ontology engineering quality, modular architectures, reengineering, and design patterns aldo gangemi...

Ontology EngineeringQuality, Modular architectures, Reengineering, and Design patterns

Aldo Gangemi

Laboratory for Applied Ontology

The Institute for Cognitive Science and Technologies

National Research Council, Rome, Italy

[email protected]

SSSW, Cercedilla, 19-24 July 2004 www.loa-cnr.it 2

Summary

Use cases and competency questions (only some of them are detailed)

Quality of ontologies Ontology engineering techniques (recap) Modular architectures Reengineering Ontology design patterns


Factual cases for ontologies

The red block is on the blue one My car has four (five) wheels Prof. Enrico Motta has trespassed a speed limit Last transaction by Mr. Blackspot is suspicious The monitored network had a serious breakdown Atlantic Enterprise II carries drifting longlines onboard Monna Lisa painting at the Louvre shows a mysteriously smiling woman The Aegyptian sphinx is not the same as the Greek one, but they result

to be sometimes blended in Renaissance art ACME e-publishing workflow requires that the project manager is

responsible for …, the author has the duty to … the assistant is obliged to …


Competency questions wrt those facts

Is the yellow block on the blue one? And the green one? Is there any spare wheel that I can borrow? When, where and why has Enrico trespassed the limit? Why do you think Mr. Blackspot transaction is suspicious? Is it convenient to repair the network for the monitoring company? Are there any vessels from a nearby country in the marine area

50N060W, which can fish a Thunnus alalunga stock through allowed techniques?

Is it convenient for the monitoring company to repair the broken network?

How to index the Monna Lisa painting at the Louvre? What’s the difference between (and examples of) respective shinges? Did the last ACME e-publishing procedure conform to the workflow?

Quality in the ontology lifecycle


What is an ontology? (yet another!)

A Formal, Partial Specification of a Conceptualization Conceived by some Rational agent for some (good or bad) Reason, and made in order to Negotiate that conceptualization with Someone else, or to Reuse it.

Extended from Gruber 1993, Guarino 1998.

LogicRepresentationMeaningCognitionEmbodimentContextAgreement SocietyCulture


Ontologies and KBs

An axiomatic theory: the Blocks World Ontology (BWO) Signature: {On, Block} Axioms: On(x,y) Block(x) Block(y) On(x,y) On(y,x) (On(x,y) On(y,z)) On(x,z)

A model M in BWO: On(red_block#1, blue_block#1) On(green_block#1, red_block#1) On(yellow_block#1, red_block#1)


Toy blocks in M

M


Quality dimensions of ontologies?

Precision(only the intended models)

Flexibility (relatedness of alternative intended models, scoping both inside and outside of a domain)

policy, economics, regulations, geography, sociology, …

contexts of many kinds …

Coverage(all the intended models)


Quality checking of BWO

Does BWO catch all the intended models Is BWO so precise in order to exclude non-intended

models (if any):• “Non-Vertical” On?• “Unconnected” On?

Is BWO flexible enough?• Can we specialize the theory in order to make it more

precise?• Can we represent supplementary entities (“surface”,

“cable”)?• Can we encode alternative axioms for the same signature, in

the same or a different theory?


Beware overcommitment!

BWO*: Signature: {On, Constitution, Moves, Block, Substance, Atom,

Baby, …} Axioms:

{!}


Blowing off the SW bottleneck


Recap on ontology engineering techniques

Extraction of patterns by machine learning or NLP (pull) Generation of SW documents through Web Services-oriented

pre-designed patterns (push) Reuse of existing/legacy ontologies (pull/push)

• Simple reuse (pull/push)• Standards for definite domains (push)• Building blocks (pull/push)

Re-engineering of metadata (pull) Emergence from communities of interest (pull/push)


Anyway …

From KE viewpoint, there is a need for - a priori or a posteriori:• Selection: how can ontologies survive and reproduce?• Quality: what expertise is addressed, and to what extent?• Modularity: is there a viable design methodology for

ontology architectures?• Reusability: are there viable ontological components to

reuse (e.g. as building blocks)?

Inherent needs for Semantic Web deployment? All of them?

