possibly incomplete) axiomatization of a conceptualization.

conceptualization conceptualization C(L)C(L)

ontologyontology

set of all models M(L)set of all models M(L)logiclogictheoriestheories

http://www-ksl.stanford.edu/KR96/Guarino-What/P003.html


Definition [Sowa]The subject of ontology is the study of the categories of things that exist or may exist in some domain. The product of such a study, called an ontology, is a catalog of the types of things that are assumed to exist in a domain of interest D from the perspective of a person who uses a language L for the purpose of talking about D. The types in the ontology represent the predicates, word senses, or concept and relation types of the language L when used to discuss topics in the domain D. An uninterpreted logic, such as predicate calculus, conceptual graphs, or KIF, is ontologically neutral. It imposes no constraints on the subject matter or the way the subject may be characterized. By itself, logic says nothing about anything, but the combination of logic with an ontology provides a language that can express relationships about the entities in the domain of interest.

http://www.bestweb.net/~sowa/ontology/


Ontology (cont.)Typically, an ontology contains:

Classes – the objects of a domaineach class is characterized by properties shared by all elements in that classRelations – relations between classes or between the elements of the classesHierarchies - organizes these classes in a subclass hierarchy


Formal OntologyTheory of formal distinctions

among thingsamong relations

Basic toolsTheory of parthood

What counts as a part of a given entity? What properties does the partrelation have? Are the different kinds of parts?

Theory of integrityWhat counts as a whole? In which sense are its parts connected?

Theory of identityHow can an entity change while keeping its identity? What are its essential properties? Under which conditions does an entity loose its identity? Does a change of “point of view” change the identity conditions?

Theory of dependenceCan a given entity exist alone, or does it depend on other entities?


An explicit description of a domain 1Concepts (class, set, type, predicate)

event, gene, gammaBurst, atrium, molecule, cat

Properties of concepts and relationships between them (slot)

Taxonomy: generalization ordering among concepts isA, partOf, subProcessRelationship, Role or Attribute: functionOf, hasActivity location, eats, size

animal

rodent cowcat

mouse

eats

dog

domesticvermin


ConceptsPrimitive concepts:

properties are necessaryGlobular protein must have hydrophobic core (but a protein with a hydrophobic core need not be a globular protein)

Defined concepts: properties are necessary + sufficientEukaryotic cells must have a nucleus. Every cell that contains a nucleus must be Eukaryotic.

[Robert Stevens][Robert Stevens]


What is a concept?Different communities have different notions on what a concept means:

Formal concept analysis (see http://www.math.tu-dresden.de/~ganter/fba.html) talk about formal conceptsDescription Logics (see http://dl.kr.org/): They talk about concept labelsISO-704:2000 – Terminology Work: (see http://www.iso.ch/)Often the classical notion of a frame in AI or a class in OO modeling is seen as equivalent to a concept.


Formal Concept Analysis (FCA)

Concept Lattice

Formal Concept AnalysisFormal Concept Analysis

[Sowa, http://users.bestweb.net/~sowa/misc/mathw.htm]


An explicit description of a domain 2Constraints or axioms on properties and concepts:

value: integer domain: catcardinality: at most 1range: 0 <= X <= 100oligonucleiotides < 20 base pairscows are larger than dogscats cannot eat only vegetationcats and dogs are disjoint

Values or concrete domainsinteger, strings20, trypotoplan-synthetase

animal

rodent cowcat

mouse

eats

dog

domesticvermin

[Carole Goble, Nigel [Carole Goble, Nigel ShadboltShadbolt, , OntologiesOntologies and the Grid Tutorial]and the Grid Tutorial]


An explicit description of a domain 3Individuals or Instances

sulphur, trpA Gene, felixNominals

Concepts that cannot have instancesInstances that are used in conceptual definitionsItalianDog = Dog bornIn Italy

InstancesAn ontology = concepts+properties+axioms+values+nominalsA knowledge base = ontology+instances

animal

rodent cowcat

mouse

eats

dog

domesticvermin

mickey

felix

jerry

tom



Light and Heavy expressivity

LightweightConcepts, atomic typesIs-a hierarchyRelationships between concepts

HeavyweightMetaclassesType constraints on relationsCardinality constraintsTaxonomy of relationsReified statementsAxiomsSemantic entailmentsExpressivenessInference systems

A matter of rigour and representational expressivity



A semantic continuum

Shared human consensus

Text descriptions

Semantics hardwired; used at runtime

Semantics processed and used at runtime

Pump: “a device for moving a gas or liquid from one place or container to another”

(pump has (superclasses (…))

Implicit Informal(explicit)

Formal(for humans)

Formal(for machines)

Further to the right means: •Less ambiguity•More likely to have correct functionality•Better inter-operation (hopefully)

•Less hardwiring•More robust to change•More difficult


Ontology & KRThe Knowledge-level: a level of description of the knowledge of an agent that is independent of internal format.

