Lri Owl And Ontologies 04 04

Joost Breuker

eLEGI Workshop 15-16 april 2004

Developing Ontologies

Joost Breuker

Joost Breuker


overview

What is an ontology? Ontology, thesauri and other taxonomic species Core ontologies

Representing ontologies (Rinke) Knowledge representation: from T-Boxes to OWL KR tools

Using ontologies traditional roles and the Semantic Web tools: reasoning, information retrieval, knowledge management

Developing LRI-Core principles of common sense main divisions (phyisical, abstract, mental, role, occurrence)

Joost Breuker


when you miss the points…

W3C documentation on semantic web, RDF & OWL http://www.w3.org/2001/sw/

Grigoris Antoniou and Frank van Harmelen. A Semantic Web Primer. MIT-Press, 2004.

S. Staab and R. Studer, Handbook on Ontologies, Springer, 2003 F. Baader, et al, (Eds), Description Logic Handbook, Cambridge

University Press,2002. (Ch 1, and part III, applications (…Ch 13)

Joost Breuker


Leibniz (1647-1716) on computable ontologies

“Once the characteristic numbers of most notions are determined, the human race will have a new kind of tool, a tool that will increase the power of the mind much more than optical lenses helped our eyes, a tool that will be as far superior to microscopes or telescopes as reason is to vision”

from: Philosophical Essays

Joost Breuker


Leibniz (1647-1716) on computable ontologies

“Once the characteristic numbers of most notions are determined, the human race will have a new kind of tool, a tool that will increase the power of the mind much more than optical lenses helped our eyes, a tool that will be as far superior to microscopes or telescopes as reason is to vision”

from: Philosophical Essays

computableindex

concepts

reasoning by“ars combinatorix”

Joost Breuker


what is an ontology?

`formal specification of conceptualization’ (Gruber 94)

“An ontology defines the terms used to describe and represent an area of knowledge” (Jeff Heflin, OWL-Use cases,

http://www.w3.org/TR/2004/REC-webont-req-20040210/ )

terms: concept (= meaning)… (+ symbol (word; index;…)?) knowledge representation: from informal (eg text) to machine

interpretable (via formalization) ontology: `what is’ ≈ what we know

Joost Breuker


an example: newspaper ontology

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epistemology vs ontology

ontology in philosophy: study of existence of entities epistemology: how do we know?

epistemology is about justification of knowledge `correct’ reasoning

in ontological engineering: ontology: definitional structure of concepts

Joost Breuker


different things or point of view?

reasoningmethod

PSM inference

deduction abductionclassificationcover & differentiate

PSM

hypothesistesting

assemblehypothesis

matchdata

hypothesisgeneration

predictvalues

obtaindata

`epistemological’view

`ontological’view

IS-A

inferences inferences

DEPENDENCY

PART-OF

Joost Breuker


(DAML)OWL-S: an `ontology’ for web services

Joost Breuker


Another pseudo-ontology: FOLaw normative reasoning (Valente, Breuker & Brouwer, 99)

CASE

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Is that a problem?

Yes: they are reasoning frames by representing reasoning dependencies between types of knowledge/partitions of knowledge bases; not classes (concept definitions)

No: OWL (and other KR formalisms) can express easily these frames

IMPORTANT: Highly useful in reuse (eg specifying web-services by OWL-S) However: better keep these `epistemological frameworks’

separate

Joost Breuker


Semantic levels of ontology

Level 0: Dictionaries, describing informal definitions associated to concept names, with

no formal semantic primitives; Level 1: Taxonomies,

describing specialization relationships between concepts; Level 2: Thesauri,

adding to taxonomies various lexical relationships (hyperonimy, synonimy, partonomy, etc…) eg Wordnet

these enable some identification of terms (text) Level 3: Reference models,

combining many of the relations above and many other (axiomatic) relations:

these enable reasoning…

Joost Breuker


Abstraction levels of ontologies

Upper, top, foundational ontologies capturing our most abstract, often common-sense, notions