Modular architectures

Semantics of modules

Stratification

Foundational and core ontologies


Semantics of modules

Island namespaces• E.g. XML-OWL: local consistency must be ensured

Dependent modules• E.g. “uses” relation in Ontolingua or Loom: augmented local

consistency must be ensured

Partially ordered modules• Precise semantics: global consistency must be ensured

Contexts• E.g. C-OWL: bridging axioms (local semantics)


OWL-RDF (XML) namespaces

ns1:vehicles

ns2:trafficnorms

Merging of namespacesNo global consistency required


An ontology dependency graph

ns1:vehicles

ns2:trafficnorms

Merging of namespacesConsistency in ns2 of used elementsfrom ns1 must be ensured

uses


An ontology library

ns1:vehicles

ns2:trafficnorms

inherits from

Merging of namespacesConsistency of all elements from ns1 must be ensured in ns2


Where are the modules?

mereology

Allen’s event calculus

RCC-8Italian Constitution

theory of flogistum

“how to make a coffee” for dummies

railway timetable

WordNet

Roget’s thesaurus

AAT thesaurus

the modal jazz harmony

the glossary of nurses

UMLS Metathesaurus

CYC

conversational maxims

DOLCE


Stratification (qualified building blocks)

Stacking of modules in types (strata), with additional constraints over:• Coverage• Reusability• Axiomatization depth

An example


WonderWeb Foundational Ontologies Library (WFOL)

Reflects different commitments and purposes, rather than a single monolithic view.

A starting point for building new foundational or domain ontologies.

A reference point for easy and rigorous comparison among different ontological approaches.

A common framework for analyzing, harmonizing and integrating existing ontologies and metadata standards.


The structure of the WFOL

Modules are organized along two dimensions:• visions, corresponding to basic ontological choices made• specificity, corresponding to the levels of generality/specific domains

Choose Vision

Choose Specificity

Top

Bank

Law

4D

3D

Single VisionSingle Module

Formal LinksBetween Visions and Modules

Mappings betweenVisions/Modulesand Lexicons


Formal criteria (concepts)

individuals vs. concepts (Italy vs. Country) 3D vs 4D (Italy at once vs. Italy as temporal

worm) unity vs. amount (Italy vs. soil of Italy) regions vs. entities (Italy extension vs.

territory of Italy) descriptions and situations (a plan and its

execution, a theory and its models, a norm and its cases)


Formal criteria (relations)

symmetry (e.g. “connection” vs. “part”) transitivity (e.g. “ancestor” vs. “father”) immediacy vs. compositionality (e.g. “part” vs.

“systemic component”) intra-categoriality vs. inter-categoriality (e.g.

“part” vs. “participation”) schematic primitives

• part, function, structure, participation, inherence, localization, succession, satisfaction …


A toy example of stratification

Domain ontology

{Sculpture,Restoration, Mythical being, Caryatid, Doric order, Armilla, Fresco, …}

Core ontology (specific domain-independent)

{Work of art, Painting technique, Author, Artistic period, Plastic art, Interpretation, …}

Foundational ontology (domain-independent)

{Object, Process, Part, Time, Location, Representation, Plan, …}

inherits from

inherits from


A realistic example of stratification

DOLCE foundationalontology – “ground”

OntoWordNetfragments –“posts”

Fishery coreontology –“walls”

Fishery domainontologies –“roof” and “floors”

The “toyhouse”


Sample taxonomy within library

Class(Moonfish#10 partial (Agrovoc)

Class(Saltwater_Fishes#10 partial (Agrovoc)

Class(Fishes#10 partial (Agrovoc)

Class(Aquatic-Organism#3 partial (COF)

Class(LIFE_FORM$ORGANISM$BEING$LIVING_THING#4 partial (ONTOWordNet)

Class(agentive-physical-object#1 partial (DOLCE)

…


An example of a foundational ontology: DOLCE

DOLCE: a Descriptive Ontology for Linguistic and Cognitive Engineering

Cognitive/Linguistic bias• Categories mirror cognition, common sense, and the lexical

structure of natural language.• Categories as conceptual containers: no “deep” metaphysical

implications wrt “true” reality.• No deep commitment on “intellectual economy” of the primitive

notions: focus on the simplicity of the representation primitives.


Basic Ontological Choices in DOLCE

Objects/Substances and Events/Processes are distinct categories linked by the relation of participation.

Qualities inhere in Objects (Physical Qualities) or in Events (Temporal Qualities) and they corresponds to “individualized properties”, i.e. they inhere only in a specific entity, e.g. “the color of this court”, “the velocity of this serve”, etc.

