From Terminologies To Ontologies - Advances In Knowledge .
DynamOntMethodology for Dynamic Ontology CreationFrom Terminologiesto Ontologies – Advances inKnowledge OrganizationGerhard BudinUniversity of Vienna2007-07-01Terminology Summer SchoolFrom Terminologies to Ontologies – Tools ofKnowledge Organization Terminologies structured ( /-) collections ofconcepts and terms in a certain language in aspecific subject field Ontologies formal, explicit (conceptual) modelsof object ranges in a computationalrepresentation Differences and commonalities Methods of organizing knowledge (personal andcollective levels) Knowledge organization systems: all structuredterminology system: classifications, thesauri,taxonomies, nomenclatures – they can be„ontologized“1
Philosophical Foundations and HistoricalOrigins of Terminology Studies 17th and 18th centuries: Developing German as alanguage of science – Wolff Leibniz: ideal language of science Kant: constructionist concept theory 19th century: Bolzano, Hartmann, Brentano – Neo-Aristotelian EpistemologyPhilosophical Foundations and HistoricalOrigins of Terminology Studies – Foundations of Modern Ontology and Psychology Foundations of Modern Logic: Frege Early 20th century: Brentano’s school: Husserl, Meinong, Marty – philosophy of language and language theory Boltzmann, Mach, Carnap – Logical Positivism, Vienna Circle Bühler (semiotic language theory, new era of thought psychology) New wave of internationalist normative approaches to languages(planned languages, in particular Esperanto) M. Dewey: new approach to universal classification systems forindexing and retrieval in libraries and early documentation centers Industrialization Globalization – generic need for standardization Long history of lexicography – innovations such as Schlomann2
Knowledge Organization Processes of organizing knowledge– What concept of knowledge? (Process or result, implicit/explicit,knowledge, etc.) - Theories of knowledge– What concept of „organization“? (Process or result? – theories oforganization Psychological, cognitive concepts of knowledge (personal knowledge),concept theories, theories of categorization, prototype theory, etc. Linguistic theories (cognitive ling.), classification, computational ling. Cultural studies - cultural knowledge, social theories (sociology ofknowledge), organizing knowledge as a socio-economic process - knowledge management Pedagogical concepts of knowledge (learning and knowledgeacquisition), personal knowledge organization „epistemic-philosophical“ concepts of knowledge, systems theory– E.g. collective knowledge, knowledge as a result (Wissen vs.Erkenntnis!), objective knowledge (Karl Popper et al) – “logic ofscientific discovery”, evolutionary epistemology, etc. Information science, library science – knowledge organization systems Computer science – digital libraries, ontologies, knowledge engineering Convergence through a cognitive turn of philosophy of science?Knowledge (organization) systems Cognitive knowledge systems collective knowledge systems, cultural systems, socialsystems, language and communication systems Formal knowledge systems, knowledge representationsystems, “semantic systems” (Semantic Web) Applications:– Knowledge organization as part of knowledge management(Nonaka, Takeuchi, et al)– Knowledge organization as daily practice in libraries andinformation systems (for more than 2000 years)– Knowledge organization as formal representations in collectiveknowledge systems - Semantic Web applications3
What is knowledge organization?1.A part of information and library science, a part ofphilosophy of science and of epistemology, but also ofknowledge management and knowledge engineering 2.3.Investigating and representing structures of knowledgeEpistemological aspects, cognitive science aspectsLinguistic and socio-cultural aspects (e.g. folk taxonomies)Historical aspects (e.g. Leibniz, encyclopedism, administrativecategorizations in ancient societies, history of science, etc.)Practical work: creating and using knowledgeorganization systems (see further down)Knowledge organization is also a crucial process inlinguistic action (sprachliches Handeln) – Textorganization both in reception and productionTheoretical basis: systems theory Theory of social systems (e.g. Niklas Luhmann)– Sense/meaning as an axiomatic concept– communication as system, social expectations– Structure/event, reduction of social complextity Systems theory (control, intervention, social processes) by HelmutWillke– Point of departure for a theory of knowledge management Formal systems theory by Herbert Simon– Contributing to the foundations of Artificial Intelligence, Informatik Semiotic systems theories– Peirce, Cassirer, Eco– Communication as system (linguistic theories – Saussure, Chomsky,Halliday, etc.) Systems theories in cultural studies– Cassirer, Hansen, Sperber, etc. Systems theory in pedagogy, etc.4
