Fishery Ontology Service Exploratory Project

Aldo Gangemi*Domenico M. Pisanelli*Daniele Cerboneschi*

Frehiwot Fisseha (FAO-GILW)Ian Pettman (OneFish/FAO)

*CNR-ISTCLaboratory for Applied Ontology

http://ontology.ip.rm.cnr.it

Summary

• Aim and methods of FOS project• Sources, tools, and work done• Uses of foundational ontologies• Some fishery conceptual schemas• Demo (not in this section)• Exploitation scenarios

Aim of the project

To build a preliminary version of a core ontology for the fishery domain.  The ontology will support semantic interoperability among existing fishery information systems and will enhance information extraction and text marking, envisaging a fishery semantic web.

Methods

The ontology is being built through the conceptual re-engineering, integration and merging of existing fishery terminologies, thesauri, reference tables,

DTDs, and topic trees. Integration and merging are shown to benefit from the methods and tools of

formal ontology.

Ontological engineering

OntoDevelop Component

Data

<- dbs<- docs<- forms<- disaggr data

Logical Language

EXPRESSED ACC TO

DB artifact

Terminology

Format

REFERENCE

MetaDataTARGET-OF

<- agreed models

PRODUCT

Aggregated data

<- selected docs<- parts of docs<- catalogs<- views<- matchings<- novel patterns

External app

APIPARTICIPANT

INSTRUMENT

Ontology exploitationUSED-FOR

REFERENCE

INVOLVESMethodology

NLP technique

Ontology component

Ontologies

INSTR

PRODUCT

INSTRUMENT

Ontological procedure

Tool

TARGET-OF

Different uses of ontologies

• Reference ontologies (development time)– establish consensus about meaning of terms (in general)– higher expressivity (non-stringent computational reqs.): task to be

undertaken only once for cooperation process types

• Application ontologies (run time)– offer terminological services for semantic access, checking

constraints between terms– limited expressivity (stringent computational reqs.)– can be derived from reference ontologies

• Mutual understanding more important than mass interoperability– understanding disagreements in the context of common criteria– establish trustable mappings among application ontologies

Conceptual tools from OntoLab

• DOLCE Foundational Ontology, a set of cognitively motivated categories to support domain analysis.

• The OntoClean methodology and meta-properties [Guarino et al., 2002], currently implemented in many toolkits for ontology development, provides means to remodel existing ontologies by separating their backbone, stable taxonomy, from accessory hierarchies.

• The ONIONS methodology [Gangemi et al., 1999], provides guidelines to analyze and merge existing ontologies, and addresses the reengineering of domain terminologies. It commits to an integration of linguistic, conceptual, and contextual categories.

• The OnionLeaves library is a library containing plug-ins (so-called conceptual templates) to the DOLCE foundational ontology that have been customized by starting e.g. from systematic polysemy evidence [Gangemi et al. 2000]. Currently, it includes plug-ins for plans, semiotic relations, spatial location relations, functional participation relations.

FOS sources

the oneFish topic trees (about 1,800 topics), made up of hierarchical topics with brief summaries, identity codes and attached knowledge objects (documents, web sites, various metadata);the AGROVOC thesaurus (about 500 fishery-related descriptors), with thesaurus relations (narrower term, related term, used for) among descriptors, lexical relations among terms, terminological multilingual equivalents, and glosses (scope notes) for some of them;the ASFA thesaurus, similar to AGROVOC, consisting in about 10,000 descriptors;the FIGIS reference tables, with 100 to 200 top-level concepts, with a max depth of 4, and about 30,000 'objects' (mixed concepts and individuals), relations (specialised for each top category, but scarcely instantiated) and multilingual support. the FIGIS DTDs, with 823 elements and a rich attribute structure

Existing “ontologies”

• Controlled terminologies or axiomatic theories?

• Terminologies need re-engineering– Low detail (e.g. DAML DB, …)– Low formalization (e.g. thesauri, …)– Inexplicable or non-explicit distinctions (e.g.

bottom-up domain specifications)

• Heterogeneity– How to negotiate, integrate, merge?