Each kind of Quality is associated to a Quality Space representing the space of the values that qualities can assume

• Quality Spaces are neither in time nor in space• Different quality spaces associated to the same kind of Quality are admitted.• Space and Time are specific quality spaces• Different kinds of space and time are admitted.

Different Objects or Events can be spatio-temporally co-localized: the relation of constitution is considered

Rich axiomatization of classes and relations


DOLCE+ top-level

Reengineering legacy metadata

A complete workflow


Ontologies by precision

Ontological precision

Axiomatized theory

Glossary

Thesaurus

Taxonomy

DB/OO scheme

tennisfootballgamefield gamecourt gameathletic gameoutdoor game

Catalog

game athletic game court game tennis outdoor game field game football

gameNT athletic game NT court game RT court NT tennis RT double fault

game(x) activity(x)athletic game(x) game(x)court game(x) athletic game(x) y. played_in(x,y) court(y)tennis(x) court game(x)double fault(x) fault(x) y. part_of(x,y) tennis(y)

precision: the ability to catch/provide all and only the intended meaning(for a logical theory, to be satisfied by intended models)


Lifting and datamodel creation/integration


Legacy aquaculture hierarchies from fishery terminology systems

AQUACULTURE (AGROVOC)NT1 fish culture NT2 fish feedingNT1 frog culture…rt agripisciculturert aquaculture equipment…Fr aquacultureEs acuicultura

AQUACULTURE (ASFA)NT Brackishwater aquaculture

NT Freshwater aquaculture NT Marine aquaculture rt Aquaculture development rt Aquaculture economics rt Aquaculture engineering rt Aquaculture facilities

Biological entity (FIGIS)

Taxonomic entity

Major group

Order

Family

Genus

Species

Capture species (filter)

Aquaculture species (filter)

Production species (filter)

Tuna atlas spec

SUBJECT (OneFish)

Aquaculture

Aquaculture development

Aquaculture economics @

Aquaculture planning


Heterogeneous datatypes in legacy FIGIS DTDs


Sample data model analysis/conversion in FOS

Term ≠ Concept

Term String

Concept = Class

BT ≈ subsumption between classes

RT ≈ top-level conceptual relation

{Descriptors} = {Classes},{Individuals}

Individual Class

Concept ≠ Subject/Topic/Domain


Formalization and Core Ontology Creation


Datamodel mapping and its effects on translation

agrovoc_schema:Descriptor• agrovoc:River• agrovoc:Amazon

owl:Class(agrovoc:River) owl:Individual(agrovoc:Amazon(rdf:type agrovoc:River))


Datamodel mapping and required transformations

agrovoc:RT agrovoc_schema:Descriptor

• agrovoc:Fishing_vessel• agrovoc:Fishing_gear• agrovoc:Fishing_vessel,RT,Fishing_gear

Class(agrovoc:Fishing_vessel partial

(restriction(agrovoc:RT

someValuesFrom(agrovoc:Fishing_gear))))


One fishery core ontology for techniques


Modularization and alignment


FOS library

DOLCE foundationalontology – “ground”

OntoWordNetfragments –“posts”

Fishery coreontology –“walls”

Fishery domainontologies –“roof” and “floors”

The “toyhouse”


Alignment method

Does the class to be aligned (or a proximal one) exist in COF? if so, align it, e.g.: • Class(figis:Marine_fishes partial cof:Aquatic_organism)

• Otherwise: Does the class exist in WordNet? if so, include the OntoWordNet fragment

related to that class into the library, e.g.: • Class(figis:Aquatic_resource partial own:Asset)

• Otherwise: Do the experts think the class is very relevant for fishery, or the class has a rich

taxonomy under it? if so, create a small taxonomy that sketches a new core ontology for that domain, e.g. by adapting them from OntoWordNet (e.g. vehicles, equipments) or from existing ones (biomedicine)

• Otherwise: Align the class directly under a generic class in the foundational layer, e.g.:

• Class(figis:Country partial dolce+:Political_Geographic_Object).