Knowledge organization systems Covers all concept systems and terminologies used forordering and retrieving knowledge (knowledge units,artifacts, etc.), such as–––––––Classification systemsThesauriIndexing systemsTaxonomiesNomenclatures„Ontologies“Etc. .each having their own prototypical data models,purposes, traditions, but also many hybrid formsFunctions of knowledge organization systems1.2.3.4.5.6.7.Instruments of structuring and archiving the content oflarge scale collectionsStructural components of information systemsSupport of targeted retrieval of information based onconceptual search criteriaSearch aids, visual navigation, query languagesCommunication support tools (cross-lingual, crossdisciplinary, cross-cultural)Instruments of corporate knowledge managementLearning support, orientation support, didactic tools5
Properties of knowledge organization systems1.2.3.4.5.6.7.Conceptual structures (hierarchical and nonhierarchical structures)Explicitation of conceptual links, definitions (mono- ormultilingual)Terminological and linguistic standardizationIncreasingly formalized and digital (in particular as„ontologies“)Different scales (from small KOS to large ones (morethan 200.000 concepts)Increasingly with visualized structures, interactive userinterfacesStatic or dynamic (e.g. ontologies for modellingbusiness processes in companies)„Ontologies“ as formal knowledge systems Computer science: From Ontology as a traditional field ofphilosophy (theory of being, existence, theory of objects,etc.) to formal, digitally represented concept systems/knowledge systems Concepts are explicitly defined – terms are assigned Relations between concepts are explicitated Terms are standardized Logical application rules and constraints are specified Ontologies as knowledge representation systems6
Domain-specific knowledge organization systems Medicine, health, bio- and life sciencesBusiness, tradeIndustry, engineeringNatural sciencesAdministration, governmentCulturePedagogyLinguisticsEtc.General trends in knowledge organization Dynamization, flexibilizationNetworking, lti-functionalHybridizationFormalization, automationInternationalization7
Problem Description1.2.3.There is (still) a communication gap between formalizedknowledge representations such as ontologies and usersof information and communication systems, where suchontologies are used, also on user interfaces.Although the Semantic Web has been designed primarilyfor machine-to-machine-communication, we needseamless natural language interaction workflows in(semantic) web services of any kindWhile the Semantic Web is (still) essentially monolingualand the international lingua franca is English, there is agrowing need for multilingual ontology resources as wellas ontology-based translation services that overcomecommunication barriers arising from cultural-linguisticdifferences, lack of excellent command of English, needfor high precision in communication, etc.Need for integration of diverse methods As expressed in standards and implemented in technologies, thefollowing “traditions” increasingly merge:– Ontology engineering standards, frameworks, technologies e.g. OWL (based on RDF), SKOS (also on RDF) (W3C),DOLCE/SUMO, description logic, frame logic, unified logic, annotation Types of ontologies (e.g. domain o., upper o., application o., task o.) Editors such as Protégé, Altova, OntoEdit, div. merging/annotation tools– Translation engineering standards i.e. various paradigms in machine translation and computer-assistedtranslation (language-based, statistical MT, Transl. Memories, patterns)– Terminology and language engineering standards (as the pre-requisite forand interface between ontology and translation) Terminology and lexical markup frameworks: TMF, LMF (ISO) Markup languages such as TBX (language industry ISO) Lexical databases/ling.ontol: WordNet, Ontowordnet, EuroWordNet Linguistic enrichment of ontologies (e.g. FrameNet) Interaction mechanisms, translation of ontologies Integration of multilingual ontologies in machine translation processes8
