Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic...
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Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
The definition of a formal ontological framework aimed at semantic
interoperability: the case for fisheries domain
Aldo Gangemi1, Frehiwot Fisseha2, Johannes
Keizer2, Marc Taconet4, Ian Pettman3, Domenico M. Pisanelli1
1 Institute of Cognitive Sciences and Technology, CNR (National Research Council), Rome, Italy {gangemi,pisanelli}@ip.rm.cnr.it, http://ontology.ip.rm.cnr.it2 FAO-GILW, Rome, Italy {Frehiwot.Fisseha,Johannes.Keizer}@fao.org, http://www.fao.org3 One Fish, SIFAR, Grange-over-Sands, Cumbria, UK, [email protected], http://www.onefish.org3
4 FIDI, FAO, Rome, Italy, [email protected], http://www.fao.org
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
An example of interoperability
• Trockenbeerenauslese
• late collection of grapes &• infected by botrytis cinerea
axiomatization axiomatization
• Muffato della Sala
roquefort cheese
compatible with
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
Semantic interoperability of terminology-based systems
Different fishery thesauri, taxonomies, topic trees Different conceptualizations (current state)
Heterogeneous search results from multiple queries Integrated conceptualizations (info brokering solution)
Union of results from one query engine (?homogeneity? depends on amount of analysis performed)
Merged fishery ontology Merged conceptualizations (formal ontology solution)
Union of more precise (homogeneus) results from one query engine
Conceptual navigation, custom user profiles, community knowledge sharing, ontology-based catalogue
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
Resources for the fishery ontology merging project
• 10,600 thesarus “descriptors” (Agrovoc, ASFA)• 1,800 topic tree “subjects” (OneFish)• 200 core “composite concepts” (FIGIS)• 30,000 (≈taxonomical) “objects” (FIGIS)
Ontology Integration Framework ONIONS merging methodology OntoClean upper-level ontology
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
Ontology Merging Guidelines
Basic steps in the ONIONS/OntoClean Methodologies
DesignLibrary
Architecture
Deconstruct ExistingSources
Collect|integ.Ontology
DevelopmentResources
Create andDecompose
Glosses
AxiomatiseOntologyElements
Refine Informal
Relationships
Refine Taxonomy,
Define Rules
LexicaliseOntologyElements
Opt: MapReused
Ontologies
RefineLibrary
DecomposeTerms into
DependencyStructures
Assign Term Meaning to
OntologyElements
Assign OCT Taxonomical
Position & Meta-property
Iterate forNew Elementsfrom Glosses
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
Preliminary fisheries ontology library
Domain ontologies
Representationontology
Upperontology
Coreontology
Geographicontology
Speciesontology
Institutionsontology
Fishingdevicesontology
Fishing andfarming
techniquesontology
Farmingsystemsontology
Fisheryregulationsontology
Fisherymanagement
ontology
BiologicalontologyDevices
ontology
Legalontology Management
ontology
external theories:
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
oneFish topic trees (worldviews)
Administration
Subjects Ecosystem
Geography Species
Stakeholders
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
What is being done for fast prototyping a FOS-based system (1)
• Choosing and installing an ontology server• Translating the most conceptually transparent
portions of the resources into formal logic-based languages
• Building a preliminary core-level ontology wrt OCT upper ontology and FIGIS composite concepts
• Cleaning ontology building data to populate domain ontologies (next slide)
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
Aquaculture in AGROVOC
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
Aquaculture in ASFA
Aquaculture
BrackishwaterAquaculture
MarineAquaculture
AquacultureDevelopment
AquacultureFacilities
BROADERTERM
BROADERTERM
RELATEDTERM
RELATEDTERM
FreshwaterAquaculture
AquacultureEconomics
AquacultureEngineering
BROADERTERM
RELATEDTERM
RELATEDTERM
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
Aquaculture in oneFish
Aquaculture
AquacultureEconomics
AquaculturePlanning
Subject
AquacultureDevelopment
SUBTOPICSUBTOPIC
SUBTOPIC
SUBTOPIC
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
Aquaculture in FIGIS (composites)
Aquaculture Resource
Water Area
land
strains
Specieslife cycle
Farming system
management system
Production center
Spawning technique
Breeding technique
Hatchery technique
Expl. form
Regulation
Farming technique
Environment
Institution
Health monitoring technique
diseases
suppliers
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
FOS development (2)
• BT/NT are transformed into taxonomies; e.g.: SUBSUMES(c1,c2), provided that c1 \ c2 according to upper ontology?
• RT are transformed into axioms; e.g.: PARTICIPANT(i1,i2), provided that the topmost parents of c1(i1) and c2(i2) are related by PARTICIPANT in the core ontology?
• Topic trees into (preliminary) topic spaces
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
Conceptual templates
• Conceptual templates are relevant parts of ontologies that describe the core concepts and relations of a domain (core ontologies)
• Ex. APO, BFQF, etc.
• They can be often discovered from:
• database schemata, forms, elicited know-how
• analysis of systematic polysemy
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
The APO schema
Activity
:Occurrence
1 PARTICIPANT nnnnnn Object
:Entity
1
METHOD
nnnnnn Plan
:MentalObject
(composed)1
INVOLVED-IN
nnnnnn
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
Use of APO
• It resulted useful in several domains: • clinical guidelines (planning vs. process
knowledge)• fishery (management vs. intervention knowledge)• banking regulations (legal vs. world knowledge)• possibly extendable to the general problem of control
knowledge• In the third lecture some examples from the banking
domain will be given
• Warn the mereotopological constraints when applying it!