Minimal case of alignment

asfa:Trawlers

cof:Fishing_vessel

dolce+:physical_objectfoundational

core

domain

= rdfs:subClassOf


Sample taxonomy within library

Class(Moonfish#10 partial (Agrovoc)

Class(Saltwater_Fishes#10 partial (Agrovoc)

Class(Fishes#10 partial (Agrovoc)

Class(Aquatic-Organism#3 partial (COF)

Class(LIFE_FORM$ORGANISM$BEING$LIVING_THING#4 partial (ONTOWordNet)

Class(agentive-physical-object#1 partial (DOLCE)

…


Consistency issues

From ASFA formalization, we get that:• Class(asfa:Trap_fishing partial asfa:Catching_methods)• Class(asfa:Trap_fishing partial asfa:Fishing)

From the alignment, we know that (transitively):• Class(asfa:Catching_methods partial dolce+:Object), while

Class(asfa:Fishing partial dolce+:Activity)

And from DOLCE+ we know that:• (disjointClasses dolce+:Object dolce+:Activity)

Hence the intersection:• Class(asfa:Trap_fishing partial

(intersection asfa:Catching_methods asfa:Fishing))

is inconsistent


BT polysemy issues

From AGROVOC we know:• Class(agrovoc:Blood_Cells partial agrovoc:Blood)

From a biomedical ontology(ON9.3) we know:• Class(agrovoc:Blood_Cells partial on9:Cellular_structure)• Class(agrovoc:Blood partial on9:Tissue)• (DisjointClasses on9:Tissue on9:Cellular_structure)• Class(on9:Tissue partial• (restriction(dolce+:constituent someValuesFrom(on9:Cellular_structure))))

Therefore, on the basis of ON9, we can suggest that• Class(agrovoc:Blood_Cells partial agrovoc:Blood)

is a polysemous use of the BT relations (a cellular structure cannot be a tissue, because the two classes are disjoint), therefore it is possibly the case that:

• Class(agrovoc:Blood_Cells partial• (restriction(dolce+:constituent someValuesFrom(agrovoc:Blood))))


Refinement, annotation, and merging


ReLearning procedure

Given RT relations and axioms from reference ontologies:• Class(asfa:Trawlers partial

(restriction(asfa:RT someValuesFrom(asfa:Pelagic_fisheries))))• Class(own:Instrumentality partial

(restriction(dolce+instrument_for

someValuesFrom(dolce+:Activity))))

and we know transitively that:• Class(asfa:Trawlers partial own:Instrumentality)• Class(asfa:Pelagic_fisheries partial cof:Fishing_activity)

then we can infer that:• Class(asfa:Trawlers partial

(restriction(dolce+instrument_for

someValuesFrom(asfa:Pelagic_fisheries))))


Annotations

Domain lexicon (synonyms and multilingual labels) Subject trees (topics) Identification codes (unique identitifers) Comments (glosses, scope notes, examples, etc.)


Merging

Homonymic merging:• agrovoc:Trawlers• figis:Trawlers• asfa:Trawlers• fos_vessel:Trawler

Heterogeneous taxonomic position (emergent polysemy):• Class(agrovoc:Dredgers partial agrovoc:Ships)• Class(asfa:Dredgers partial asfa:Work_platforms)

Expert’s merging (emergent synonymy):• asfa:Ships• figis:Non-fishing_vessels


Post-processing lifecycle


Query expansion and brokering to distributed document management systems


Fishery KBase through the Ontosaurus ontology server


Mock-up for querying distributed dynamic data

"tell me what vessels from a nearby country are currently in the marine area 50N060W within Atlantic Ocean, provided that also some Thunnus alalunga stock can be fished by those vessels, through allowed techniques"

(retrieve ?ves (exists (?type ?tech ?spec ?cou ?stock) (and (instance-of ?ves ?type) (superrelations ?type Fishing-Vessel) (superrelations ?tech Fishing-Technique) (has-depend-ons ?type ?tech) (has-depend-ons ?tech ?spec) (subrelations ?spec |Thunnus alalunga|) (instance-of ?cou Country) (instance-of ?stock Aquatic-Stock) (FLAG-STATE ?ves ?cou) (WEAK-CONNECTION ?cou |Atlantic Ocean@fig|) (OCEANIC-POSITION ?stock |50N060W@fig|) (OCEANIC-POSITION ?ves |50N060W@fig|) (fail (has-depend-ons ?tech |Drifting longlines@fig|)) (RESOURCE-SPECIES ?stock |Thunnus alalunga|))))

-> (|i||Atlantic Enterprise II|)

Blue: core concept typesPale blue: domain concept typesRed: individualsDark red: individual(s) found

Ontology Design Patterns: the route to practical, well-founded ontologies

What is an ODP

Part

Space-time

Roles and tasks

Information objects


Related projects

FP5 Project WonderWeb: Ontology Infrastructure for the Semantic Web: http://wonderweb.semanticweb.org • Languages, tools, foundational ontologies, reengineering methods