Methodology types

• Linguistic ontology development– lexicographic treatment of domain terminologies

• Community ontology development– negotiating an intersubjective agreement among the

members of a community of interest

• Cognitive ontology development– axiomatic theories and cognitive invariants to be

used in performing domain analysis

Basic activities in FOS

Catalog building

PRECEDESPRECEDESPRECEDES OntologyMerging

Wrapping

TerminologyRe-

engineering

Formatting Union Mapping

Interfacing

Exploitation

Matching

DiscoveryConsistency checking

Formalization

ConceptualIntegration

Analysis

Importing

DescriptorsTermsRelationsScope notesSubjectsIdentifiersCodesDB specific links

ConceptsRelationsAxiomsRulesLexicalizationAnnotations

The world of conceptual modeling

continent

country

material_place

name

extension

name

population

entity

relationship

attribute

(1,2)

(m,n)

The world of relational tables

name extensionAfrica …. Europe …. Asia …. America …. Oceania ….

name populationItaly …Germany …Turkey …Spain …Libia …… …

name_Country name_ContinentItaly EuropeGermany EuropeTurkey EuropeTurkey AsiaLibia Africa… …

continent-table country-table

material-place-table

Ontological layers

INHERITS-FROM

Domain ontology

INCL: kinase, cAMP pathway, Drosophyla, Phosphorylation, Adenylyl cyclase

Core ontology (specific domain-independent)

INCL: Biological process, Enzyme, Factor, Function, Substance, Protein, Pathway

Foundational ontology (domain-independent)

INCL: Object, Process, Part, Time, Location, Representation, Plan

INHERITS-FROM

Foundation Ontology

FOS core

FOS integrated FOS merged

FIGIS Reference Tables ASFA

FIGIS DTD

ONE FISH

AGROVOC

Integration themes

• Lexical normalization available for free by reusing thesauri (refinement needed?)

• Documentation inherited from sources• Agrovoc potentially needs less effort than ASFA, but its

fishery descriptors are “entrenched” in the thesaurus and required top-level subjects (“domains”) to be extracted

• One Fish to be linked after a complete fishery ontology is available, since it is constituted by subject hierarchies

Merging

now merging FIGIS and ASFA started machine ontology learning on ASFA

and FIGIS DTDs- ASFA multihierarchies sometimes inconsistent- ASFA RT heuristics started (next slide)- Possible synergy with OntoLearn

started FIGIS DTD semantics analysis, in order to get semantic interoperability with FIGIS XML resources

TARGET°PLAYED-BY°MEMBER

PLACE

Ontology learning from RT relationships

PLAYED-BY°MEMBER

Aquatic organismPLACE

TARGET

Aquatic resource

Habitat

Environment

Aquaculture

FOS Core

Freshwater organism

RT

RTRT

Freshwater ecology

Inland water environmentFreshwater aquaculture

ASFA(draft domain ontology from reengineered descriptors)

Compatibility

Currently implemented in Loom translation to other languages started

– Loom to KIF or Ontolingua available– Loom to FaCT, DAML+OIL, RDFS built by us– once in some web language, the Fishery

Ontology can be used for Semantic Web applications

DOLCE• DOLCE (Descriptive Ontology for Linguistic and Cognitive

Engineering) foundational ontology [Masolo et al., 2002]:– currently includes about 200 domain-independent concepts and

relations with a rich axiomatic characterization.• Necessary axioms for concepts• Ground axioms and cross-relational axioms for relations

– is a cognitively-oriented ontology, based on primitive space and time, 3-dimensional intuition (objects are disjoint from processes), distinction between conceptual and perceptual qualities, physical and intentional objects, etc.

– is a descriptive (as opposed to prescriptive) ontology, because it helps categorizing an already formed conceptualization.