An agent “knows” if it acts like it does. A software agent “acts” by telling and asking

An agent commits (conforms) to an ontology if it “acts” consistently with the definitions

Ontological Commitments are agreements to use the vocabulary in a coherent and consistent manner. Common ontology ≠common knowledge.


Ontologies vs KBAn ontology is a particular KB (knowledge base), describing facts assumed to be always true by a community of users:

in virtue of the agreed-upon meaning of the vocabulary used (analytical knowledge).... whose truth does not descend from the meaning of the vocabulary used (non-analytical, common knowledge).

An arbitrary KB may describe facts which are contingently true, and relevant to a particular epistemic state.


Ontologies vs KB contd.There is no clear separation between ontology and knowledge base

Example:

Often it remains a modeling decision if something is modeled as concept or as instance. In many applications meta-modeling means are required.

person

Ann

medication Aspirin

Aspirin pill-1 pill-2 cured-with

taken-aspirins

taken-aspirins


Ontology & Natural LanguageThere is a m:n relationship between words and concepts.This means practically:

different words may refer to the same concepta word may refer to several concepts

Ontologies languages should provide means for making this difference explicit.


ExampleOntology: C = {c1,c2, c3}, R = {r1}, HC(c2,c1), r1(c2,c3),Lexicon: LC = {person, employee, organisation}, LR = {works at}F(person) = c1, F(employee) = c2, F(organisation) = c3, G(works at) = r1

c3

c1

...

c2

..

....r1(c2,c3),

HC(c2,c1)person

employee

organisation

works at


Ontology vs. DB SchemaAn ontology provides an explicit conceptualisation that describe the semantics of the data. They have a similar function as a database schema. The differences are:

A language for defining ontologiesis syntactically and semantically richer than common approaches for databases. The information that is described by an ontology consists of semi-structured natural language texts and not tabular information. An ontology must be a shared and consensual terminology because it is used for information sharing and exchange. An ontology provides a domain theory and not the structure of adata container.


What Isn’t an ontologyA database or program

because they share internal formats

a conceptualization because it isn’t a specification -it’s a vision

a table of contents but wait, isn’t a Taxonomy an Ontology? only if it defines a set of concepts


The World Ontology


Ontologies: FormalFormal => (partially) Computable Semantics

EngMath-basis for mathematical modeling of physical systems

physical quantities, units, dimensions Frame Ontology-unifying theory for frame-based

representation systems classes, relations, slots

Configuration Design-for representing a design task components, subparts, attributes, constraints


Ontologies: SemiformalSemiformal => useful computations on formal part

Reference Dictionaries and Thesauri -domain terms anduntypedrelations among them Ontology.org-XML based industry standards for e-

commerce data exchange product, price, …

CIDOC CRM -conceptual reference model for cultural heritage data

place, time span, appellation, right


Ontologies: InformalInformal => human interpretation aided by

computation (Non-semantic) Web Ontology -for identifying

and linking information objects Thing-with-URI, Link

Intraspect’s Context Ontology-for capturing and sharing information in its context of use by knowledge workers

parent/child, document, message, comment


Ontologies - Some ExamplesGeneral purpose ontologies:

WordNet / EuroWordNet, http://www.cogsci.princeton.edu/~wnThe Upper Cyc Ontology, http://www.cyc.com/cyc-2-1/index.htmlIEEE Standard Upper Ontology, http://suo.ieee.org/

Domain and application-specific ontologies:RDF Site Summary RSS, http://groups.yahoo.com/group/rss-dev/files/schema.rdfUMLS, http://www.nlm.nih.gov/research/umls/KA2 / Science Ontology, http://ontobroker.semanticweb.org/ontos/ka2.htmlRETSINA Calendering Agent, http://ilrt.org/discovery/2001/06/schemas/ical-full/hybrid.rdfAIFB Web Page Ontology, http://ontobroker.semanticweb.org/ontos/aifb.htmlWeb-KB Ontology, http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/theo-11/www/wwkb/Dublin Core, http://dublincore.org/