Diversity and interoperability Strong diversity of lexico-terminological resources– Data models, data structures data semantics– Diversity of semantic, linguistic/cultural complexity and semanticdepth/richness Diversity of user groups and their requirements Sheer quantity of resources Data interchange between organizations (within andacross domains) as well as (distributed) data integration –early needs asking for immediate solutionsÆ History of data modeling History of interchange standards History of semantic interoperability managementNeed for multi-level modeling architectures9
roperabilityDeveloping the Terminology Markup Frameworkin order to cope with this complexity-diversity Based on empirical studies and practical user-drivenrequirements analysis Markup/representation/modeling: XML, XMLS, RDF, UML Open standards strategy (ISO TC 37)– ISO 12620 Data categories – meta-model element semanticsregistry (RDF)– ISO 16642 Terminology Markup Framework (TMF) – meta-modelarchitecture and specifications (UML)– ISO 12200 – Terminology Markup Language (XML) Instance for language industry: TBX Termbase Exchange Format(XML) Instance for lexicography/publishing: LexML ISO 1951––––Lexical Markup Framework (LMF) (UML)ISO 704 and ISO 1087 (foundational level)ISO 15188 (workflow and collaborative issues)Alignment with ISO 11179, W3C, OASIS, etc.10
Introduction to TBX TBX stands for TermBase eXchange TBX is a Terminological Markup Framework (TMF)markup language– TMF is an ISO standard (16642) TBX is consistent with ISO 12200 (MARTIF) TBX is maintained by OSCAR (www.lisa.org) The TBX specification is free Serving portability of resources across proprietaryterminology management systems, as well asinteroperability of application-specific resourcesTBX structure A TBX file is an XML document A TBX file consists of:– A header that describes the file– A set of entries, one per concept in the termbase– For each concept, a set of terms, grouped bylanguage, that designate the concept A terminological concept entry (termEntry)– Can be multilingual– Can be monolingual11
TBX and Other Standards (1) TBX and ISO 16642 (TMF) (2) TBX and ISO 12620 (Data Categories) (3) TBX and SKOS1: TBX and ISO 16642 TBX is a TML (Terminological Markup Language) ofTMF (ISO 16642) (see Annex B) TBX maps to the TMF meta-model– A TBX file is a TDC (terminological data collection)– martifHeader provides GI (global information)– termEntry: TE (terminological entry)– langSet: LS (language section)– tig/ntig: TS (term section) A TMF DCS (Data Category Selection) in TBX is in XCS(eXtensible Constraint Specification) format TBX uses ISO 12200 for its XML style12
TMF MetamodelTerminological Data Collection (TDC)GlobalInformation(GI)Terminological(Concept) age Section(s)(LS)Term Section(s)(TS)Term ComponentSection(s)(TCS)TMF and lexical resources In general, a terminological resource is organized intoconcept entries, each of which includes one or moreterms designating a particular concept In general, a lexical resource is organized into lexicalentries, each of which includes one or more senses of aparticular lexical item (a word or phrase) A concept entry containing multiple terms can be splitinto multiple lexical entries, one per term, and multiplelexical entries associated with the same concept can becombined into one concept entry Link to Lexical Markup Framework (LMF)13
2: TBX and ISO 12620 All data categories in the default TBX DCS are takenfrom ISO 12620 ISO 12620 is organized as an online registry and servesas a meta-ontology for resource modeling and forresource interoperability3: TBX and SKOS A typical concept entry will contain a subject fieldto specify the domain of the concept. However, the subject field is typically some kindof hierarchy that is flattened into a string withinTBX SKOS makes it possible to represent the subjectfield hierarchy as a hierarchy and then create alink within TBX14