• full ontologies (CyC, SUMO, DOLCE, Sowa, LRI-Core…)• partial ontologies about: time, space, liquids, physical processes,…

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Sowa’s (1999) top ontology

Joost Breuker




• full ontologies (CyC, SUMO, DOLCE, Sowa, …)• partial ontologies about: time, space, liquids, physical processes,…

Core ontologies capturing the most abstract terms in a field of practice eg. electro-mechanical engineering, medicine, law, process-

industry-components, cultural heritage, etc often: need for including/starting with some `top’ ontology

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LRI-core ontology for law

Joost Breuker




• full ontologies (CyC, SUMO, DOLCE, Sowa, …)• partial ontologies about: time, space, liquids, physical processes,…

Core ontologies capturing the most abstract terms in a field of practice eg. electro-mechanical engineering, medicine, law, process-

industry-components, cultural heritage…---> organic chemistry often: need for including/starting with some `top’ ontology

Domain ontologies the `real’ stuff:

• wines, newspapers, Dutch criminal law, ships,

Joost Breuker


Representing ontologies:a short overview of KR-research

core business of AI research two major formalisms: networks-of-concepts and rules from semantic networks (…RDF(S)) to description

logics (DL) based systems (KL-ONE `family’) from informal, intuitive `semantics’ to logic based (model theory) trade-off between expressiveness of formalism and tractability

of implied inferences distinction between `terminology’ and `assertions’

• every country has a capital (generic concepts; T-Box)• Amsterdam is the capital of the Netherlands (individuals; A-Box)

AI/strict-ontological engineering view: ontology is T-Box in DB/OO community: individuals + schema

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KR for the Semantic Web: OWL

To allow semantics based services and information management, the Web needs protocols and standards that enable: specification, access, and maintenance of the meaning of

terms and objects (images) of web-pages also: non-human agents to process pages by their content see http://www.w3.org/TR/2004/REC-webont-req-20040210/ for use-cases

Meaning of terms/(images of) objects can be specified in an ontology.

Need for an ontology language: RDF(S)?

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layers of the Semantic Web

OWL

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OWL and RDF(S)

RDF(S) knows about: classes (concepts), properties (relations), individuals

(instances) but:

• intractable reasoning allowed (too expressive)• lacking expressivity (eg. cardinality, disjunction, properties of

properties)

Solution: OWL design requirements: formal founding (subset of FOPL) extending RDF(S) by new constructors (cardinality, etc.) three levels of expressivity:

Joost Breuker


three species of OWL

OWL-Full: most expressive, but intractable… all of OWL; plus fully `upward’ compatible with RDF --> any legal RDF/S document is also a legal OWL document

OWL-DL: limited to a Description Logic (SHIQ; fragment of FOPL)

--> any legal OWL document is a legal RDF/S document (but not vv)

OWL-Light no enumerated classes, disjointness, and full cardinality

Joost Breuker


W3C only provides specifications: no tools thus far, the OWL/RDF development tools come

from academia Tools are very important

hiding an awful syntax supporting information management

• graphical interfaces…

Three most used tools Protégé with OWL `plug-in’ http://protege.stanford.edu

/download.html Triple-20 (Prolog based, fast classifier):

http://swi.psy.uva.nl/tools OILed http://oiled.man.ac.uk/download.shtml

Tools for developing ontologies in OWL

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…Protégé

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Reasoning tools

The KR tools produce OWL structures. To be able to reason one needs `inference engines’ (plug-ins; built-in) eg FACT, RACER,…

Classifier (subsumption): automatic classification of new classes (also: multiple classes) automatic verification of individuals: basis for consistency

checking small stuff: inheritance, exclusion, …

Special reasoning: implications of: part-of structures and aggregation positions and areas of space and time, etc

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Use of ontologies (1)

Knowledge systems: specifying the ingredients of a knowledge base (CommonKADS) part of a(n articulate) knowledge systems (model based reasoning)

