=Paper=
{{Paper
|id=Vol-64/paper-3
|storemode=property
|title=A Formal Ontological Framework for Semantic Interoperability in the Fishery Domain
|pdfUrl=https://ceur-ws.org/Vol-64/gangemi.pdf
|volume=Vol-64
|authors=Aldo Gangemi,Frehiwot Fisseha,Ian Pettman,Domenico,M.Pisanelli,Marc Taconet,Johannes Keizer
}}
==A Formal Ontological Framework for Semantic Interoperability in the Fishery Domain==
https://ceur-ws.org/Vol-64/gangemi.pdf
A Formal Ontological Framework for Semantic
Interoperability in the Fishery Domain
Aldo Gangemi1, Frehiwot Fisseha2, Ian Pettman3, Domenico M. Pisanelli1, Marc
Taconet4, Johannes Keizer2
1
Institute of Psychology, CNR (National Research Council), Rome, Italy
{gangemi,pisanelli}@ip.rm.cnr.it
http://saussure.irmkant.rm.cnr.it
2 FAO-GILW, Rome, Italy
{Frehiwot.Fisseha,Johannes.Keizer}@fao.org
http://www.fao.org
3 One Fish, SIFAR, Grange-over-Sands, Cumbria, UK
ip@ceh.ac.uk
http://www.onefish.org3
4 FIDI, FAO, Rome, Italy
Marc.Taconet@fao.org
http://www.fao.org
Abstract. This paper outlines a project (involving FAO, SIFAR, and CNR)
aimed at building an ontology in 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. The ontology is being built through the
conceptual integration and merging of existing fishery terminologies,
thesauri, reference tables, and topic trees. Integration and merging are
shown to benefit from the methods and tools of formal ontology.
1 INTRODUCTION
1.1 The general problem
Specialized distributed systems are the reality of today’s information systems
architecture. Developing specialized information systems/resources in response to
specific user needs and/or area of specialization has its own advantage in fulfilling the
information needs of target users. However, such systems usually use different
knowledge organization tools such as vocabularies, taxonomies and classification
systems to manage and organize information. Although the practice of using
knowledge organization tools to support document tagging (thesaurus-based
indexing) and information retrieval (thesaurus-based search) improves the functions of
a particular information system, it is leading to the problem of integrating
information from different sources due to lack of semantic interoperability that
exists among knowledge organization tools used in different information systems.
The different fishery information systems and portals that provide access to
fishery information resources are one example of such scenario. This paper
demonstrates the proposed solution to solve the problem of information integration in
fishery information systems. The proposal shows how a fishery ontology that
integrates the different thesauri and taxonomies in the fishery domain could help in
integrating information from different sources be it for a simple one-access portal or a
sophisticated web services application.
1.2 The local scenario
Fishery Ontology Service (FOS) is a key feature of the Enhanced Online
Multilingual Fishery Thesaurus, a project aimed at information integration in the
fishery domain. It undertakes the problem of accessing and/or integrating fishery
information that is already partly accessible from dedicated portals and other web
services.
The organisations involved in the project are: FAO Fisheries Department
(FIGIS), ASFA Secretariat, FAO WAICENT (GIL), the oneFish service of SIFAR,
and the Ontology and Conceptual Modelling Group at ISTC-CNR. The systems to be
integrated are: the "reference tables" underlying the FIGIS portal [1], the ASFA online
thesaurus [2], the fishery part of the AGROVOC online thesaurus [3], and the
oneFish community directory [4].
The official task of the project is "to achieve better indexing and retrieval of
information, and increased interaction and knowledge sharing within the fishery
community". The focus is therefore on tasks (indexing, retrieval, and sharing of
mainly documentary resources) that involve recognising an internal structure in the
content of texts (documents, web sites, etc.). Within the semantic web community
and the intelligent information integration research area (cf. [5] and [6]), it is
becoming widely accepted that content capturing, integration, and management
require the development of detailed, formal ontologies.
In this paper we sketch an outline of the FOS development and some hint of
the functionalities that it carries out.
2 ONTOLOGY INTEGRATION AND MERGING
2.1 Heterogeneous systems give heterogenous interpretations
An example of how formal ontologies can be relevant for fishery information
services is shown by the information that someone could get if interested in
aquaculture.