W3C Semantic Web Best Practices and Deployment Working Group: http://www.w3.org/2001/sw/BestPractices/ • Ontology engineering design patterns, metadata reengineering

FP6 Project Metokis: Methodology and Tool Infrastructure for the Creation of Knowledge Units: http://metokis.salzburgresearch.at • Ontological engineering for task description and content description

(workflows, content analysis and filtering, etc.). Leveraging also on FP5 projects Cultos and Inkass


Sample Generic Use Cases

who does what, when and where? what are the parts of sth? what is it made of? what’s the place of sth? what’s the time frame of sth? how can you do what you do? does my behaviour conform to that rule? what’s the function of that artifact? how is it built? how did that phenomenon happen? what’s your role in that affair? is my scheduling compatible with yours?


Related work on design patterns

Theoretical architecture: C. Alexander, The Timeless way of building, 1979

Software engineering: E. Gamma et al., Elements of reusable OO software, 1994• D. Maplesden et al., Design Pattern Modelling Language• N. Baker et al., Meta-modelling patterns (-> “descriptive models”)

Ontology engineering (very recent interest): • G. Guizzardi et al., LINGO: foundation for meta-environments,

domain engineering in requirement analysis (bridging OE and OO)• J.R. Reich, patterns of ontology constructs, applied to Mbiology• M. Klein: ontology change/versioning patterns• G. Schreiber: started committee on DPs in OE• ODPs here: focus on the architecture of the content, rather than on

the architecture of the logical form that represents that content


Features of ontology design patterns

Any ontology is a potential domain design pattern, since it can instantiate a set of states of affairs.

An ODP focuses on specialization, rather than instantiation An ODP is similar to top-level or foundational ontologies, but it has certain

additional properties:

1. An ODP is a template for solving a domain modelling problem2. An ODP "extracts" a fragment of a TLO or FO, which is its "background”3. An ODP is axiomatized according to the fragment it extracts4. An ODP can be represented in any ontology representation language, although its

intuitive and compact visualization seems an essential requirement5. An ODP can be an element in a partial order, where the ordering relation requires that

at least one of the classes or relations in the ODP are specialized6. An ODP can be intuitively exemplified and catches relevant "core" notions of domains 7. An ODP can be often built from informal or simplified schemes used by domain

experts, together with the support of a foundational ontology and a methodology for domain ontology analysis, or by specializing existing ODPs

8. An ODP can/should be used to describe a "best practice" of modelling9. An ODP is similar to a DB schema, but an ODP is defined wrt a foundational ontology

and should have a general character, independently from local design details


This talk: using UML class diagrams

Non-standard use of UML to visualize ODPs:• generalisation -> subsumption (“subClassOf”)• association -> two-way conceptual relation (“property”)• attribute -> one-way conceptual relation (“property”)• assuming reasoning capabilities with classification and role

chaining• association with no cardinality: 0..*


Basic DOLCE pattern

(participant match#1 {Enrico, Aldo})(located {Enrico, Aldo} Milton_Keynes)(located match#1 2004_Jan18_1:00\2:00GMT)(part match#1 ace#12)


Time intervals pattern

MTA(x,y) (MereoTopological Association) =dfweekly connected(x,y) strongly connected(x,y) part(x,y) overlaps(x,y) successor(x,y))

[(located match#1 2004_Jan18_12:00\13:00GMT)(located match#2 2004_Jan23_12:00\13:00CET)(successor 2004_Jan18_12:00\13:00GMT 2004_Jan23_12:00\13:00CET)] (precedes match#1 match#2)

deducible (sufficient conditions)


Places pattern

(place ball#1 racquet#2) [(located ball#1 ball#1_space)(located racquet#2 racquet#2_space)(weakly_connected ball#1_space racquet#2_space)]

not deducible (only necessary conditions)


The D&S design pattern for roles, courses, and parameters

This design pattern can be used to represent interpretations (rules, norms, plans, designs, histories, etc.) of a same entity or a same set of related entities


D&S specialization for norms

[(legal_description traffic_norm#232) (legal_course driving_task) (legal_role driver) (legal_role vehicle) (legal_parameter speed_limit) (sequences driving_task driving#3283376400) (played_by driver Enrico) (played_by vehicle Lotus_Elise#M186YER) (valued_by speed_limit ≤60mph) (speed_location driving#3283376400 130mph)] [@(case driving_sit#666) (setting_for driving_sit#666 {driving#3283376400, Enrico, Lotus_Elise#M186YER, 130mph})]