Simple Knowledge OrganizationSystem (SKOS) “SKOS is an area of work developingspecifications and standards to support the useof knowledge organisation systems (KOS) suchas thesauri, classification schemes, subjectheading lists, taxonomies, other types ofcontrolled vocabulary, and perhaps alsoterminologies and glossaries, within theframework of the Semantic Web.”- http://www.w3.org/2004/02/skos/ (Accessed on 3/17/06)Sample SKOS skos:Concept rdf:about "#s71" skos:prefLabel Food /skos:prefLabel skos:narrower rdf:resource "#s81"/ skos:narrower rdf:resource "#s79"/ /skos:Concept skos:Concept rdf:about "#s81" skos:prefLabel Recipe Ingredient /skos:prefLabel skos:broader rdf:resource "#s71"/ /skos:Concept skos:Concept rdf:about "#s79" skos:prefLabel Restaurant Menu Item /skos:prefLabel skos:broader rdf:resource "#s71"/ /skos:Concept 15
Visual Representation of SKOSFoodRecipe IngredientAppetizerRestaurant Menu ItemEntreeSaladGrocery Store ItemHomemade ItemSoupMoving up (and down) the Ontology Spectrum The challenge: from linguistic-cultural diversity of discourse and freeform lexical structures to a unified, formalized, axiomatized ontology –and back, to support human understanding and social processes suchas collaborative learning The method: an integrative, multi-level modelling approach specifyingthe steps in a process-oriented workflow framework (with variable,combinable steps depending on concrete needs) for––––Gradual semantic enrichmentGradual semantic formalizationMulti- and cross-lingual referencing/alignment for text managementConstant interaction between full texts and lex-term resources The technology: a multi-component workbench (i.e. Dynamont-WBincl. ProTerm/Convera as a central element), using GRDDL, XML,RDF, OWL, SKOS, WordNet GlobalWordnet, MLIF (containing TBX,TMX, XLIFF, LMF, TMF, etc.), FrameNet, etc. The advantage: full exploitation of all types of languages resources(LR) and knowledge organization systems (KOS), providing aframework not only for their semantic enrichment and formalization asontologies but also for ontology-based multilingual authoring, textgeneration and translation16
An Integrated Process Component ModelIntellectual (source) text analysis and markupTMX/TBXautomatic (source) text analysisConveraterm-lex selection & descriptionTBX, WNtext chunking, storing, referencingTMX, FNMultilingual text alignmentTMXtranslation or ML authoringXLIFFsemantic enrichmentWN FN TBXKOS alignment enrichmentXML, SKOSML information object integrationMLIF, GRDDLOntology building, ML alignment, semantic enrichmentOWLA Multidimensional Meta-Model: Resource-Format MatrixParadigmatic axisResourcesFormatsDomain OntologiesThesaurus, ClassificationTerminology DatabasesWordNetTask ontologiesLexical DatabasesText corpora, Translation MemoriesXLIFFDCR, LEXUS-IMDIFrameNetMLIF, LMF, TMFXMDR, MOFDOLCE, SUMOOWLSKOSTBXXMLUMLTBX, LexML, XML,XML, TMXsyntagmatic axisXMLXML(S), RDFXML, OWLRDF, UMLXML, RDF, UMLOWL, RDF, XMLFrameworksFormats17
The global risk communication scenario Many projects since 1994 covering the following activities:––––––––––Thesaurus buildingCreating multilingual terminology databasesCreating multilingual text corporaLexicographical glossarySemantic enrichment (e.g. conceptual links, frame semantics)Collection and analysis of relevant knowledge organizationsystemsAnnotation of resourcesMark-up of resources (TBX, etc.)Ontology buildingCommunication designFrom texts and terminologies toontologies Using the Risk scenario– Termbase Export XML Domain Models – meta-models - patterns– Text corpus Term extraction – comparative testing ProTerm, MultiTermExtract, MultiCorpora Aligning with termbase Convert to RDF– Ontology import - editor– Mappings (GMT, XML, RDF, OWL, UML, comma delimited,RDB, for different kinds of lex-term resources, FN- OWL, etc.) The MULTH-WIN Project as an example of methodsintegration:18
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Terminological frame semantics INTERVENTION (ACTOR(S), ACTIVITIES/PHASES):RISK DETECTING (PRE-EVENT)R-ASSESSMENTR-PERCEPTION (X is risk)EXPERIENCE (statistics, case studies)OBSERVATION ATURESSITUATION/CONTEXT (danger/hazard)SIMULATION (course of events)PROBALISTIC METHODS (safety)RELIABILITYR-IDENTIFICATION (DAMAGE)R-SOURCEDAMAGE CAUSEVULNERABILITY (DAMAGE TARGET)SUSCEPTABILITY (capacity/people)RothkegelTerminological frame semanticsI. Pre-event B. Public awareness and planning, II. In-event: C.Events and responseafflux/Hochwasser durch AufstauBE [[TYPE flood], [PLACE ], [TIME ]],HAVE [CAUSE [[ORIGIN ], [NIEDERSCHLAG [TYPE ]], [STAU[TYPE Aufstau]]],DAMAGE [TARGET , SOURCE , DEGREE ]],HAPPEN [STATES , PROCESSES ]]backwater/RückstauBE [[TYPE flood], [PLACE ], [TIME ]],HAVE [CAUSE [[ORIGIN ], [NIEDERSCHLAG [TYPE ]], [STAU[TYPE Rückstau]]],DAMAGE [TARGET , SOURCE , DEGREE ]],HAPPEN [STATES , PROCESSES ]]Rothkegel21
