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Disentangling in the ontology forest The Fishery Ontology Service Project

Disentangling in the ontology forest The Fishery Ontology Service Project. Aldo Gangemi a.gangemi@istc.cnr.it LOA-ISTC-CNR, Via Nomentana 56, Roma, Italy. Summary. Presentation of the Laboratory for Applied Ontology

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Disentangling in the ontology forest The Fishery Ontology Service Project

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  1. Disentangling in the ontology forestThe Fishery Ontology Service Project Aldo Gangemi a.gangemi@istc.cnr.it LOA-ISTC-CNR, Via Nomentana 56, Roma, Italy

  2. Summary • Presentation of the Laboratory for Applied Ontology • Basic primer on formal ontology distinctions, design patterns and techniques (link to other tutorial) • Methodology used in the FOS project • Some implemented ontology-driven services for fishery

  3. What is LOA?

  4. What is LOA? • Laboratory_for_Applied_Ontology • -partOf- Institute_of_Cognitive_Sciences_and_Technology • -partOf- Italian_National_Research_Council • -memberNumerosity- 125 (70 staff) • -location- {Roma, Trento, Padova} • -head- Cristiano_Castelfranchi • -memberNumerosity- 13 • -location- {Roma, Trento} • -head- Nicola_Guarino • -researchTopic- {axiomatic_theories, foundational_ontologies, domain_ontologies, lexical_ontologies, ontology_development}

  5. Selected ongoing Projects • IKF : Intelligent Knowledge Fusion (Eureka Project) Ontology of banking transactions (with ELSAG Banklab) Ontology of Service-Level Agreement and IS monitoring (with SELESTA) Ontology of Insurance Services (with Nomos SpA) • WonderWeb (FP5): Ontology Infrastructure for the Semantic Web (LOA: foundational ontologies for the Semantic Web, ontology of information, ontology of services) • OntoWeb (FP5 - NoE): Ontology-based information exchange for knowledge management and electronic commerce (LOA: SIG on Content Standards) • FOS (with UN-FAO): Alignment of legacy fishery terminologies, ontology-based query expansion • Metokis (FP6 - Strep): Middleware between domain knowledge and user task-oriented applications (LOA: Task modelling ontology for the communication of knowledge objects) • SEMANTIC MINING (FP6 - NoE): Semantic Interoperability and Data Mining in Biomedicine • TICCA (PAT&CNR): Interaction and cooperation with artificial agents (LOA: ontology of social interaction) 

  6. Collaborations • IBM Italia • Nomos Spa • Selesta Spa • Elsag Spa • Libero/Wind • UN-FAO • Boeing Inc • IBM Watson Research Centre • Ontology Works • Language & Computing • U. of Trento, Padova, Roma, Torino, Verona • ITC-IRST • ITTIG-CNR, ILC-CNR • History of Science Museum, Florence • IRIT-CNRS • IFOMIS • Columbia University • U. of Manchester, Leeds • U. of Amsterdam, VUA • Berlin-Brandenburgische Akademie der Wissenshaften • U. of Leipzig, Bremen, Hamburg • European Media Lab, Heidelberg • U. of Salzburg • U. of Queensland • ICS-Forth, Heraklion • UPM

  7. Research topics • Logical tools for ontological analysis and development • Domain ontologies • Physical objects and artifacts • Information and information processing • Social interaction • Legal and financial entities • Biomedical entities • Services and planning • Fishery and agriculture • Cultural heritage • Ontology in language and cognition • Lexical ontologies • Ontology-driven information systems • Conceptual modeling • Information access • Information integration

  8. Basic primer on formal ontology distinctions, tools and techniques Link to other tutorial!

  9. ONIONS@FOS Methodology and lifecycle Shown through a UML activity diagram (divided into five pieces for readability)

  10. Lifting and datamodel creation/integration

  11. Ontologies by precision game(x)  activity(x) athletic game(x)  game(x) court game(x)  athletic game(x)  y. played_in(x,y)  court(y) tennis(x)  court game(x) double fault(x)  fault(x)  y. part_of(x,y)  tennis(y) game athletic game court game tennis outdoor game field game football tennis football game field game court game athletic game outdoor game Axiomatized theory Taxonomy game NT athletic game NT court game RT court NT tennis RT double fault Glossary DB/OO scheme Catalog Thesaurus Ontological precision precision: the ability to catch/provide all and only the intended meaning (for a logical theory, to be satisfied by intended models)