Meta-OntologiesSemantic Translation, http://www.ecimf.org/contrib/onto/ST/index.htmlRDFT, http://www.cs.vu.nl/~borys/RDFT/0.27/RDFT.rdfsEvolution Ontology, http://kaon.semanticweb.org/examples/Evolution.rdfs

Ontologies in a wider senseAgrovoc, http://www.fao.org/agrovoc/Art and Architecture, http://www.getty.edu/research/tools/vocabulary/aat/UNSPSC, http://eccma.org/unspsc/DTD standardizations, e.g. HR-XML, http://www.hr-xml.org/


CriteriaUse of ontologies

Purpose of using ontologiesArchitecture of ontologies used

Ontology representationKind of languages used to represent ontologiesGeneral structure of ontologies

Use of mappingsHow information is mapped to ontologiesInter-ontology mapping

Ontology engineeringSupport for development of ontolgiesSupport for evolution of ontologiesSupporting tools


GEON Architecture

Rocky Mountains

Midatlantic Region


SMART (Meta)data I: Logical Data Views

Source: NADAM Team(Boyan Brodaric et al.)

Adoption of a standard (meta)data model => wrap data sets into unified virtual views


SMART Metadata II: Multihierarchical Rock Classification for “Thematic Queries” (GSC) –– or: Taxonomies are not only for biologists ...

Composition

Genesis

Fabric

Texture

“smart discovery & querying” via multiple, independent concept hierarchies (controlled vocabularies)• data at different description levels can be found and processed


Biomedical InformaticsResearch Networkhttp://nbirn.net

Biomedical InformaticsResearch Networkhttp://nbirn.net

SMART Metadata III: Source Contextualization & Ontology Refinement

Focused GEON ontology working meeting last week ... (GEON, SCEC/KR, GSC, ESRI)


EcoCyc


Gene Ontology http://www.geneontology.org“a dynamic controlled vocabulary that can be applied to all eukaryotes”Built by the community for the community.Three organising principles:

Molecular function, Biological process, Cellular component

Isa and Part of taxonomy – but not good!~10,000 conceptsLightweight ontology, Poor semantic rigour. Ok when small and used for annotation. Obstacle when large, evolving and used for mining.


Controlled Vocabulary

AGROVOC: Agricultural Vocabulary


Types of OntologySingle ontology Multiple ontology Hybrid ontology


Types of Ontologies (contd.) [Guarino, 98]

describe very general concepts like space, time, event, which are independent of a particular problem or domain. It seems reasonable to have unified top-level ontologies for large

communities of users.

describe the vocabulary related to a generic domain by

specializing the concepts introduced

in the top-level ontology.

describe the vocabulary related to a generic task

or activity by specializing the

top-level ontologies.

These are the most specific ontologies. Concepts in application ontologies often correspond to roles played by domain entities while performing a certain activity.


Types of Ontologies (contd.)Domain ontologies –capture the knowledge valid for a particular type of domain (e.g. Electronic, medical, mechanic, Core. Generic or common sense ontologies –aim at capturing

general knowledge about the world, providing basic notions and concepts for things like time, space, state, event, etc. Representational ontologies –provide representational

entities but not state what to be represented, such as Frame Ontology defining frames, slots, and slot constraints, allowing the expression of knowledge. Method and Task ontologies –provide a state-transition and

reasoning poit of view on domain knowledge.


Single OntologySIMSOne global ontologyHierarchical terminological databaseCombination of several specialized ontolgies(for modularization)Can be used when all information sources to be integrated provide nearly the same view on a domainMinimal ontology commitmentSusceptible to changes in the information sources


Multiple OntologiesOBSERVEREach information source is described by its own ontology (source ontology)No shared vocabulary No common and minimal ontology commitment is neededSimplifies integration and supports changes in sourcesDifficult to compare different source ontologiesInter-ontology mapping is needed


Hybrid OntologiesCOINSemantics of each source is described by its own ontologyBuilt from a a global shared vocabularyShared vocabulary contains basic terms of a domainNew sources can easily be addedSupports acquisition and evolution of ontologiesSource ontologies are comparable because of shared vocabularyExisting ontologies can not easily be reused, but have to be redeveloped from scratch


Ontologies and their Relatives (1)There are many relatives around:

Controlled vocabularies, thesauri and classification systems available in the WWW, see http://www.lub.lu.se/metadata/subject-help.html

Classification Systems (e.g. UNSPSC, Library Science, etc.)Thesauri (e.g. Art & Architecture, Agrovoc, etc.)