In fact, beyond simple keyword-based searching, searches based on tagged
content or sophisticated natural-language techniques require some conceptual
structuring of the linguistic content of texts. The four systems concerned by this
project provide this structure in very different ways and with different conceptual
’textures’. For example, the AGROVOC and ASFA thesauri put aquaculture in the
context of different thesaurus hierarchies; an excerpt of the AGROVOC result is (uf
means used for, NT means narrower than; rt means related term, Fr and Es are the
corresponding French and Spanish terms):
AQUACULTURE
uf aquiculture
uf mariculture
uf sea ranching
NT1 fish culture
NT2 fish feeding
NT1 frog culture
rt agripisciculture
rt aquaculture equipment
Fr aquaculture
Es acuicultura
The AGROVOC thesaurus seems to frame aquaculture from the viewpoint of
techniques and species. On the other hand, the ASFA aquaculture hierarchy is
substantially different:
AQUACULTURE
uf Aquaculture industry
uf Aquatic agriculture
uf Aquiculture
NT Brackishwater aquaculture
NT Freshwater aquaculture
NT Marine aquaculture
rt Aquaculture development
rt Aquaculture economics
rt Aquaculture engineering
rt Aquaculture facilities
Actually this hierarchy seems to stress the environment and disciplines related to
aquaculture.
A different resource is constituted by the so-called reference tables in FIGIS
system; the only reference table mentioning aquaculture puts it into another context
(taxonomical species):
Biological entity
Taxonomic entity
Major group
Order
Family
Genus
Species
Capture species (filter)
Aquaculture species (filter)
Production species (filter)
Tuna atlas spec
The last resource examined is oneFish directory, which returns the following
context (related to economics and planning):
SUBJECT
Aquaculture
Aquaculture development
Aquaculture economics @
Aquaculture planning
With such different interpretations of aquaculture, we can reasonably expect
different search and indexing results. Nevertheless, our approach to information
integration and ontology building is not that of creating a homogeneous system in
the sense of a reduced freedom of interpretation, but in the sense of navigating
alternative interpretations, querying alternative systems, and conceiving alternative
contexts of use.
To do this, we require a comprehensive set of ontologies that are designed in
a way that admits the existence of many possible pathways among concepts under a
common conceptual framework. This framework should reuse domain-independent
components, be flexible enough, and be focused on the main reasoning schemas for
the domain at hand.
Domain-independent, upper ontologies should characterise all the general
notions needed to talk about economics, biological species, fish production
techniques; for example: parts, agents, attribute, aggregates, activities, plans,
devices, species, regions of space or time, etc. While the so-called core ontologies
should characterise the main conceptual habits (schemas) that fishery people actually
use, namely that certain plans govern certain activities involving certain devices
applied to the capturing or production of a certain fish species in certain areas of water
regions, etc.
Upper and core ontologies [7,8] provide the framework to integrate in a
meaningful and intersubjective way different views on the same domain, such as
those represented by the queries that can be done to an information system.
2.2 Methods applied to develop the integrated fishery ontology
Once made clear that different fishery information systems provide different
views on the domain, we directly enter the paradigm of ontology integration, namely
the integration of schemas that are arbitrary logical theories, and hence can have
multiple models (as opposed to database schemas that have only one model) [9]. As a
matter of fact, thesauri, topic trees and reference tables used in the systems to be
integrated could be considered as informal schemas conceived to query semi-formal or
informal databases such as texts and tagged documents.
In order to benefit from the ontology integration framework, we must
transform informal schemas into formal ones. In other words, thesauri and other
terminology management resources must be transformed into (formal) ontologies.
To perform this task, we apply the techniques of three methodologies:
OntoClean [8], ONIONS [10], and OnTopic [11].
The first one contains principles for building and using upper ontologies for
core and domain ontology analysis, revision, and development. In its current form,
OntoClean also features an axiomatised domain-independent top-level of formal
criteria, concepts and relations (Figure 3) [18].
ONIONS is a set of methods for enhancing the informal data of
terminological resources to the status of formal ontological data types. Some methods
are aimed at reusing the structure of hierarchies (e.g., BT/NT relations, subtopic
relation, etc.), the additional relations that can be found (e.g., RT relations), and at
analysing the compositional structure of terms in order to capture new relations and
definitional elements. Other methods concern the management of semantic
mismatches between alternative or overlapping ontologies, and the exploitation of
systematic polysemy to discover relevant domain conceptual structures.