(high demands on inference) Information retrieval, knowledge management, …SemWeb

information = data * knowledge (DB-schemas vs knowledge models)

implied (key) terms in search: sub/super class terms• Note: more positives (many more fals ones!)

annotating and indexing documents• handling large quantities of documents and other sources of

information question answering (high demands on interpretation and inference)

• eg web-bots (lowest price of good stuff)• eg legal case description --> which law violated

Joost Breuker


Use of ontologies (2)

Semantic basis for dialogue, transaction & translation ontology as common semantic reference (vs data-model)

• eg alternative to EDI ontology as source for mutual understanding

• role of common sense in human discourse problem: common sense (upper) ontology (CYC?)

• role of tacit knowledge in professional/specialized communication core ontologies NB: this is not really new: see information science and professional

terminology standards

ontology as `interlingua’ for NL-translation (cf Euro-Wordnet)

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Some relevant examples

EPISTLE (http://www.epistle.ws/) European process industry terms for components and processes over 20 years experience about 20 permanent staff number of ISO standards etc.

Process Specification Language (PSL) http://www.mel.nist.gov/psl

National Institute of Standards Ontology of `process’ fully axiomatized in KIF (FOPL)

The CIDOC Conceptual Reference Model (CIDOC CRM) http://cidoc.ics.forth.gr

ISO standard for Cultural heritage terminology UMLS, OpenGALEN, etc

DL based medical terminology (see Ch 13, Handbook of DL) For more: see Ontoweb portal (SIG-1), deliverables 3.1 and 3.2

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developing upper ontologies

more than 2500 years in philosophy (eg Aristotle) (see Sowa. `99) IEEE-SUO (Standard Upper Ontology)

basic reference ontology for Semantic Web… strong committee and open web/email based communication proposals, workshops huge clash of views, alternatives, discussion

• major trend: the longer the discussion the larger the disagreement• proposal to vote!• technical/formal merge of several proposals (SUMO)

SUMO: physical and mathematical worlds (Sowa, EPISTLE, …)

All upper/foundational ontologies are: a source of disagreement necessary to structure and facilitate core/domain ontologies

• all established core ontologies have upper ontologies. These upper ontologies have been the source of some major overhauls!

Joost Breuker


Some morals: core ontologies

Developing core ontologies is highly successful if: there is a well managed, dedicated, professional organization

that is well recognized, but works in small teams rather than by

open discussion is able to establish some common upper ontology, ie use

abstractions about the field of concern NB: the upper ontology emerges as a side effect! However: only the team members learn from sharing

Joost Breuker


Some morals: upper ontologies

Developing upper ontologies appears never to be successful by itself increasing divergence

• cf philosophy (metaphysics)• cf huge mailing list of SUO

…but they are necessary! emerging from core ontology development (long time effect) reuse! parts etc. some start….

Joost Breuker


Some morals: sharing vs shared in constructing abstract ontologies

I have learned a lot in participating in … SUO Ontoweb, SIG-1 community of legal ontologies

so that I have my own ideas of what an upper ontology should look like…

and more in particular: what a legal core ontology should look like

Joost Breuker


stop

that is for another session…

Joost Breuker


constructing LRI-Core

need for fixing recurring concepts in law (mostly: common sense) --> LRI-core has a strong `foundational flavour’

view: corresponding with our common sense intuitions about the physical, mental and social world naïve physics vs qualitative physics `revisionary views’ in philosophy needed: `evidence’ from psychological research

• cognitive (development) psychology• evolutionary psychology

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Principles from this view

Common sense in an evolutionary view starting with animal `understanding’ and action primacy of physical world adapting to environmental `domain specific inference engines’ (deficiencies)

physical world: (re-)acting to physical change objects: relatively static

• keep identity independent of position (-> motion) processes: kinds of changes of objects our knowledge of processes is dependent on

• sensors/perception• what changes occur 1) more frequent and 2) more `speedy’