Lexical Semantic NetsWordNet, see http://www.cogsci.princeton.edu/~wn/EuroWordNet, see http://www.hum.uva.nl/~ewn/

Topic Maps, http://www.topicmaps.org (e.g. used within knowledge management applications)

In general it is difficult to find the border line!


Ontologies and their Relatives (2)

Catalog / ID

Terms/Glossary

Thesauri

InformalIs-a

FormalIs-a

FormalInstance

Frames

ValueRestric-tions

Generallogical

constraints

AxiomsDisjointInverse Relations,...


Ontology Query ModelIntegrated global viewGlobal query schemaUser formulates query in terms of the ontologySystem reformulates queries in terms of sub-queries for each sourceStructure of the query model should be more intuitive for the user


Ontology Verificationmappings from a global schema to the local source schemaAutomatic verificationQuery containment

Ontology concepts corresponding to the local sub-queries are contained in the ontology concepts related to the global query

67


Ontology Representations

In-/Semi-formal ParadigmsFormalisms for Ontology


Ontologies

TopicMaps

extended ER-Modell

Thesauri

Formal Approaches:Predicate Logics /Frame LogicDescription Logics/Annotated Logic

Semantic Nets

Taxonomies

Representation Paradigms


Ontology RepresentationWhat is a „representation“?

“Jaguar“

Concept


Ontology Representation Languages

Machines need communication with formal content to restrict meaningWhat makes a language „formal“?

model theory (first order logic)proof theory (Gentzen calculus)

But also:conventions (e.g. Java)


What makes a language suitable?For machine communication

✣ model theory ✣✣ proof theory✣ tracktability✤ strong conventions of use✤ human readable names ✤

For human communication

✣ strong conventions of use ✣✣ human readable names ✣✣ “natural“ primitives ✣


Thesauri

AAT: Art & Architecture Thesaurus


Apfelsine (german)Example:

Fruit

Orange

VegetablesimilarTo

synonymWith

NarrowerTerm

- Well known in library science- cf. terminologies / classifications (Dewey)

- Graph with labels edges (similar, nt, bt, synonym)- Fixed set of edge labels (aka relations)- no instances

Thesauri


Topic MapsStandardized: ISO/IEC 13250:2000

ISO standard published Jan. 2000enabling standard to describe knowledge structures,electronic indices, classification schemes, ...

Web enabled:XML Topic Maps (XTM) are ready to use

Designed to:manage the info glutbuild valuable information networks above any kind of resources / data objectsenable the structuring of unstructured information


Back-of-the-Book Index “British Virgin Islands”

Gorda Sound see North SoundLittle Dix Bay .................... 89North Sound ....................... 90Road Harbour see also Road Town ... 73Road Town ...................... 69,71Spanish Town ................... 81,82Tortola ........................... 67Virgin Gorda ...................... 77


Index “British Virgin Islands”


