Project proposal for a fishery ontology service

Project Proposal for a Fishery Ontology Service Aldo Gangemi CNR-ISTC Institute of Cognitive Sciences and Technologies [email_address]

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

Ontologies constrain intended meaning Conceptualization C Language L Models M(L) Ontology Intended models I K (L)

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 in FIGIS (composites) Aquaculture Resource Water Area land strains Species life 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 Ontologies banana, organic lettuce, rose Middle and Core* Ontologies plant*, crop*, fishery*, law, ship Upper Ontologies object, event, part, precedes, shape Representation Ontologies concept, slot, instance, role, function

Fishery ontology library Domain ontologies Representation ontology Upper ontology Core ontology Geographic ontology Species ontology Institutions ontology Fishing devices ontology Fishing and farming techniques ontology Farming systems ontology Fishery regulations ontology Fishery management ontology Biological ontology Devices ontology Legal ontology 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

The core ontology for capture fishery

The core ontology for aquaculture

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 n n n n n n Object :Entity 1 METHOD n n n n n n Plan :MentalObject (composed) 1 INVOLVED-IN n n n n n n

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

Project proposal for a fishery ontology service

by AIMS (Agricultural Information Management Standards) FAO of the UN

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