Upload
madison-terry
View
215
Download
0
Tags:
Embed Size (px)
Citation preview
Project Proposal for a Fishery Ontology Service
Aldo Gangemi
CNR-ISTC
Institute of Cognitive Sciences and Technologies
The task of the preliminary proposal
“enhanced online multilingual fishery and aquatic resources terminology tools … in conjunction with the development of an AGROVOC ontology server … the oneFish Community Directory, ASFA, FIGIS and WAICENT would gain mutual benefit from the development of such tools” to achieve better indexing and retrieval of information, and increased interaction and knowledge sharing within the fishery community”
It is a content capturing task!
The focus is on tasks (indexing, retrieval, and sharing of mainly documentary resources) that involve recognising an internal structure in texts (documents, web sites, etc.)
Content capturing, integration, and management is naturally addressed by ontologies
What is an ontology
• An ontology is a formal, explicit description of a domain, aiming at an intersubjective agreement. It is composed of these main (meta)data types:– Concepts
– Conceptual Relations
– Axioms (properties and attributes of concepts)
– Individuals
– Topics
– Documentation
Ontology data vs. o. building elements
• Usually these data are not built from scratch, but through knowledge transformations on ontology building elements: terms, classifications, linguistic rules, connectives, thesaurus and lexical relations, scope notes and glosses, etc.
• Warning! Not every building data type is an ontology data type (e.g. a morphological rule, a thesaurus relation, a text corpus are not data that can be found in an ontology)
Formalised ontology vs. informal terminology
• Logical formality (subsumption, models)
• Theoretical validity (formal data types)
• Concepts (+lexicalizations)
• Conceptual relations• Concept-based topics
• Linguistic correctness• Usage validity• Informal hierarchies
and data types• Normalised terms• Conventional relations• Intuitive topics
Conceptualization C
Language LModels M(L)
Ontology
Intended models IK(L)
Ontologies constrain intended meaning
Ontological interoperability
• Ontologies are used in many applications
• A common use of ontologies is in the task of getting semantic interoperability between information systems and among artificial or natural agents
• Semantic interoperability is required because terminologies per se do not ensure the unambiguous communication of the intended meaning of users
• In the realm of ambiguity, meta-level information is required
Ontology for metadata specification
• Metadata are data ‘about’ data
• Markup languages (XML, RDF, OIL)
• Schemas and models written in markup languages can be made more rigorous and intersubjective if they are based on formal ontological principles
In general, ontologies enhance the quality of an application by expliciting the assumptions of designers, implementors, users
Main advantages of conceptual-based terminologies for textual data treatment
• Information brokering and/or integration: for example, unified querying of heterogeneous thesauri, multiple search over different document databases
• Conceptual navigation within terminologies and terminology-controlled resources (documents, sites, etc.), for example, highlighting of different senses, viewpoints, and contexts of use
• Automatic or customised construction of user profiles, with possibility of automatic delivery of new or updated documents or site addresses
Aquaculture in AGROVOC
Aquaculture in ASFA
Aquaculture in oneFish
Aquaculture
AquacultureEconomics
AquaculturePlanning
Subject
AquacultureDevelopment
SUBTOPICSUBTOPIC
SUBTOPIC
SUBTOPIC
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
oneFish topic trees (worldviews)
Administration
Subjects Ecosystem
Geography Species
Stakeholders
Technical issues involved in the project
• Partition between terminological space (lexicon) and conceptual space (ontology); concept names can be chosen in the terminological space, but they are not terms; terms ‘lexicalise’ concepts
• Formal characterisation of concepts, relations, individuals, and descriptions (axioms) in a (description) logic with classification services and consistency checking
• Formal characterisation of topics in a dedicated topological space• Explicit linking of topic spaces (modules) with conceptual space (dependency
chains)• Domain-independent criteria and relations to guide analysis and modelling
processes• Interoperability of systems based on heterogeneous terminologies by integrating their ontologies (semantic interoperability through mediation or merging)
Detailed steps for prototype IFO development
• Modularise ontology library according to topics• Load and classify upper and core level ontologies in the Ontology
Server• Complete taxonomy cleaning, element glossing, and topic integration • Axiomatize glosses• Assign meta-properties• Integrate domain taxonomies and axioms with top and core ontologies• Reconstruct dependency chains to check topic topology• Define mapping relations from ontology to source schemas (converse
mapping should have been maintained during the development of the library)
• Provide multi-lingual lexicalisation to elements in the ontology library (easily derivable from source mapping maintenance)
Ontology Library Architecture
Domain Ontologiesbanana, organic lettuce, rose
Middle and Core* Ontologiesplant*, crop*, fishery*, law, ship
Upper Ontologiesobject, event, part, precedes, shape
Representation Ontologiesconcept, slot, instance, role, function
Fishery 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:
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 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)
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(c1,c2), provided that the topmost parents of c1 and c2 are related by PARTICIPANT in the core ontology?
• Topic trees into (preliminary) topic spaces
A view of the OCT upper level
Object
Occurrence Feature
Aggregate
QualityQualityRegion
Entity
INHERES-TO
PARTICIPANT
EXTENSION
The core ontology for capture fishery
CaptureFishery
GeographicArea
FishingGear
FishingVessel
FishingTechnique
HandlingMode
FishStock
AquaticOrganism
FishingAuthority
FishingManagement
System
LOCATED
TARGET
INSTRUMENT
METHOD
PART
MEMBER
CONTROLLED-BY
PLANNED-BY
The core ontology for aquaculture
Aquaculture
GeographicArea
BreedingTechnique
Health-MonitoringTechnique
FishFarming
Technique
HatcheryTechnique
AquacultureResource
AquaticOrganism
Institution
AquacultureManagement
System
LOCATED
TARGET
METHOD
PART
MEMBER
CONTROLLED-BY
PLANNED-BY
SpawningTechnique
An excerpt of 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)))
The APO schema
Activity
:Occurrence
1 PARTICIPANT nnnnnn Object
:Entity
1
METHOD
nnnnnn Plan
:MentalObject
(composed)1
INVOLVED-IN
nnnnnn
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
Fishery merged info access
Integrated Fishery Ontology (IFO)
FisheryOntology
Server(FOS)
Topic-BasedFisheryBrowser(TBFS)
Queryinterface
Results(documents)
Userquery
Results(specialised
info,terminological
equivalents,glosses, etc.)
oneFishTopicTrees
FIGISTaxonomies
AgroVocThesaurus
ASFAThesaurus