Topics




Occurrences




Different topic classes




Different occurrences classes




Multiple topic names




Association


Topics – Computerized Subjects

SurfBVIBVI Welcome CaribNet

Resources

TopicsLittle Dix Bay Tortola Road Town

Virgin GordaSubject

Subject

Subject Subject

North Sound

Subject

Road Harbour

Subject

Spanish Town

Subject

Bay Island Town Topic classes



Occurrences

Resources


Virgin Gorda

North Sound

Road Harbour

Spanish Town

Occurrences

OccurrenceclassesImage

Map

Article

MapMap

Map

MapArticle

Article

Article

ArticleArticle

Article

Image Image

Image

Image


Occurrences

Resources


Virgin Gorda

North Sound

Road Harbour

Spanish Town

Occurrences


OccurrenceclassesImage

Map

Article


Associations

Topics

Little Dix Bay Tortola Road TownVirgin Gorda

North Sound

Road Harbour

Spanish Town

Associations

Associationclasses

Vicinity

Part-Whole

Part-Whole

Geo Containment

Geo Containment

Geo Containment

Geo ContainmentVicinityPart-Whole


Associations

Topics

Little Dix Bay Tortola Road TownVirgin Gorda

North Sound

Road Harbour

Spanish Town

Associations

Associationclasses

Geo ContainmentVicinityPart-Whole


Class Hierarchies


Virgin Gorda

North Sound

Road Harbour

Spanish Town

Bay Island Town Topic classes


Class Hierarchies


Virgin Gorda

North Sound

Road Harbour

Spanish Town

Bay

Island

Town Super-classes

Bay forswimming

Anchorbay

Land

CapitalSuburb Sub-classes


Scopes

Brit. Virgin IslandsBrit. Jungferninseln

CaribbeanKaribik

Great BritainGroßbritannien

ImageBild

MapKarte

ArticleArtikel


Geo ContainmentGeo Umschließung

Political DependencyPolitische Abhängigkeit


Scopes


CaribbeanKaribik


ImageBild

MapKarte

ArticleArtikel




Scopes


Scopes


CaribbeanKaribik




ImageBild

MapKarte

ArticleArtikel


Names:English

Deutsch

Scopes


Scopes


CaribbeanKaribik




ImageBild

MapKarte

ArticleArtikel


Names:English

Deutsch

Scopes

Occurrences:Public

Confidential


Scopes


CaribbeanKaribik




ImageBild

MapKarte

ArticleArtikel


Names:English

Deutsch

Scopes

Occurrences:Public

Confidential

Associations:Geography

Politics


Scope Examples: English, Public, Politics

Brit. Virgin IslandsCaribbean

Geo Containment

Great Britain

Political Dependency

Image

Map

Article


Names:English

Deutsch

Scopes

Occurrences:Public

Confidential

Associations:Geography

Politics


In-/Semi-formal Approaches: Topic Maps, ThesauriAdvantages

Capture a lot of modeling experiences

Intuitive

Interesting primitives that are not available in other approaches (TM)

Disadvantages

No characterization independent from particular implementation

May be misinterpreted (TM) / few primitives (Thesauri)


Common errors about ontology representation languagesAI people‘s errors

„it is good if it is formal“

„it is good if someone with a logic background may easily use it“

„it is good if the language allows everything“

Engineer‘s errors

„it works in my application, thus it is good“„who needs formality anyway?“

„it did not work when I looked at it 10 years ago“


Description Logic

DL definition of “Happy Father”(Example from Ian Horrocks, U Manchester, UK)


DL Statements as Rules

Another syntax: first-order logic in rule form:happyFather(X)

man(X), child(X,C1), child(X,C2), blue(C1), green(C2),not ( child(X,C3), poorunhappyChild(C3) ).

poorunhappyChild(C) not rich(C), not happy(C).

Note:the direction “ ” is implicit here (*sigh*)see, e.g., Clark’s completion in Logic Programming


Description LogicsTerminological Knowledge (TBox)

Concept Definition (naming of concepts):

Axiom (constraining of concepts):

=> a mediators “glue knowledge source”

Assertional Knowledge (ABox)the marked neuron in image 27

=> the concrete instances/individuals of the concepts/classes that your sources export


Querying vs. ReasoningQuerying:

given a DB instance I (= logic interpretation), evaluate a query expression (e.g. SQL, FO formula, Prolog program, ...)boolean query: check if I |= ϕ (i.e., if I is a model of ϕ) (ternary) query: { (X, Y, Z) | I |= ϕ (X,Y,Z) }

=> check happyFathers in a given databaseReasoning:

check if I |= ϕ implies I |= ψ for all databases I, i.e., if ϕ => ψundecidable for FO, F-logic, etc.Descriptions Logics are decidable fragments

⇒ concept subsumption, concept hierarchy, classification⇒ semantic tableaux, resolution, specialized algorithms


Formalizing Glue Knowledge:Domain Map for SYNAPSE and NCMIR Domain Map

= labeled graph with concepts ("classes") and roles ("associations")• additional semantics: expressed as logic rules

Domain Map= labeled graph with concepts ("classes") and roles ("associations")• additional semantics: expressed as logic rules

Domain Map (DM)

Purkinje cells and Pyramidal cells have dendritesthat have higher-order branches that contain spines.Dendritic spines are ion (calcium) regulating components.Spines have ion binding proteins. Neurotransmissioninvolves ionic activity (release). Ion-binding proteinscontrol ion activity (propagation) in a cell. Ion-regulatingcomponents of cells affect ionic activity (release).

Domain Expert Knowledge

DM in Description Logic


Source Contextualization & DM RefinementIn addition to registering(“hanging off”) data relative to

existing concepts, a source may also refine the mediator’s domain map...

⇒ sources can register new concepts at the mediator ...