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
The APO schema modified for clinical conditions
Condition
:Occurrence
1 PARTICIPANT nnnnnn Bio.Object
:Object
1
REPRESENTED-BY
nnnnnn Finding
:MentalObject
(composed)1
INVOLVED-IN
nnnnnn
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
APO applied to transactions
Transaction
:Occurrence
1 PARTICIPANT nnnnnn Customer
:Social Object
1
METHOD
nnnnnn Contract
:Information Object
(composed)1
INVOLVED-IN
nnnnnn
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
OCT: the OntoClean Top-level
Phenomenon
SpeConstDep:Object
Activity
GenConstDep:AbstractObject
Quality
EnduringSpeConstDep:EntityInhere-To:Entity
Quality Region
EnduringAlwaysPresent
Social Object
GenConstDep:Community
Mental Object
GenConstDep:SingularPerson
Abstract Object
NOT(Inhere:Location)GenConstDep:PersonMereoVariable
Arbitrary Collection
Pseudo-constantPart:Essential whole
Physical Object
Inhere:SpatialLocationNOT(Inhere:TemporalLocation)NOT(SpeConstDep:Object)
Occurrence
PerduringParticipant:ObjectInhere:TemporalLocationNOT(Inhere:SpatialLocation)[ALL]Temporal-Part:Occurrence[ALL]SpatialPart:Occurrence
Feature
Host:EntityEssential WholeHeterogeneous UnityGenConstDep:Entity
Object
EnduringEssential WholeHeterogeneous UnityParticipatesIn:Occurrence
Relational S
[EX>1]Participant:Object
Non-Relational S
[EX1!]Participant:Object
Entity
Process
Weakly-Homeomeric
Accomplishment
NOT(Homeomeric)
State
Homeomeric
Body
MereoInvariant
Ordinary Object
MereoVariable
Amount of Matter
MereoInvariant
Aggregate
EnduringNOT(Essential Whole)
Relevant Part
Part-Of:*Host
Place
NOT(Part-Of:*Host)
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
A core ontology for aquaculture
1
GENERICALLY-LOCATED
111111
nENFORCED-BY
111111
Institution
:Legal Person:Social Object
1TARGET
nnnnnn
MANAGED-BY
nnnnnn
Aquaculture
:Fishery:Activity
Geographic Area
:Spatial Location:Quality
1 SITUATED111111
Environment
:Physical Place
Water Area1SPATIALLY-LOCATED 111111
Aquaculture Management System
:Social Object
Regulation
:Plan
nnn1 REGULATED-BY nnn
1
MEMBER
nnnnnn
nnn
nnnnnn
nnn
1
METHOD
nnnnnn
Fish Farming Technique
:Technique:Plan
Aquatic Organism
:Biological Object
Aquaculture Resource
:Biological Collection
Breeding TechniqueHatchery Technique
Health Monitoring TechniqueSpawning Technique
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
An excerpt from the ontology
The concept fishing technique is formalized in a description logic as follows:
(defconcept Fishing-Technique :annotations ((DOCUMENTATION "FIGIS: A fishing
technique describes the set of equipment used for the capture of a target species together with any associated fishing practices."))
:is (:and Technique (:some INVOLVES Gear) (:some METHOD-OF Fishery) (:some PART Handling-Mode)))
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
FOS development (3)
• Producing or reusing glosses (informal descriptions)• Building and refining library architecture• Choosing integration architecture (mediation or merging)• Applying integration, building and active cataloguing
procedures• Building (or reusing) query interface and wrappers to
source dbs
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
Fisheries merged information access
A user query is submitted to an interface connected to the Fishery Ontology Server. Interface and Server communicate with a Topic-Based Fishery Information Browser. The browser can either interrogate the source systems, or perform own searches to document corpora. The Server can directly provide information on fishery conceptual structures, terminology, and scope notes.
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
Web sites
• CNR site with methodology papers and example ontologies: http://ontology.ip.rm.cnr.it
• Agriculture Ontology Service site: http://www.fao.org/agris/aos
• Forthcoming fishery ontology site
Gangemi, Fisseha, Keizer, Taconet, Pettman, Pisanelli: A Merged Fishery Ontology for Semantic Interoperability
Metadata types in ontology development resources
111
as reusable component
Documentation
Taxonomy
Lexical item
Topic
Informal domain ontology
InformalAxioms
Set of assertions
n EXTRACTED-FROM
111
Library of modules
Source
Set of axioms
Fishery resource types::Ontological structure
Ontological structure
Informal ontology fragment
RT informal axioms
Reusable component from original
Thesaurus
BT,NT,RT informal axioms
Glossary
Documentation
Topic tree
Inclusion hierarchies
Domain schema (conceptual template)
(Informal) axioms
Upper ontology
OntologicalStructureTopics namespace
Assertion
Individuals namespace
Relations namespace
ConceptConcepts namespace
Relation
Individual
Axiom
Set of lexical items
111
Processed namespace
n
EXTRACTED-FROM
111
Ontology element
BT/NT hierarchy
Topic tree fragment
Resource for ontology development