– Download site of first deliverable: • http://ontology.ip.rm.cnr.it/research/

11,nPHYSICAL-LOCATION

0,n0,n0,n0,n0,n0,n

0,nFED-WITH1,n1,n1,n1,n1,n1,n

Aquatic organism

PLACETARGET-OFAGESUBSTRATE-OFPREYED-UPON-BYFED-WITHPHYSICAL-LOCATIONTEMPORARY-COMPONENTINFORMAL-DESCRIPTION

Behaviour

Physical RegionLength

Size

Shape

Reproduction

Weight

Water Condition

Growth Fecundity

Mortality

Sex

Color

Body Part

Scales

Fins

Gills

Habitat

Description String

3,nTEMPORARY-COMPONENT

0,n0,n0,n0,n0,n0,n

Time Interval

Fishery

0,n

TARGET-OF

1,n1,n1,n1,n1,n1,n

0,n SUBSTRATE-OF1,n1,n1,n1,n1,n1,n

0,n

INFORMAL-DESCRIPTION

0,n0,n0,n0,n0,n0,n

0,n AGE 111111

0,n

PLACE

1,n1,n1,n1,n1,n1,n

0,nPLACE1,n1,n1,n1,n1,n1,nGeographic Object

Food

A view from FCO

T-PART

1,n PARTICIPANT

0,n0,n0,n0,n0,n0,n

0,n

Q-LOCATION

0,n0,n0,n0,n0,n0,n

0,n

Q-LOCATION

0,n0,n0,n0,n0,n0,n

0,n

HAS-QUALITY

0,n0,n0,n0,n0,n0,n

0,n

HAS-QUALITY

0,n0,n0,n0,n0,n0,n

0,n

Q-LOCATION

0,n0,n0,n0,n0,n0,n

0,n

HAS-QUALITY

0,n0,n0,n0,n0,n0,n

Quality

Region

Endurant

Time-IntervalSpace-Region

AbstractRegion TemporalRegionPhysicalRegion

TemporalQualityAbstractQualityPhysicalQuality

PerdurantNonPhysicalEndurantPhysicalEndurant

DOLCE Top-Level

Incoherence detection:formalized ASFA BTs

(DEFCONCEPT |Trap fishing@asfa|:IS-PRIMITIVE (:AND |Catching methods@asfa|

|Fishing@asfa|))

Incoherence detection:inherited axioms

(defconcept |Trap fishing@asfa| :is-primitive (:and Asfa-Domain^|Fishing@asfa| Asfa-Domain^|Catching methods@asfa| (:some Descriptions^Encompasses Fos-Core^Fishing-Zone) (:some Descriptions^Encompasses Fos-Core^Aquatic-Resource) (:some Descriptions^Encompasses Fos-Core^Aquatic-Organism) (:some Descriptions^Encompasses Fos-Core^Gear) (:some Descriptions^Encompasses Fos-Core^Vessel) (:some Plans^Method-Of Fos-Core^Fishery) (:some F-Participation^Product Fos-Core^Commodity) (:some Dolce^Duration Dolce^Time-Interval) (:some Dolce^Temporal-Location Fos-Core^Fishing-Season) (:some F-Participation^Instrument Fos-Core^Vessel) (:some F-Participation^Instrument Fos-Core^Gear) (:some F-Participation^Instrument Everyday^Device) (:some F-Participation^Has-Target Fos-Core^Aquatic-Resource) (:some F-Participation^Has-Target Fos-Core^Aquatic-Organism) (:some Places^Participant-Place Fos-Core^Fishing-Zone) (:some Plans^Has-Method Fos-Core^Fishing-Technique) (:some Descriptions^Referenced-By Fos-Core^Fishing-Regulation) (:some Fos-Core^Managed-By Fos-Core^Management-Method)))

Incoherence detection:incoherence reason

? (print-concept-outline '|Catching methods@asfa| :direction :up)

|Catching methods@asfa|: FISHING-TECHNIQUE: : TECHNIQUE: : : Method: : : : S-DESCRIPTION: : : : : DESCRIPTION: : : : : : NON-PHYSICAL-ENDURANT: : : : : : : ENDURANT: : : : : : : : ENTITY: : : : : : : : : THING

? (print-concept-outline '|Fishing@asfa| :direction :up)

|Fishing@asfa|: CAPTURE-FISHERY: : FISHERY: : : ACTIVITY: : : : Action: : : : : ACCOMPLISHMENT: : : : : : EVENT: : : : : : : PERDURANT: : : : : : : : ENTITY: : : : : : : : : THING

Incoherence detection:other annotations

(defconcept |Trap fishing@asfa| :characteristics (:closed-world :incoherent) :annotations ( (INCOHERENCE-REASON "Concept |C|DDO::|Trap fishing@asfa| is a

member of two or more disjoint partition classes, i.e., it is incoherent. Partition: DDO::$ENTITIES$, Disjoint classes: (|C|

DDO::PERDURANT |C|DDO::ENDURANT)") (RT (THE-RELATION '|Bait@asfa| 1)) (RT (THE-RELATION '|Bait fishing@asfa| 1)) (RT (THE-RELATION '|Crab fisheries@asfa| 1)) (RT (THE-RELATION '|Gastropod fisheries@asfa| 1)) (RT (THE-RELATION '|Lobster fisheries@asfa| 1)) (RT (THE-RELATION '|Trap nets@asfa| 1))) :context Asfa-Domain)

Incoherence detection:effects to ontolearning

Given that (RT (THE-RELATION '|Crab fisheries@asfa| 1))

for |Trap fishing@asfa|

it can be learnt the following axiom:

(:some METHOD-OF |Crab fisheries@asfa|) (as technique)

or the following one:

(:some PART-OF |Crab fisheries@asfa|) (as fishery)