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some further principles

humans vs/and other animals (mammals) intentional stance consciousness natural language: manipulation of symbols representing

• metaphorization,• `reification’ (beliefs, etc.)

these all enable the development of worlds beyond the physical world mental world as a metaphor of physical world distinction between behaviour and planned/desired behaviour

• roles creating abstract world (`form’) by metaphorizing `instincts’

about the physical world (eg: grasping entities of the same kind, counting, …)

Joost Breuker


…and a very basic principle…

the knowledge entities (concepts) are èternal’ once acquired…(ontogenesis; phylogenesis) they work like Plato’s idols the more abstract, the more èternal’ (eg circle, point, line..)

therefore, there are no `temporary’ concepts in an ontology no òccurent’ (perdurant)/ `continous (endurant) distinction IN

the ontology • e.g. plans, roles, processes, etc may use time/space as a

`resource’, but they exist independent of their instantiation• e..g. distinction between plan and plan-execution, norm and action,

role and role-taker/performance individuals have life cycles (identity criteria) instances òccur’ in time and space

Joost Breuker


..however…

we need terms to refer to occurrences events and states situations and histories foreground/background, system/environment causation: the glue between events

on the canvas of space and time positions, areas, instances, duration time’s arrow

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five `worlds’ of concepts

physical world matter/energy --> object and process

mental world metaphor intentional stance communication

roles physical and social roles social organization

abstract occurence

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physical world

basic `natural’ concepts: energy & matter basic defined concepts: physical object & process

both contain mixtures of energy & matter objects are in states (see further: `occurences’) processes are/cause changes (and the source of `causation’)

• transfer (changing places)• changing value• transformation (changing type)

types of processes• mechanics: movement & support are core (cf senses & muscles)• thermo-dynamics: heat exchange• chemistry: mixing/changing substances

biology: life, breathing, growing, illness, …

Joost Breuker


process and object

energymatter

processobject

heat

electricity

force

state

substance

transfer

quantity

form

position???

aggregation

transformationchange-of-value

is-a

change-of-substance

mass

change

is-a

is-a

is-a

part-of

heat exchange

radiationmovement

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…processes in OWL-S….

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the mental world (1)metaphor of the physical world

mappings: energy --> emotion|motivation matter/substance --> thought/content (information) object ---> mental-object (concept,…)

• container ----> mind, memory process ---> mental-process (thinking, memorizing, …)

• process --> action• transfer ---> speaking

exchange ---> communication nb: reasoning-structures vs ontology of reasoning terms (hypothesis,

evidence, etc) mind/body `problem’

person has mind; mind is container of mental entities action: will as `force’ NB: this naïve view is incorrect! (but still the accepted wisdom in phil.)

Joost Breuker


the mental world (2): intentional stance

intention in philosophy: Husserl --> phenomenology (--> Dennet, Searle, etc) instantiated goal state

• motivated• planned

intentional/teleological/functional stance vs causal view physical events are explained by processes (causation) agent initiated events are explained by actions

• actions have intentions• world `predicted by’ plans (concatenations of actions and

processes)• abductive reasoning: from effect to causal determinants (initial

states) vs from initial state to (all possible/predictable) states artifacts (physical): taking a intentional view

communication….

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roles

distinguishing between role and role taker

• cutting - knife (for physical objects/artifacts: often: `function’)• student - person

roles define complementary relations (property constraints) speaker-hearer, student - teacher these `complementary relations’ explain duty/rights relations in

legal theories roles ARE behavioural pre-scriptions

requirements for role taking (cf man taking `mother role’) norms, procedures

role performance may be assessed against role

Joost Breuker


social roles

social roles from teleological view on community behaviour `division and distribution of labour’ knowledge about society: role divisions between constructing roles as artifacts and evolving complexity of

social organization social organization as `assemblies’ of roles NB the fact that role-taking has a temporary character

does not mean that roles are `occurrences’! confounding role with role taker confounding role with role performance