Relationship raltypepercipitationhailrain„Stau“Aufstau afflux Rückstau backwater im Entwässerungssystem drainage flood Rothkegel22
Ordnance SurveyOrdnance Survey23
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Concept Relations - some typologies Domain approaches– UMLS– Biomedical ontologies– SNS– FAO Generic approachesIn terminological knowledge engineering27
UMLSBiomedical ontologies Barry Smith et al. OBO and related initiatives Three levels (binary relations):– class, class : for example, the is a relation obtaining betweenthe class SWR1 complex and the class chromatin remodelingcomplex, or between the class exocytosis and the classsecretion;– instance, class : for example, the relation instance ofobtaining between this particular vesicle membrane and theclass vesicle membrane, or between this particular instance ofmitosis and the class mitosis;– instance, instance : for example, the relation of instance-levelparthood (called part of in what follows), obtaining between thisparticular vesicle membrane and the endomembrane system inthe corresponding cell, or between this particular M phase ofsome mitotic cell cycle and the entire cell cycle of the particularcell involved.28
Continuants vs. Processes, classes vs.instancesC, C1, . to range over continuant classes;P, P1, . to range over process classes;c, c1, . to range over continuant instances;p, p1, . to range over process instances;r, r1, . to range over three-dimensional spatialregions;t, t1, . to range over instants of time.Primitive instance level c instance of C at t - a primitive relation between a continuant instance and a classwhich it instantiates at a specific timep instance of P - a primitive relation between a process instance and a class whichit instantiates holding independently of timec part of c1 at t - a primitive relation between two continuant instances and a time atwhich the one is part of the otherp part of p1, r part of r1 - a primitive relation of parthood, holding independently oftime, either between process instances (one a subprocess of the other), or betweenspatial regions (one a subregion of the other)c located in r at t - a primitive relation between a continuant instance, a spatialregion which it occupies, and a timer adjacent to r1 - a primitive relation of proximity between two disjoint continuantst earlier t1 - a primitive relation between two timesc derives from c1 - a primitive relation involving two distinct material continuants cand c1p has participant c at t - a primitive relation between a process, a continuant, and atimep has agent c at t - a primitive relation between a process, a continuant and a timeat which the continuant is causally active in the process29
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Anita Nuopponen Anita Nuopponen32
Anita Nuopponen Anita Nuopponen33
Anita Nuopponen Anita Nuopponen34
ESA - EO Ontology Approach Specify at high level the EO reality: EO Ontology Add classification vs. other domains: Multi-domain Ontology Derive a simplified, more abstract Classification Ontology– Supporting multiple domains– Providing equal visibility of all concepts– Using fixed concepts and relations– Permitting an implementation as an “isolate” Web Service with limited dependency from evolving reality / dynamicchanges Verify its applicability (also to non-EO domains)Sergio D’Elia, ESA35
EO ionactivationData / InformationflowData / DataSergio D’Elia, ESAMulti-domain tivationData / InformationflowThemeData / rmationDataSergio D’Elia, ESA36
Classification yServiceCategoryProductCategorySergio D’Elia, ESA37
TSS July 2007Gerhard BudinList of URLs (to be extended)Semantic Web - W3C - OWL:http://www.w3.org/2001/sw/DAMLincl. ontology libraryhttp://www.daml.org/Topic Maps:Metadata? Thesauri? Taxonomies? Topic sauri.htmlSUMO nphil.uni-tuebingen.de/lsd/Conceptual Graphshttp://conceptualgraphs.org/Unified Medical Language n.htmlConcept Maps CMAPhttp://cmap.ihmc.us/Topic Maps in XMLhttp://www.topicmaps.org/xtm/1.0/Roget’s Thesaurus online, visualized:http://ella.slis.indiana.edu/ jold/Roget2000/classes/roget122b.htmlGlobal Monitoring for Environment and Securityhttp://www.gmes.info/FrameNet Annotation htmlFormal Concept Documents/BackgroundAOS.htmlEnvironmental Terms EPAhttp://www.epa.gov/docs/OCEPAterms/Course on Conceptual Graphshttp://www.huminf.aau.dk/cg/Sowa Conceptual y Project e ontologies tro/Barry Smith website with many e Webhttp://knowledgeweb.semanticweb.org/Laboratory for applied ontologyhttp://www.loa-cnr.it/