DLDescription Logics – Formal semantics & reasoningCLASSIC, GRAIL, LOOM, OILDescribe knowledge in terms of concepts and rolerestrictionsDerive classification hierarchies automatically from concepts and role restrictions Decidability and completeness – guarantee that reasoning algorithm always terminate with correct answersReasoning tasks – satisfiability, subsumption (is-a), instance checking, classification


Frame-based System

Frame-based systemsOKBC, Ontolingua, F-Logic

Frame is a structure for representing a concept or situationFrames are composed of slots (attributes) for which fillers (values) have to be specifiedProperties and restrictions can be provided for fillers

DLs are descendants of frame-based systemsClasses (objects/concepts), roles (attributes/properties)


FrameFrames

Classes: Genes, Reactions Instances:

Relationships Slots: Chromosome, map-position, citations, reactants, products Facets: Chromosome is single-valued, instance of class Chromosomes; Citations is multiple valued, set of strings.


Frame/OOAdvantages

Intuitive and popular modelling style. Many tools and examples. OKBC standard for semantics. Some reasoning.

Disadvantages Extending/evolving problematic

Hand crafting taxonomies and asserted properties. Static classifications. Pre-enumerate concepts. Little reasoning support Difficult to build large coherent and complete ontologies(e.g. multiple


Other ApproachesFormal concept analysis

Based on the calculation of a common concept hierarchy for different information sources

– limited expressivenessObject Languages

designed for specific needsused in geographic domainprovides solution for integration of spatial and thematic information

Annotated Logicsused to resolve conflictseg. – KAMEL

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Ontology Reasoning


Role of ReasoningClass membership

x instance of C, C subclass of D, therefore x instance of DEquivalence of classes

A equivalent to be, B equivalent to C, therefore A equivalent toCConsistency

Uncovers errors in the ontology and its instantiationClassification

P a sufficient condition for C, x satisfies P, therefore x is aninstance of C


Role of ReasoningBenefits of reasoning:

Useful for query answeringAs a design support tool

For large ontologiesWith multiple authors

For integrating and sharing ontologies

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Language


Web Ontology LanguagesRequirements for WOL

Well-defined syntaxEfficient reasoning supportSufficient expressive powerConvenience of expression

All are important, but there is tradeoff between:Efficient reasoning supportSufficient expressive power


Semantic Web LanguagesMain ontology languages for the Semantic

Web:RDFSDAML+OILOWL


RDFSThe essence of RDF Schema:

Classes and binary relations (properties)Subclass and subproperty relationsDomain and range restrictionsInstance declarations


Features of RDFSProperty-centric:Each property specifies what classes of subjects and objects it relates. New properties can be added to a class without modifying the class

resource, class, subClassOf, type property, subPropertyOfdomain, range, constraintResource, constraintProperty

Definitions can include constraints which express validation conditions

domain constraints link properties with classes range constraints limit property values

BUT expressive inadequacy and poorly defined semantics


Missing Features of RDFSLocal scope of propertiesCows eat only plants, while other animals eat meat, tooDisjointness of classesMales and females are disjoint classesBoolean combinations of classesPersons are union of males and femalesCardinality constraintsAn offered course is taught by at least one lecturerpecial characteristics of propertiesTransitivity, uniqueness, inverse of another property


Rules for SWNext step in the development of the Semantic Web.Why?

Provides expressivity not offered by OWLRules are orthogonal to description logicsRule technology is more mature than descriptionlogicsNon-experts are more familiar with rulesRules integrate well into the commercial

mainstream software engineering, e.g. OO and DB


Rules for SWWhere do rules fit in the Semantic Web design?

Rules within Web ontology languagesRules “on top” of ontology languages

Rules on top of ontologiesOntological knowledge used in rules (bodies)No new ontological knowledge is derived using

rules


Rules for SWRules in Web ontology languages

Full combination of rules and description logics leads to computability and intractability problemsCurrently: common subset of DL and rulesAn alternative: RDFS +Rules


Rule Standardization for SWRuleML Initiative (2000 -)

Dozens of institutions, especially in EU and USAMission: Enable semantic exchange of rules and facts between most commercially important rule systemsFirst standards specifications ready, quite stable

Current state: Datalog. Planned extensions:Logically more expressive: full Horn logic programsReactive rulesNonmonotonic rules


DAML-SAn upper ontology for service

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Ontological Engineering


Ontology EngineeringDevelopment methodology

1) Identify a purpose and scope2) Building the ontology

1) Ontology capture – knowledge acquistion2) Ontology coding – developing a structured concept model

3) Integrating existing ontologies4) Evaluation – verification and validation5) Guidelines for each phase


Building OntologiesNo field of Ontological Engineering equivalent to Knowledge or Software Engineering;No standard methodologies for building ontologies;Such a methodology would include:

a set of stages that occur when building ontologies; guidelines and principles to assist in the different stages; an ontology life-cycle which indicates the relationships among stages.