  12. AQUACULTURE (AGROVOC) NT1 fish culture NT2 fish feeding NT1 frog culture … rt agripisciculture rt aquaculture equipment … Fr aquaculture Es acuicultura AQUACULTURE (ASFA) NT Brackishwater aquaculture NT Freshwater aquaculture NT Marine aquaculture rt Aquaculture development rt Aquaculture economics rt Aquaculture engineering rt Aquaculture facilities Biological entity (FIGIS) Taxonomic entity Major group Order Family Genus Species Capture species (filter) Aquaculture species (filter) Production species (filter) Tuna atlas spec SUBJECT (OneFish) Aquaculture Aquaculture development Aquaculture economics @ Aquaculture planning Legacy aquaculture hierarchies from fishery terminology systems

  13. Heterogeneous datatypes in legacy FIGIS DTDs

  14. Sample data model analysis/conversion in FOS Term ≠ Concept Term  String Concept = Class BT ≈ subsumption between classes RT ≈ top-level conceptual relation {Descriptors} = {Classes},{Individuals} Individual  Class Concept ≠ Subject/Topic/Domain

  15. Formalization and Core Ontology Creation

  16. Datamodel mapping and its effects on translation • agrovoc_schema:Descriptor • agrovoc:River • agrovoc:Amazon  • owl:Class(agrovoc:River) • owl:Individual(agrovoc:Amazon(rdf:type agrovoc:River))

  17. Datamodel mapping and required transformations • agrovoc:RT • agrovoc_schema:Descriptor • agrovoc:Fishing_vessel • agrovoc:Fishing_gear • agrovoc:Fishing_vessel,RT,Fishing_gear  • Class(agrovoc:Fishing_vessel partial (restriction(agrovoc:RT someValuesFrom(agrovoc:Fishing_gear))))

  18. Core Fishery Ontology – 2:DOLCE+ top

  19. Going practical: Generic Use Cases and Ontology Design Patterns • Foundational ontologies are applied • Sample Generic Use Cases: • who does what, when and where? • what are the parts of sth? • what is it made of? • what’s the place of sth? • what’s the time frame of sth? • how can you do what you do? • does my behaviour conform to that rule? • what’s the function of that artifact? • how is it built? • how did that phenomenon happen? • what’s your role in that affair? • is my scheduling compatible with yours?

  20. This talk: using UML class diagrams • Non-standard use of UML to visualize ODPs: • generalisation  subsumption (“subClassOf”) • association  two-way conceptual relation (“property”) • attribute  one-way conceptual relation (“property”) • assuming reasoning capabilities with classification and role chaining • association with no cardinality: 0..* • realization  instantiation • role  non-rigid property

  21. Core Fishery Ontology – 3:Fishery Technique Ontology Design Pattern

  22. Modularization and alignment

  23. Minimal case of alignment dolce+:physical_object foundational cof:Fishing_vessel core asfa:Trawlers domain = rdfs:subClassOf

  24. Realistic layers in fishery ontology alignment Fishery domain ontologies – “roof” and “floors” OntoWordNet fragments – “posts” Fishery core ontology – “walls” DOLCE foundational ontology – “ground” The “toyhouse”

  25. Alignment method • Does the class to be aligned (or a proximal one) exist in COF? if so, align it, e.g.: • Class(figis:Marine_fishes partial cof:Aquatic_organism) • Otherwise: • Does the class exist in WordNet? if so, include the OntoWordNet fragment related to that class into the library, e.g.: • Class(figis:Aquatic_resource partial own:Asset) • Otherwise: • Do the experts think the class is very relevant for fishery, or the class has a rich taxonomy under it? if so, create a small taxonomy that sketches a new core ontology for that domain, e.g. by adapting them from OntoWordNet (e.g. vehicles, equipments) or from existing ones (biomedicine) • Otherwise: • Align the class directly under a generic class in the foundational layer, e.g.: • Class(figis:Country partial dolce+:Political_Geographic_Object).

  26. Consistency issues • From ASFA formalization, we get that: • Class(asfa:Trap_fishing partial asfa:Catching_methods) • Class(asfa:Trap_fishing partial asfa:Fishing) • From the alignment, we know that (transitively): • Class(asfa:Catching_methods partial dolce+:Object), while Class(asfa:Fishing partial dolce+:Activity) • And from DOLCE+ we know that: • (disjointClasses dolce+:Object dolce+:Activity) • Hence the intersection: • Class(asfa:Trap_fishing partial (intersection asfa:Catching_methods asfa:Fishing)) • is inconsistent