OnTopic is about creating dependencies between topic hierarchies and
ontologies. It contains methods for deriving the elements of an ontology that describe
a given topic, and methods to build ’active’ topics that are defined according to the
dependency of any individual, concept, or relation in an ontology.
In Figure 1, a class diagram is shown of the informal and formal data types
taken into account by the forementioned methodologies.
In section 3.1 the types of (meta)data extracted from the resources are
described. In the subsequent sections the (meta)data types obtained from the
transformation of resources into a merged ontology are also described.
We briefly describe:
• the resources that are integrated
• how the Integrated Fishery Ontology (IFO) is being built
• a mediation architecture to interface the fishery ontology service with the
source information systems.
3 OUTLINE OF THE FOS PROJECT
3.1 Resources
The following resources have been singled out from the fishery information
systems considered in the project:
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 hierarchy (average depth: 3) is ordered by (at least)
two different relations: subtopic, and intersection between topics, the last being
notated with @, similarly to relations found in known subject directories like
DMOZ. There is one ’backbone’ tree consisting of five disjoint categories, called
worldviews (subjects, ecosystem, geography, species, administration) and one
worldview (stakeholder), maintained by the users of the community, containing own
topics and topics that are also contained in the first four other categories (Figure 5).
Alternative trees contain new ’conjunct’ topics deriving from the intersection of topics
belonging to different categories.
Resource for ontology development
1 HAS-PART
Source Processed namespace Ontology element Ontological structure
Topic tree
Inclusion hierarchies 1 1
HAS-MEMBER
Glossary Concepts namespace 1 n
Concept n HAS-MEMBER 1
Taxonomy
Upper ontology Documentation
Set of axioms
OntologicalStructure
Thesaurus Topics namespace
1HAS-MEMBERn 1
BT,NT,RT informal axioms
Topic Lexical item
EXTRACTED-FROM Set of assertions
Informal domain ontology n
InformalAxioms 1
Relations namespace HAS-MEMBER
1 n
Relation HAS-MEMBER
HAS-MEMBER
Domain schema (conceptual template)
(Informal) axioms 1
1HAS-MEMBERn Individual HAS-MEMBER
Individuals namespace
Set of lexical items
Fishery resource types::Ontological structure n
as reusable component n n
Axiom
EXTRACTED-FROM
Reusable component from original n
n
Assertion
Library of modules
Documentation BT/NT hierarchy Informal ontology fragment
Topic tree fragment RT informal axioms
Fig. 1. A class diagram of the source data types taken into account
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.
ASFA thesaurus, similar to AGROVOC, but with about 10,000 descriptors.
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.
There are modules (water areas, continental areas, biological entities, vessels,
commodities, stocks, etc.), also organised by ’views’.
In Figure 2 a diagram is sketched of the methodology used to extract and
refine the informal data from the fishery information systems. The methodology is
also described in the next sections.
3.2 Translation and refining of the components for IFO building
The (meta)data from the resources that have been singled out have been
processed, in order to integrate them within a homogeneous environment, and with a
clear assessment of their nature. In the following we list a set of guidelines that have
been followed to translate and refine data components:
• A detailed evaluation of each source (find the schema -explicit or not- underlying
the implementation of source data, then describe each data type both qualitatively
and quantitatively) is performed.
• A language to represent the KB is chosen that hosts the integration activity. A
description logic like DLR [9] is an ideal choice for its compatibility with the
ontology integration framework.
• An ontology server is installed that supports DLR or compatible languages.
• Some data types from the sources (Figure 1) seem appropriate to be included in a
preliminary prototype. The following steps are performed on them:
• Discuss, refine and formalise FIGIS fishery conceptual schemas [12] to build
a preliminary core ontology. Also the upper-level concepts from the source
thesauri should be matched against the FIGIS conceptual schemas. This
results in a resource for core ontology development.
• Translate FIGIS reference tables: taxonomy, individuals, and local relations
(to be transformed into formal axioms). This results in a draft resource for
domain ontology development.
• Reuse oneFish topic trees to design a preliminary architecture for IFO
library. This architecture should match the preliminary core ontology. This
results in a resource for ontology library design.
