Project Proposal for a Fishery Ontology Service

Aldo Gangemi

CNR-ISTC

Institute of Cognitive Sciences and Technologies

gangemi@ip.rm.cnr.it

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