A simple application: satisfiability of (being a case for) a traffic norm


Normative scenario (descriptive layer)

(regulative_description traffic_norm#232)[ (legal_course driving_task) (legal_role driver) (legal_role vehicle) (legal_parameter speed_limit) (requisite-for speed_limit driving_task) (functionally-depends-on vehicle driver)][ (sequences driving_task #x:Running&etc.) (played_by driver #y:Person&etc.) (played_by vehicle #z:Car&etc.) (valued_by speed_limit #w:Speed&≤60mph) (localization #x:Running&etc. #w:Speed&≤60mph)…]

Norm components

Expected ground constraints


Case scenario (ground layer)

[ @(case driving_sit#666) (setting_for driving_sit#666 {driving#3283376400, Enrico, Lotus_Elise#M186YER, 130mph})]

Case is established here by means of another descriptive component (@), e.g. a database schema, a sensing system, a temporal synchronization over possible fillers of norm components, etc.-> an aggregation component is always needed, be it even an automatic clustering (based on some predefined or emerging pattern, based on rules for pattern detection, …) -> constructive filtering bias


Pairing normative and case scenarios

Given that: [Running(driving#3283376400) Person(Enrico) Car(Lotus_Elise#M186YER) Speed(130mph)]

We can infer that:[(sequences driving_task driving#3283376400) (played_by driver Enrico) (played_by vehicle Lotus_Elise#M186YER) (speed_location driving#3283376400 130mph)]???


Reasoning on D&S satisfaction

Does driving_sit#666 satisfy traffic_norm#232?

No, why?

speed_limit cannot be valued by 130mph

because

130mph is not within the allowed range: ≤60mph


A pattern for information objects


Construction example: Sphinges


How many (and which) sphinges?

monument

plays_role

stoney objectto be restored

plays_role

instance-of

Greek mythical creature

instance-of statue

subclass-of

refers_torealizes

Englishexpressed_according_to

class

individual

word

Aegyptian mythical creature

instance-of

“sphinx”

The Sphinx

sphinx

Sphinx

sphinx

Sphinx

sphinx

metaphoricalblending

symbolic figure

instance-of

pharaoh

metaphoricalblending

*refers_to

* in naïve iconography

“Oedipus and the Sphinx”

realized_by

lexicalizeslexicalizes

lexicalizeslexicalizes

lexicalizes

lexicalizes

interpersonal role

instance-of


Which design patterns have been used?

Logical or metadata level ODPs:• Individual(The_Sphinx type(Sphinx))• Class(Sphinx partial Statue)• Class(Sphinx partial annotation(label “sphinx”))

Logical+Ontological ODPs:• Individual(The_Sphinx value(Realizes Sphinx_figure))• Class(Information_object partial restriction(someValuesFrom

Represents(Entity)))• Class(Information_object partial restriction(allValuesFrom

RefersTo(Description)))• Class(Physical_object partial restriction(allValuesFrom Plays(Role)))• Class(Entity partial restriction(allValuesFrom MetaphoricBlending(Entity)))


Sphinx query

Ex: query on semistructured data

«tell me what works of art from ancient Egypt are related to European works of art that also involve Greek mythology in a same cultural unit»

(retrieve (?x1 ?x2) (exists (?y ?z) (and (creation_3 ?x1) (creation_3 ?x2) (non-physical-object ?y) (entity ?z) (realizes ?x1 ?y) (origin ?y EGYPTIAN_EMPIRE$EGYPT) (or (and (represents ?x2 ?z) (origin ?z Classical_Greece)) (exists ?w1 (and (non-physical-object ?w1) (realizes ?x2 ?w1) (refers-to ?w1 ?z) (origin ?w1 EUROPE)))))))


Considerations on domain ontology building

Developing precise domain ontologies is time-consuming, and requires high competences

Not always is deep granularity required Not always is full expressivity required Our position:

• “lightweight” is ok, but if we a have a level for sharing intuitions and to establish negotiation, it is much better

• “local” precise ontologies are ok, but if we have a level to align and merge different local ones, it is much better

• precision is “heavyweight”, but scalability is reachable by using good patterns and interfaces

ontology engineering quality, modular architectures, reengineering, and design patterns aldo gangemi...

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