A Formal Ontological Framework for SemanticInteroperability in the Fishery DomainAldo Gangemi1, Frehiwot Fisseha2, Ian Pettman3, Domenico M. Pisanelli1, MarcTaconet4, Johannes Keizer21Institute of Psychology, CNR (National Research Council), Rome, re.irmkant.rm.cnr.it2 FAO-GILW, Rome, p://www.fao.org3 One Fish, SIFAR, Grange-over-Sands, Cumbria, UKip@ceh.ac.ukhttp://www.onefish.org34 FIDI, FAO, Rome, t. This paper outlines a project (involving FAO, SIFAR, and CNR)aimed at building an ontology in the fishery domain. The ontology willsupport semantic interoperability among existing fishery informationsystems and will enhance information extraction and text marking,envisaging a fishery semantic web. The ontology is being built through theconceptual integration and merging of existing fishery terminologies,thesauri, reference tables, and topic trees. Integration and merging areshown to benefit from the methods and tools of formal ontology.1 INTRODUCTION1.1 The general problemSpecialized distributed systems are the reality of today’s information systemsarchitecture. Developing specialized information systems/resources in response tospecific user needs and/or area of specialization has its own advantage in fulfilling theinformation needs of target users. However, such systems usually use differentknowledge organization tools such as vocabularies, taxonomies and classificationsystems to manage and organize information. Although the practice of usingknowledge organization tools to support document tagging (thesaurus-basedindexing) and information retrieval (thesaurus-based search) improves the functions ofa particular information system, it is leading to the problem of integratinginformation from different sources due to lack of semantic interoperability thatexists among knowledge organization tools used in different information systems.
The different fishery information systems and portals that provide access tofishery information resources are one example of such scenario. This paperdemonstrates the proposed solution to solve the problem of information integration infishery information systems. The proposal shows how a fishery ontology thatintegrates the different thesauri and taxonomies in the fishery domain could help inintegrating information from different sources be it for a simple one-access portal or asophisticated web services application.1.2 The local scenarioFishery Ontology Service (FOS) is a key feature of the Enhanced OnlineMultilingual Fishery Thesaurus, a project aimed at information integration in thefishery domain. It undertakes the problem of accessing and/or integrating fisheryinformation that is already partly accessible from dedicated portals and other webservices.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 beintegrated are: the "reference tables" underlying the FIGIS portal [1], the ASFA onlinethesaurus [2], the fishery part of the AGROVOC online thesaurus [3], and theoneFish community directory [4].The official task of the project is "to achieve better indexing and retrieval ofinformation, and increased interaction and knowledge sharing within the fisherycommunity". The focus is therefore on tasks (indexing, retrieval, and sharing ofmainly documentary resources) that involve recognising an internal structure in thecontent of texts (documents, web sites, etc.). Within the semantic web communityand the intelligent information integration research area (cf. [5] and [6]), it isbecoming widely accepted that content capturing, integration, and managementrequire the development of detailed, formal ontologies.In this paper we sketch an outline of the FOS development and some hint ofthe functionalities that it carries out.2 ONTOLOGY INTEGRATION AND MERGING2.1 Heterogeneous systems give heterogenous interpretationsAn example of how formal ontologies can be relevant for fishery informationservices is shown by the information that someone could get if interested inaquaculture.In fact, beyond simple keyword-based searching, searches based on taggedcontent or sophisticated natural-language techniques require some conceptualstructuring of the linguistic content of texts. The four systems concerned by thisproject provide this structure in very different ways and with different conceptual
