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Fishery Ontology Service  Exploratory Project Aldo Gangemi* Domenico M. Pisanelli* Daniele Cerboneschi* Frehiwot Fisseha (...
Summary <ul><li>Aim and methods of FOS project </li></ul><ul><li>Sources, tools, and work done </li></ul><ul><li>Uses of f...
Aim of the project To build a preliminary version of a core ontology for the fishery domain.    The ontology will support ...
Methods The ontology is being built through the conceptual  re-engineering,   integration  and  merging  of existing fishe...
Ontological engineering OntoDevelop Component Data <- dbs <- docs <- forms <- disaggr data Logical Language EXPRESSED ACC ...
Different uses of ontologies <ul><li>Reference ontologies ( development time ) </li></ul><ul><ul><li>establish consensus  ...
Conceptual tools from OntoLab <ul><li>DOLCE  Foundational Ontology, a set of cognitively motivated categories to support d...
FOS sources the  oneFish  topic trees (about 1,800 topics), made up of  hierarchical topics  with brief summaries, identit...
Existing “ontologies” <ul><li>Controlled terminologies or axiomatic theories? </li></ul><ul><li>Terminologies need re-engi...
Methodology types <ul><li>Linguistic ontology development </li></ul><ul><ul><li>lexicographic treatment of domain terminol...
Basic activities in FOS Catalog building PRECEDES PRECEDES PRECEDES Ontology Merging Wrapping Terminology Re-engineering F...
The world of conceptual modeling continent country material_place name extension name population entity relationship attri...
The world of relational tables continent-table country-table material-place-table
Ontological layers
Foundation Ontology FOS core FOS integrated FOS merged FIGIS Reference Tables ASFA FIGIS DTD ONE FISH AGROVOC
Integration themes <ul><li>Lexical normalization  available for free by reusing thesauri (refinement needed?) </li></ul><u...
Merging <ul><li> now  merging  FIGIS and ASFA </li></ul><ul><li> started  machine ontology learning  on ASFA and FIGIS D...
Ontology learning from RT relationships TARGET°PLAYED-BY°MEMBER PLACE PLAYED-BY°MEMBER Aquatic organism PLACE TARGET Aquat...
Compatibility <ul><li>Currently implemented in Loom </li></ul><ul><li>translation to other languages started </li></ul><ul...
DOLCE <ul><li>DOLCE (Descriptive Ontology for Linguistic and Cognitive Engineering) foundational ontology [Masolo et al., ...
A view from FCO
DOLCE Top-Level
Incoherence detection: formalized ASFA BTs <ul><li>(DEFCONCEPT  |Trap  fishing@asfa| </li></ul><ul><ul><li>:IS-PRIMITIVE (...
Incoherence detection: inherited axioms <ul><li>(defconcept  |Trap fishing@asfa| </li></ul><ul><li>:is-primitive (:and Asf...
Incoherence detection: incoherence reason <ul><li>? (print-concept-outline '|Catching methods@asfa| :direction :up) </li><...
Incoherence detection: other annotations <ul><li>(defconcept |Trap fishing@asfa| </li></ul><ul><li>:characteristics (:clos...
Incoherence detection: effects to ontolearning <ul><li>Given that   (RT (THE-RELATION '|Crab fisheries@asfa| 1)) </li></ul...
DOLCE Top-Level
Quality regions in FOS
Basic Relations <ul><li>Parthood </li></ul><ul><ul><li>Between quality regions, or btw perdurants (immediate) </li></ul></...
Descriptions and Situations Template  (context-based view)
Descriptions and Situations Template 0,n Functional Role Endurant Region Parameter T-COMP D-TOPOLOGY T-TOPOLOGY LOCATION-O...
Roles and descriptions
Roles and descriptions (2) 0,n PLAYED-BY *COMMODITY FISH 0,n 0,n 0,n *COMMODITY FISH 0,n 0,n 0,n Aquatic Organism 0,n PLAY...
Fishery D&S Schema ex. PCP 0,n SEQUENCES 1,n 1,n 1,n 1,n 1,n 1,n Impact Situation Routes, Action  sequences,  Routines, et...
Exploitation <ul><li>Enhanced navigation, user profiles </li></ul><ul><li>Unified query system </li></ul><ul><li>Supportin...
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Laboratory for applied ontology

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Laboratory for applied ontology

  1. 2. Fishery Ontology Service Exploratory Project Aldo Gangemi* Domenico M. Pisanelli* Daniele Cerboneschi* Frehiwot Fisseha (FAO-GILW) Ian Pettman (OneFish/FAO) *CNR-ISTC Laboratory for Applied Ontology http://ontology.ip.rm.cnr.it
  2. 3. Summary <ul><li>Aim and methods of FOS project </li></ul><ul><li>Sources, tools, and work done </li></ul><ul><li>Uses of foundational ontologies </li></ul><ul><li>Some fishery conceptual schemas </li></ul><ul><li>Demo (not in this section) </li></ul><ul><li>Exploitation scenarios </li></ul>
  3. 4. 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.
  4. 5. 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.