Domain conceived
exit/ Resources selected
Resources described
entry/ Domain resources collected
do: Use a classification scheme from an ontology of resources
exit/ Resources classified
Resource processing packages created Reusable components from resources identified
entry/ Resources classified entry/ Homogeneous resource set defined
do: Define activities to be done do: Analyse resource schemas
exit/ Homogeneous resource set defined exit/ Reusable components identified
Rough list of ontology elements ready
entry/ Homogeneous resource set defined, reusable components identified
do: Collect all namespaces (concepts,relations,individuals,topics) from resources,
start assigning data types, documentation and terms collected
exit/ Rough namespaces created with flags to resources
Topic trees translated
Documentation translated entry/ Topic resources defined [oneFish]
entry/ Domain documentation resources defined [all] do: Translate resources to common format
Core ontologies translated
do: Translate resources to common format. Trace origin exit/ Preliminary topic trees formalised
entry/ Core ontology resources defined [FIGIS, top ASFA, top Agrovoc, else]
exit/ DOC resources formalised
do: Translate core resources to common format
exit/ Preliminary core ontology formalised BT/NT hierarchies translated
Lexical sets translated entry/ Domain BT/NT resources defined [ASFA,Agrovoc]
do: Translate resources to common format
entry/ Lexical resources defined [all]
exit/ BT/NT resources formalised
do: Translate resources to common format. Trace origin Domain ontologies translated
exit/ Lexicalisation resources formalised
entry/ Domain ontology resources defined [FIGIS]
do: Translate resources to common format
BT/NT hierarchies refined
exit/ Domain ontology resources formalised
RT axioms translated entry/ BT/NT resources formalised
do: Refine with heuristics based on core ontologies
entry/ Domain RT resources defined [ASFA,Agrovoc]
exit/ Refined subset of BT/NT hierarchies ready
do: Translate resources to common format
exit/ RT resources formalised
Taxonomical resources ready
RT resources refined
entry/ Domain ontologies translated, BT/NT hierarchies refined
entry/ RT resources formalised do: Prepare integration space of taxonomies
do: Refine with heuristics based on taxonomies and core ontologies exit/ Taxonomies to be integrated
exit/ Refined subset of RT axioms ready
Topic trees refined
entry/ Preliminary topic trees formalised
Axiomatic resources ready
do: Refine trees according to set-theoretic principles
entry/ Domain ontologies translated, RT axioms refined exit/ Refined topic trees ready
do: Prepare integration space of axioms
exit/ Axioms to be integrated
Assertional resources ready
entry/ Domain ontologies translated, BT/NT hierarchies refined, RT resources refined,
DOC and lexicalisation resources formalised
do: Prepare integration space of assertions
exit/ Assertions to be integrated
List of integratable ontology elements ready
entry/ Taxonomical, axiomatic, and assertional resources ready, refined topic trees ready
do: Create working namespaces with flags to original resources, maintain links between current resources
exit/ Working, interlinked namespaces created with flags to resources
Fig. 2. A diagram of the methodology used to extract and refine the informal data
• Extract IS_A taxonomies from AGROVOC and ASFA BT/NT (Broader
Term/Narrower Term) hierarchies. Heuristics from upper and core ontologies
can be applied to clean up BT/NT hierarchies, for example, the following
rule can be applied: if a body part descriptor is NT of an organism
descriptor, then this is probably not an IS_A use of NT (probably it is a
part-of relation). This results in resources for core and domain taxonomies
building.
• Expand RT (Related Term) relations from AGROVOC and ASFA. Also
non-IS_A BT/NT hierarchies could be refined (expanded) here. Heuristics can
be applied here as well, for example, if there exists a systematic relation
between to concepts in the core ontology, and there exists a RT relations
between two subconcepts of those concepts, then this is an indication for that
relation to be the refinement of the RT one. This results in resources for core
and domain axioms building.
• Reuse UF (Used For) relations and (multi-)linguistic equivalents from all
resources. Track must be kept of the context from which a linguistic item
has been extracted. This results in resources for ontology lexicalisation.
3.3 Parallel tasks
In the following sections we outline the main steps to build the basic
taxonomy, documentation, and architecture for the integrated fishery ontology.