’textures’. For example, the AGROVOC and ASFA thesauri put aquaculture in thecontext of different thesaurus hierarchies; an excerpt of the AGROVOC result is (ufmeans used for, NT means narrower than; rt means related term, Fr and Es are thecorresponding French and Spanish terms):AQUACULTUREuf aquicultureuf maricultureuf sea ranchingNT1 fish cultureNT2 fish feedingNT1 frog culturert agripisciculturert aquaculture equipmentFr aquacultureEs acuiculturaThe AGROVOC thesaurus seems to frame aquaculture from the viewpoint oftechniques and species. On the other hand, the ASFA aquaculture hierarchy issubstantially different:AQUACULTUREuf Aquaculture industryuf Aquatic agricultureuf AquicultureNT Brackishwater aquacultureNT Freshwater aquacultureNT Marine aquaculturert Aquaculture developmentrt Aquaculture economicsrt Aquaculture engineeringrt Aquaculture facilitiesActually this hierarchy seems to stress the environment and disciplines related toaquaculture.A different resource is constituted by the so-called reference tables in FIGISsystem; the only reference table mentioning aquaculture puts it into another context(taxonomical species):Biological entityTaxonomic entityMajor groupOrderFamilyGenusSpeciesCapture species (filter)Aquaculture species (filter)Production species (filter)
Tuna atlas specThe last resource examined is oneFish directory, which returns the followingcontext (related to economics and planning):SUBJECTAquacultureAquaculture developmentAquaculture economics @Aquaculture planningWith such different interpretations of aquaculture, we can reasonably expectdifferent search and indexing results. Nevertheless, our approach to in
- E.g. collective knowledge, knowledge as a result (Wissen vs. Erkenntnis!), objective knowledge (Karl Popper et al) - "logic of scientific discovery", evolutionary epistemology, etc. Information science, library science - knowledge organization systems Computer science - digital libraries, ontologies, knowledge engineering
Composite Domain Ontology Engineering Problem Existing research into ontology engineering focuses on: Creating new ontologies Aligning existing, redundant ontologies Interacting with ontologies using software tools No real effort spent on high-level engineering of cohesive ontologies Goal Develop a methodology for linking and re-purposing ontologies in
ontologies. These ontologies, along with many others that sit outside the OBO foundry, provide a set of 'building blocks' for building new application specific ontologies [5]. Re-using modules from existing ontolo-gies to build larger and more complex compositional ontologies lowers the cost of development and mainte-nance.
developing ontologies. This section presents methods and methodologies for developing ontologies from two perspectives: (1) Building ontology from scratch, and (2) Building ontologies from existing ontologies or from different data sources In what follows a set of approaches related to the first perspective are introduced. In 1995
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Fonseca, F. and J. Martin (2007) "Learning the Differences Between Ontologies and Conceptual Schemas Through Ontology-Driven Information Systems," JAIS - Journal of the Association for Information Systems - Special Issue on Ontologies in the Context of IS Volume 8, Issue 2, Article 3, pp. 129–142, February 2007 – preprint version
quality. They also act as semantic bridges supporting interoperability between domain ontologies [15,13,18]. There are two approaches for the use of foundational ontologies [28]. With a top-down approach, the foundational ontology is used as a reference for deriving domain concepts, tak
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and Materials, ASTM; and by the Society of Automotive Engineers, SAE. These groups are summarized as follows: 100 Series (C10000) Coppers This group comprises the pure coppers, those with a designated mini-mum copper content of 99.3%, for high electrical conductivity. Also included within this group are the high copper alloys, those with