Gruber's guidelines for constructing ontologies are well known.


The Development LifecycleTwo kinds of complementary methodologies emerged:

Stage-based, e.g. TOVE [Uschold96] Iterative evolving prototypes, e.g. MethOntology [Gomez Perez94].

Most have TWO stages:Informal stage

ontology is sketched out using either natural language descriptions or some diagram technique

Formal stage ontology is encoded in a formal knowledge representation language, that is machine computable

An ontology should ideally be communicated to people and unambiguously interpreted by software

the informal representation helps the former the formal representation helps the latter.


A Provisional MethodologyA skeletal methodology and life-cycle for building ontologies;Inspired by the software engineering V-process model;

The overall process moves through a life-cycle.

The left side charts the processes in building an ontology

The right side charts the guidelines, principles and evaluation used to ‘quality assure’the ontology


Methodology

Conceptualisation

Integrating existing ontologies

Encoding

Representation

Identify purpose and scope

Knowledge acquisition

Evaluation: coverage, verification, granularity

Conceptualisation Principles: commitment, conciseness, clarity, extensibility, coherency

Encoding/Representation principles: encoding bias, consistency, house styles and standards, reasoning system exploitation

Ontology in Use

User Model

Conceptualisation Model

Implementation Model

Ontology Learning

Maintenance


An Ontology Building Life-cycleIdentify purpose and scope

Knowledge acquisition

Evaluation

Language and representation

Available development tools

Conceptualisation

Integrating existing ontologiesEncoding

Building

Ontology Learning

Consistency Checking


QuestionsHow do we obtain our conceptualisation?The role of texts, experts and other sourcesHow do we derive conceptualisation from texts etcHow do we cope with tacit conceptualisations?How do we use models with the expert?How do we validate the conceptualisation?


Knowledge AcquisitionThe process of capturing knowledge including various forms of conceptualisation from whatever source including experts, documents, manuals, case studies etc.Knowledge Elicitation

techniques that are used to acquire knowledge direct from human experts

Machine Learninguse of AI pattern recognition methods to infer patterns from sets of examples


InfosleuthInfosleuth

Semi-automatically constructs ontologies from textual databasesExperts provide seed words to represent high-level concepts Processes the incoming documents extracting phrases that involve seed wordsGenerates corresponding concept terms and then classifies them into ontologiesNeeds experts for evaluation process (Phase -3 )Does not mention integration of existing ontologies


SIMSSIMS

An independent model of each info source must be described for this systemDomain model – defined to describe objects and actionsIncludes a hierarchical terminological knowledge base Indications of all relationships between the nodesScalability and maintenance issues addressedGraphical knowledge base builder can be used


KRAFTKRAFT

Shared Ontologies1) Ontology scoping 2) Domain analysis3) Ontology fomralization4) Top level ontology

Extracting OntologiesBottom-up approach to extract an ontology from existing shared ontolgies1) Syntactic translation from the KRAFT exportable view of the resource into

the KRAFT-schema2) Ontological upgrade – semi-automatic translation plus knowledge-based

enhancement – local ontology adds knowledge and further relationships between the entities in the translated schema

Lack evaluation of the ontologies


Supporting ToolsOntoEdit

Enables inspecting, browsing, codifying and modifying ontologiesSupport ontology development and maintenance

SHOEs knowledge annotatorCommits each web page to one or more ontologiesCan define categories, relations and other components in an ontologyProvides integrity checksExpose – to parse annotated web pagesParka - knowledge base


Supporting Tools (cont.)DWQ – i.com

Supporting tool for the conceptual design phaseUses extended entity relationship conceptual data modelEnriches it with aggregations and inter-schema constraintsServes mainly for intelligent conceptual modeling


Ontology EvolutionSupport for adding and/or removing sourcesMust be robust to changes in the information sourceSHOE – only system that supports ontology evolution using Expose


UML and DAML: ProjectsProjects investigating UML DAML issues:

UML-based Ontology Toolset (UBOT)UML/XMI tools for DAML ontology development, webpage annotation and formal consistency checkinghttp://ubot.lockheedmartin.com/

Components for Ontology Driven Push (CODIP)DAML ontology development and fusion with Rose 2000dissemination of DAML messages via channels http://grcinet.grci.com/maria/www/CodipSite/codip.html

Stanford Database GroupOntoAgents - http://WWW-DB.Stanford.EDU/OntoAgents/Layered Approach to Information Modeling and Interoperability

http://www.interdataworking.com/converter/Representing UML in RDF

http://www-db.stanford.edu/~melnik/rdf/uml/


UML and DAML: Process Example

Baseline ontology for business



Extended ontology for e-commerce



Translation to DAML



Graphical annotation:"Trey Clever is the CEO of the B2B company acronym.com".