3.3.1 Developing a fishery core ontology (FCO)
In this step, we pick up uppermost concepts and conceptual (categorisation)
schemas from sources and integrate them with a certified top-level containing
domain-independent concepts, relations and meta-properties. The resources needed for
such a task are:
Upper ontology resources: the OntoClean upper level [8,18] (Figure 3) is a
preferential choice for its compatibility with the methodology. For alternatives, see
[13]. Moreover, various formal ontologies and standards for relations, and general
lexical repositories like WordNet [14].
Core ontology resources: conceptual templates, (selected in the preliminary
phases), relational database schemas, theoretical views on domain topics, domain
standards, etc. An informal fishery core ontology (the FIGIS composite concepts) is
shown in Figure 4.
In the context of core ontology development, some taxonomical branches
(core concepts) have relevant conceptual integration issues that are being studied by
ontological engineers and domain experts in close collaboration:
• biological taxonomies: difficult having a stable framework of reference (in
principle, mapping from local taxonomies to a biological one is feasible, but
in practice it could be not cost effective)
• geographic regions: use GIS as a stable framework of reference? geographic
names?
• institutions: maybe automatic clustering of individuals through classification
• fishing devices (including vessels)
• fishing and fish farming techniques (plans and activity types)
• farming systems (sets of components)
• fishery regulations (norms)
• fishery managament systems (plans)
• production centers
Quality
Quality Region
Aggregate
Amount of matter
Arbitrary collection
Object
Physical Object
Body
Ordinary object
Mental Object
Feature
Relevant part
Place
Occurrence
State
Process
Accomplishment
Abstract
Fig. 3. The OntoClean top concepts
Development is performed as incremental loading and classification of upper
and core level ontologies in the Ontology Server.
Another indirect resource that can be exploited to build the core ontology is
the analysis of systematic polysemies (they have been already used in the mining of
large medical thesauri, cf.[10]). A systematic polysemy is discovered when a relation
exists between two senses of a term, and this relation is relevant for the domain that
is being analysed. Consequently, if we find many polysemies with senses that have
been conceptualised within the same concept pairs, this is an indication for a possible
core ontology relation.
3.3.2 Building domain IS-A taxonomies
This phase deals with the integration of the resources for domain ontology
development with the fishery core ontology (developed in the previous phase).
Resulting taxonomies could be either ’tolerated’ or ’cleaned up’. Tolerance
amounts to have widespread and unexplained polysemy for terms, but it is not time
consuming. Cleaning is the most time consuming task, since a frequent scenario is
the following: concept C from source S1 (C^S1) is in principle similar to a D^S2
(usually because they share one or more terms), but they actually occupy two
taxonomical places that make them disjoint according to the upper or core ontology.
The ONIONS methodology [10] in this case suggests to axiomatise their
glosses (cf. ⁄3.2.3, 3.3.3) and to check if their taxonomical position is correct. If it is
not, then they are probably polysemous senses of the same term, and some alternative
methods can be applied to relate those senses, to merge them, or to accept the
conceptual split of the senses.
Some cleaning will be needed in any case to remove at least the major
taxonomical clashes. This results into a domain taxonomy. Additional effort should
be dedicated to distinguish:
Concepts vs individuals (heuristics applicable: country names, institutions,
etc.).
Backbone concepts vs viewpoint concepts (roles, reified properties,
contingent notions), cf. [7,8].
This eventually results into a refined domain taxonomy.
Fig. 4. The FIGIS composite concepts, used as a resource for core ontology development.
3.3.3 Collecting existing documentation and producing glosses
Available resources for ontology documentation are collected and associated
as a kind of annotation (gloss) to domain concepts. Concepts lacking a gloss require a
new one.
For core concepts and relations, besides existing glosses, an extensive
description of their scope in the FCO is provided.
3.3.4 Designing a preliminary topic architecture
A preliminary topology for most general topics (to be used for ontology
modularisation as well) is figured out. Here the following resources are reused:
ontologies for topics (Welty s topic topology [15], topic maps standard [16],
OnTopic principles [11]), semantic portals design [17], oneFish topic trees.
Administration
Subjects Ecosystem
Stakeholders
Geography Species
Fig. 5. Topic spaces ("worldviews") in oneFish.