  27. BT polysemy issues • From AGROVOC we know: • Class(agrovoc:Blood_Cells partial agrovoc:Blood) • From a biomedical ontology(ON9.3) we know: • Class(agrovoc:Blood_Cells partial on9:Cellular_structure) • Class(agrovoc:Blood partial on9:Tissue) • (DisjointClasses on9:Tissue on9:Cellular_structure) • Class(on9:Tissue partial • (restriction(dolce+:constituent someValuesFrom(on9:Cellular_structure)))) • Therefore, on the basis of ON9, we can suggest that • Class(agrovoc:Blood_Cells partial agrovoc:Blood) • is a polysemous use of the BT relations (a cellular structure cannot be a tissue, because the two classes are disjoint), therefore it is possibly the case that: • Class(agrovoc:Blood_Cells partial • (restriction(dolce+:constituent someValuesFrom(agrovoc:Blood))))

  28. Refinement, annotation, and merging

  29. ReLearning procedure • Given RT relations and axioms from reference ontologies: • Class(asfa:Trawlers partial (restriction(asfa:RT someValuesFrom(asfa:Pelagic_fisheries)))) • Class(own:Instrumentality partial (restriction(dolce+instrument_for someValuesFrom(dolce+:Activity)))) • and we know transitively that: • Class(asfa:Trawlers partial own:Instrumentality) • Class(asfa:Pelagic_fisheries partial cof:Fishing_activity) • then we can infer that: • Class(asfa:Trawlers partial (restriction(dolce+instrument_for someValuesFrom(asfa:Pelagic_fisheries))))

  30. Annotations • Domain lexicon (synonyms and multilingual labels) • Subject trees (topics) • Identification codes (unique identitifers) • Comments (glosses, scope notes, examples, etc.)

  31. Merging • Homonymic merging: • agrovoc:Trawlers • figis:Trawlers • asfa:Trawlers •  fos_vessel:Trawler • Heterogeneous taxonomic position (emergent polysemy): • Class(agrovoc:Dredgers partial agrovoc:Ships) • Class(asfa:Dredgers partial asfa:Work_platforms) • Expert’s merging (emergent synonymy): • asfa:Ships • figis:Non-fishing_vessels

  32. Post-processing lifecycle

  33. FOS Ontology Library Figures • 35828 domain classes have been integrated in the library • 272 classes and 164 properties populate the DOLCE-Lite-Plus foundational ontology, with about 1200 axioms • 809 classes populate the top part of OntoWordNet resulting from the alignment of WordNet to DOLCE+ • 170 classes and 48 properties populate the COF, with about 650 axioms • 154 classes have been reused from OntoWordNet in order to align ASFA and AGROVOC to DOLCE+ (for the parts not covered by COF) • 22274 domain classes have been aligned (13554 classes not yet aligned come from the non-fishery part of AGROVOC, which have been included in the integration because have some RT relations with the fishery part). • 12700 domain classes have been merged • 9944 domain individuals have been aligned • 4700 domain individuals have been merged.

  34. Some implemented ontology-driven services for fishery A few slides. There exists an exhaustive presentation by Margherita Sini (FAO-GILW)

  35. Query taxonomy (derived from expert and user requirement) • Document search • Survey search • Regulation search • Fact sheet search • Within-document search • Instance search • Individual search • Value search • Fact search • Concept search • Object type search • Activity type search • Method/Technique search • Data search • Database query • Multiple-database query • Within-ontology search

  36. Mock-up for querying distributed dynamic data "tell me what vessels from a nearby country are currently in the marine area 50N060W within Atlantic Ocean, provided that also some Thunnus alalunga stock can be fished by those vessels, through allowed techniques" (retrieve ?ves (exists (?type ?tech ?spec ?cou ?stock) (and (instance-of ?ves ?type) (superrelations ?type Fishing-Vessel) (superrelations ?tech Fishing-Technique) (has-depend-ons ?type ?tech) (has-depend-ons ?tech ?spec) (subrelations ?spec |Thunnus alalunga|) (instance-of ?cou Country) (instance-of ?stock Aquatic-Stock) (FLAG-STATE ?ves ?cou) (WEAK-CONNECTION ?cou |Atlantic Ocean@fig|) (OCEANIC-POSITION ?stock |50N060W@fig|) (OCEANIC-POSITION ?ves |50N060W@fig|) (fail (has-depend-ons ?tech |Drifting longlines@fig|)) (RESOURCE-SPECIES ?stock |Thunnus alalunga|)))) -> (|i||Atlantic Enterprise II|) Blue: core concept types Pale blue: domain concept types Red: individuals Dark red: individual(s) found

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