T-PART

1,n PARTICIPANT

0,n0,n0,n0,n0,n0,n

0,n

Q-LOCATION

0,n0,n0,n0,n0,n0,n

0,n

Q-LOCATION

0,n0,n0,n0,n0,n0,n

0,n

HAS-QUALITY

0,n0,n0,n0,n0,n0,n

0,n

HAS-QUALITY

0,n0,n0,n0,n0,n0,n

0,n

Q-LOCATION

0,n0,n0,n0,n0,n0,n

0,n

HAS-QUALITY

0,n0,n0,n0,n0,n0,n

Quality

Region

Endurant

Time-IntervalSpace-Region

AbstractRegion TemporalRegionPhysicalRegion

TemporalQualityAbstractQualityPhysicalQuality

PerdurantNonPhysicalEndurantPhysicalEndurant

DOLCE Top-Level

Quality regions in FOS

Abstract regionTemporal regionPhysical region

ABSTRACT-REGION : DISTRIBUTION-STATUS : ECONOMIC-CLASS : ECONOMIC-GROUP : EXPLOITATION-STATUS : MONETARY-VALUE : TRADE-VOLUME-REGION : TREND-STATUS

TEMPORAL-REGION : TIME-INTERVAL : : DATE : : FISHING-SEASON

PHYSICAL-REGION : BODY-SHAPE-REGION : COLOR-REGION : FECUNDITY-VALUE : LENGTH-REGION : MORTALITY-VALUE : POWER-REGION : : GRT-CATEGORY-RANGE : : GRT-GROUP-RANGE : : POWER-CLASS-RANGE : REPRODUCTIVE-VALUE : SALINITY-RANGE : SEX-VALUE : SPACE-REGION : : BATHYMETRIC-REGION : : DEPTH-REGION : : LATITUDE-RANGE : : RANGE-OF-ACTION : : SHORE-DISTANCE-REGION : : SIZE-VALUE : : SPATIO-TEMPORAL-REGION : : : GROWTH-REGION : VOLUME : WATER-CONDITION-VALUE : WEIGHT-REGION

Basic Relations• Parthood

– Between quality regions, or btw perdurants (immediate)– Between arbitrary objects (temporary)

• Connection, Succession• Dependence

– Specific/generic constant dependence• Constitution• Inherence (between a quality and an entity)• Q-Location

– Between a quality and its region (immediate, for unchanging ent)

– Between a quality and its region (temporary, for changing ent)

• Participation (btw a perdurant and an endurant)• Reference (btw a description and a situation)

Descriptions and Situations Template (context-based view)

Situation

S-Description

CourseF-RoleParameter

PerdurantEndurantRegion

VALUED-BY PLAYED-BY SEQUENCESREFERENCES

REQUISITE-FOR

LOCATION-OF

MODALITY-TARGET

PARTICIPANT-IN

0,n PLAYED-BY

*FOOD STUFF

1,n1,n1,n

*FOOD STUFF

1,n1,n1,nSubstanceFood Role

Roles and descriptions

0,nPLAYED-BY

*COMMODITY FISH0,n0,n0,n *COMMODITY FISH0,n0,n0,n

Aquatic Organism

0,n PLAYED-BY

*FISHERY ARTICLE0,n0,n0,n *FISHERY ARTICLE0,n0,n0,n

Fishery Product

F Commodity

Roles and descriptions (2)

OR

Fishery D&S Schema ex.

PCP

0,n

SEQUENCES

1,n1,n1,n1,n1,n1,n

Impact SituationRoutes, Action sequences, Routines, etc.

0,n

VALUED-BY

0,n0,n0,n0,n0,n0,n

0,n

PLAYED-BY

1,n1,n1,n1,n1,n1,n

0,n

METHOD-OF

1,n1,n1,n1,n1,n1,n

0,n0,n0,n

0,n

ENVISAGES

0,n0,n0,n

Persons, Aquatic organisms, Gears, Vessels, Fishery grounds, Water areas, etc.

Crew, Aquatic resources, Zones, Artifact roles, etc.

Exploitation indicator, Budget, Amounts needed, etc.

Season, Crew numerosity, Exploitation data, Monetary values, etc.

Quality Region Fishery SituationFishery Activities and PhenomenaFishery Objects

Fishery TechniqueFishery ScheduleFishery RoleFishery Parameter

Aquaculture, Aggressive behaviour, Frog culture,Ice fishing

Underwater exploitation,Overfishing

Catching method,Two boat operated purse seine

Exploitation

• Enhanced navigation, user profiles• Unified query system• Supporting new information

services– Discovering novel patterns– DTD modelling– Meaning negotiation