• in law: norms addressed to roles; responsibility to role taker; norm violation to role performance

Joost Breuker


where it all happens:the world of occurrences

“And in order to understand how common sense works, there is nothing better than imagining “stories” in which people behave according to its dictates.” (Ecco, 99)

(semi-)Platonic view: ideas/concepts make up our understanding of what happens in the real world: understanding as constructing a model of a situation episodic vs semantic memory (psychology) Individuals vs Classes (A-Box/T-Box distinction) time and space as the referential canvas of situations and

events

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the world of occurrences-1situation 1

structural (topological) descriptions of objects in space

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the world of occurrences-2situation 2

inferred: time between situation1 and situation2

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the world of occurrences-3events & states of objects

desk

floor

teapot

ball

move/fall

move/fall

move/fall

move/fall

break

collide

move/fall

T-2T-1

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the world of occurrences-4identifying processes

desk

floor

ball

move/fall

move/fall

move/fall

move/fall

break

collide

move/fall

T-2

support

support

teapot

T-1

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the world of occurrences-5identifying causation

desk

floor

ball

move/fall

move/fall

move/fall

move/fall

break

collide

move/fall

support

support

teapot

Joost Breuker


desk

floor

ball

move/fall

move/fall

move/fall

move/fall

break

collide

move/fall

support

support

teapot

Why does thedesk not move?

•the world of occurrences-6limiting causal effects…

Joost Breuker


summary

identifying events by recognizing changes, which are viewed as instances of processes (-types) (cf causal-models,

Pearl, 2000)

identifying causation (= causal relations between events) identifying states as ongoing processes what happens to the forces (heat, energy,…) that are the

resources of processes (mental, qualitative simulation) (cf Michotte, 196x)

Joost Breuker


mapping processes to events

something event/state

causationsubjectrole

object process(type)

force

resourcerole

subjectrole

resourcerole

causality

space time

contextualization

instanciation

Joost Breuker


What is the Problem?

Consider a typical web page: Markup consists of:

rendering information (e.g., font size and colour)

Hyper-links to related content

Semantic content is accessible to humans but not (easily) to computers…

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What information can we see…WWW2002The eleventh international world wide web conferenceSheraton waikiki hotelHonolulu, hawaii, USA7-11 may 20021 location 5 days learn interactRegistered participants coming fromaustralia, canada, chile denmark, france, germany, ghana, hong kong, india,

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Register nowOn the 7th May Honolulu will provide the backdrop of the eleventh

international world wide web conference. This prestigious event …Speakers confirmedTim Berners-Lee Tim is the well known inventor of the Web, …Ian FosterIan is the pioneer of the Grid, the next generation internet …

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What information can a machine see…

…

…

…

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Solution: XML markup with “meaningful” tags?

<name> </

name><location> </location>

<date> </date><slogan> </slogan><participants>

</participants>

<introduction>

…

</introduction><speaker> </speaker><bio> </bio>…

Joost Breuker


But What About…

<conf> </

conf><place> </place>

<date> </date><slogan> </slogan><participants>

</participants>

<introduction>

…

</introduction><speaker> </speaker><bio> …

Joost Breuker


Need to Add “Semantics”

External agreement on meaning of annotations E.g., Dublin Core

• Agree on the meaning of a set of annotation tags Problems with this approach

• Inflexible• Limited number of things can be expressed

Use Ontologies to specify meaning of annotations Ontologies provide a vocabulary of terms New terms can be formed by combining existing ones Meaning (semantics) of such terms is formally specified Can also specify relationships between terms in multiple

ontologies

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use cases (OWL/W3C)

Web portal information management for interest communities by ontologies

(eg Ontoweb: http://www.ontoweb.org/)

Multimedia collections semantic annotations for collections of images, audio, or other

non-textual objects.

Corporate web site management Design documentation Agents and services Ubiquitous computing

Lri Owl And Ontologies 04 04

Education

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