Representation
ontology
Upper
ontology
Core
Biological ontology Legal
Devices ontology ontology Management
ontology ontology
Domain ontologies
Institutions Species Geographic
ontology ontology ontology
Fishing and
Fishing farming Farming Fishery Fishery
devices techniques systems regulations management
ontology ontology ontology ontology
ontology
Fig. 6. An example architecture for the fishery ontology library. Double frames mean
external ontologies.
The topic topology will be used both for maintaining the ontology library and for
managing text indexing and retrieval. Figure 5 shows how the current topic spaces of
oneFish are structured. Figure 6 shows an ontology-based architecture for the
Integrated Fishery Ontology.
3.4 Building domain axioms
Once taxonomies are cleaned to a certain extent, documented, and divided
into appropriate namespaces, activities aimed at raising the conceptual detail of the
ontology can be started. The most important is the characterisation of domain
concepts with axioms. In order to realise this, domain resources containing informal
relationships, and (at least some) glosses from documentation are upgraded to the
status of logical axioms.
Informal relationships can be found in thesauri (e.g. related term) as well as
reference tables and topic trees. They are mined in order to understand:
1) if the axioms are applicable to all the subconcepts of the concept to
which the axiom pertain, and
2) what quantification is applicable to those axioms: existential (necessary)
or universal (contingent)?
This results into formal Domain Axioms. This axiom set is enhanced by
axiomatising glosses. Here the ONIONS methodology [10] is applied to derive
formal domain axioms from natural language descriptions. The typical technique
consists in extracting terms, parsing them according to a dependency grammar, and
applying core and upper ontologies to assign concepts and relations to the resulting
dependency trees.
This activity is time-consuming, and semi-automatic techniques are still a
research issue [13]. Scalability and approximate results are considered here.
The axioms obtained from informal relationships and glosses are revised
according to the fishery core ontology developed so far.
3.5 Modularising ontology library according to topics
Following OnTopic methodology [11], dependency chains of core concepts
are automatically generated and the existing preliminary topic topology is checked in
order to produce a first version of the ontology library architecture. Dependency
chains are also applied to derive indexing tags and boolean search spaces.
A dependency chain is the transitive closure of the logical depend-ons of a
concept. The transitive closure is applied to the defining elements of a concept. Here a
set of relevance parameters are applied in order to
3.6 Providing multi-lingual lexicalisation to elements in the ontology library
An integrated fishery ontology benefits from the existence of terms already
related to concepts in the original resources, since they semi-automatically provide the
so-called lexicalisation of concepts. On the other hand, having an integrated ontology
also provides a powerful tool to check polysemous senses of terms, as well as to
check consistency of UF thesaurus relations and consistency of multi-lingual
equivalents.
3.7 A unified architecture
Figure 7 shows a simplified example architecture to support information
brokering [6] or unified search after merging of fishery information systems by means
of Fishery Ontology Service.
Results
(specialised
Results info,
(documents) terminological
equivalents,
glosses, etc.)
Topic-Based Fishery
Fishery Ontology
Browser Server
(TBFS) (FOS)
Integrated Fishery Ontology (IFO)
Query
interface AgroVoc FIGIS oneFish ASFA
Topic
Thesaurus Taxonomies Thesaurus
Trees
User
query
Fig. 7. A unified interface for interoperability after merging heterogeneous terminological
resources in fishery.
The basic idea is that user queries, through a query interface, can be
submitted to two kinds of servers: if the query aims at retrieving documents, a topic-
based fishery agent rewrites the query in order to submit it to heterogeneous databases
(brokering); if the query aims at finding specialised conceptual or terminological
information, it is directed to the Fishery Ontology Server (FOS). In both cases, the
query interface uses FOS. Query rewriting needs also mapping relations from the
integrated fishery ontology to the source thesauri.
CONCLUSIONS
In this paper we have outlined some research solutions within the framework
of ontology integration that are based on formal upper and core ontologies. Some
details have been given on how informal schemata such as thesauri, reference tables,
and topic trees can be reused and refined in order to be manipulated by ontology
integration. Some hints have also been shown about the dependence of topic trees
from ontologies, a promising research area for the semantic web.
In fact, the overall research issue underlying the FOS project is to provide a
unified methodology of ontology integration and merging based on formal
ontologies, ontology library design, topic trees building and maintainance, and
efficient web search and indexing.
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