UML and DAML: MappingA UML profile:

is a predefined set of stereotypes, taggedvalues, constraints and iconstailors UML for a specific domain or process

Draft UML profile for DAML:primary goal - translate class diagrams into DAML ontologies

assumption: purpose of model is to design a DAML ontology secondary goal - translate DAML ontologies into class diagramsfuture goal - translate legacy class diagrams into DAMLapproach - use UML extensions sparingly status - work in progress

not all DAML concepts mapped to UMLfor more information see:

http://ubot.lockheedmartin.com/ubot/details/uml_to_daml.htmlhttp://grcinet.grci.com/maria/www/CodipSite/DAML_UML/index.htm


UML and DAML: OntologiesThis model shows dependencies between ontologies.


UML and DAML: Domain and Range

Using explicit navigability and rolenamesDAML property: age

domain: Personrange: Integer

DAML property: mother-parentdomain: Personrange: Woman


UML and DAML: Domain and Range

Using name direction arrow and no rolenamesDAML property: age

domain: Personrange: Integer

DAML property: motherdomain: Personrange: Woman


UML and DAML: subPropertyOfFor simple hierarchy model subPropertyOf as:

stereotyped dependency between 2 associations


UML and DAML: subPropertyOfFor complex hierarchy model subPropertyOf as:

generalization between stereotyped classes


UML and DAML: Cardinality

cardinality maxcardinalitymincardinality

UniqueProperty UnambiguousProperty


UML and DAML: RemarksGraphical modeling of DAML ontologies in UML appears to be a promising approach.Differences in the design goals and origins of UML and DAML complicate the mapping.We recommend that OMG initiate standards efforts related to DAML when the DAML definition stabilizes (~mid 2001):

standard UML profile for DAML consistent with UML profile for agents

each Domain Task Force should develop standard DAML ontologies

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Ontology Application


Mappings Connecting to Information SourcesRelate the ontologies to the actual content of an

information sourceApproachesStructure resemblance

Produce a one-to-one copy of the structure of the database and encode it in a language that makes automated reasoning possible

Definition of termsUse ontology to define terms from the database or the database scheme


MappingsStructure enrichment (most common)

A logical model is built that resembles the structure of the information source and contains additional definitions and conceptsCan be done using DLs

Meta-annotation Add semantic information to an information sourceontobroker, SHOE


Inter-Ontological MappingDefined Mappings (KRAFT)

special customized mediator agents Great flexibilityFails to ensure a preservation of semantics - no verification

Lexical Relations (OBSERVER)Extend a common DL model by quantified inter-ontology relationships Synonym, hypernym, overlap, covering, disjointDo not have formal semantics


Inter-Ontology Mapping (cont.)Top-level grounding (DWQ)

Relate all ontolgies used to a single top-level ontologyInheriting concepts from a common top-level ontologyCan resolve conflicts and ambiguities

Semantic correspondencesRely on a common vocabularyUses semantic labels in order to compute correspondences Subsumption reasoning can be used to establish relations between different terminolgies


Agents and OntologiesDomain models are used at run-time

agents share a context for efficient communication:– messages declare an ontology

ontology = vocabulary, relations, operations, rules, constraints...example ontology: fruit-market

– ontology agent - allows application agents to query, retrieve and translate ontologies

– example agent conversation: (cfp :to i :from j :ontology fruit-market :content (sell plum 50))(propose :to j :from i :ontology fruit-market :content ((sell plum 50 ) (cost 200)))(reject-proposal :to i :from j :ontology fruit-market :content (price-too-high))


Agents and OntologiesRDBMS

knowledgebase

reasoningnatural

languageprocessing

learning

ontology agent

userinterface

informationretrieval

personal assistantagent

business software

broker agent

otheragents

webpages

DAMLDAML annotatedannotated

Documents

Ontology - PKU · Ontology Lin Zuoquan Information Science Department Peking University [email protected] lz/teaching/stm/saswws.html