  5. 6. Ontological engineering OntoDevelop Component Data <- dbs <- docs <- forms <- disaggr data Logical Language EXPRESSED ACC TO DB artifact Terminology Format REFERENCE MetaData TARGET-OF <- agreed models PRODUCT Aggregated data <- selected docs <- parts of docs <- catalogs <- views <- matchings <- novel patterns External app API PARTICIPANT INSTRUMENT Ontology exploitation USED-FOR REFERENCE INVOLVES Methodology NLP technique Ontology component Ontologies INSTR PRODUCT INSTRUMENT Ontological procedure Tool TARGET-OF
  6. 7. Different uses of ontologies <ul><li>Reference ontologies ( development time ) </li></ul><ul><ul><li>establish consensus about meaning of terms (in general) </li></ul></ul><ul><ul><li>higher expressivity (non-stringent computational reqs.): task to be undertaken only once for cooperation process types </li></ul></ul><ul><li>Application ontologies ( run time ) </li></ul><ul><ul><li>offer terminological services for semantic access , checking constraints between terms </li></ul></ul><ul><ul><li>limited expressivity (stringent computational reqs.) </li></ul></ul><ul><ul><li>can be derived from reference ontologies </li></ul></ul><ul><li>Mutual understanding more important than mass interoperability </li></ul><ul><ul><li>understanding disagreements in the context of common criteria </li></ul></ul><ul><ul><li>establish trustable mappings among application ontologies </li></ul></ul>
  7. 8. Conceptual tools from OntoLab <ul><li>DOLCE Foundational Ontology, a set of cognitively motivated categories to support domain analysis. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul>
  8. 9. 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
  9. 10. Existing “ontologies” <ul><li>Controlled terminologies or axiomatic theories? </li></ul><ul><li>Terminologies need re-engineering </li></ul><ul><ul><li>Low detail (e.g. DAML DB, …) </li></ul></ul><ul><ul><li>Low formalization (e.g. thesauri, …) </li></ul></ul><ul><ul><li>Inexplicable or non-explicit distinctions (e.g. bottom-up domain specifications) </li></ul></ul><ul><li>Heterogeneity </li></ul><ul><ul><li>How to negotiate, integrate, merge? </li></ul></ul>
  10. 11. Methodology types <ul><li>Linguistic ontology development </li></ul><ul><ul><li>lexicographic treatment of domain terminologies </li></ul></ul><ul><li>Community ontology development </li></ul><ul><ul><li>negotiating an intersubjective agreement among the members of a community of interest </li></ul></ul><ul><li>Cognitive ontology development </li></ul><ul><ul><li>axiomatic theories and cognitive invariants to be used in performing domain analysis </li></ul></ul>
  11. 12. Basic activities in FOS Catalog building PRECEDES PRECEDES PRECEDES Ontology Merging Wrapping Terminology Re-engineering Formatting Union Mapping Interfacing Exploitation Matching Discovery Consistency checking Formalization Conceptual Integration Analysis Importing Descriptors Terms Relations Scope notes Subjects Identifiers Codes DB specific links Concepts Relations Axioms Rules Lexicalization Annotations
  12. 13. The world of conceptual modeling continent country material_place name extension name population entity relationship attribute (1,2) (m,n)
  13. 14. The world of relational tables continent-table country-table material-place-table
  14. 15. Ontological layers
  15. 16. Foundation Ontology FOS core FOS integrated FOS merged FIGIS Reference Tables ASFA FIGIS DTD ONE FISH AGROVOC
  16. 17. Integration themes <ul><li>Lexical normalization available for free by reusing thesauri (refinement needed?) </li></ul><ul><li>Documentation inherited from sources </li></ul><ul><li>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 </li></ul><ul><li>One Fish to be linked after a complete fishery ontology is available, since it is constituted by subject hierarchies </li></ul>
  17. 18. Merging <ul><li> now merging FIGIS and ASFA </li></ul><ul><li> started machine ontology learning on ASFA and FIGIS DTDs </li></ul><ul><ul><li>- ASFA multihierarchies sometimes inconsistent </li></ul></ul><ul><ul><li>ASFA RT heuristics started (next slide) </li></ul></ul><ul><ul><li>Possible synergy with OntoLearn </li></ul></ul><ul><li> started FIGIS DTD semantics analysis, in order to get semantic interoperability with FIGIS XML resources </li></ul>
  18. 19. Ontology learning from RT relationships TARGET°PLAYED-BY°MEMBER PLACE PLAYED-BY°MEMBER Aquatic organism PLACE TARGET Aquatic resource Habitat Environment Aquaculture FOS Core Freshwater organism RT RT RT Freshwater ecology Inland water environment Freshwater aquaculture ASFA (draft domain ontology from reengineered descriptors)
  19. 20. Compatibility <ul><li>Currently implemented in Loom </li></ul><ul><li>translation to other languages started </li></ul><ul><ul><li>Loom to KIF or Ontolingua available </li></ul></ul><ul><ul><li>Loom to FaCT, DAML+OIL, RDFS built by us </li></ul></ul><ul><ul><li>once in some web language, the Fishery Ontology can be used for Semantic Web applications </li></ul></ul>
  20. 21. DOLCE <ul><li>DOLCE (Descriptive Ontology for Linguistic and Cognitive Engineering) foundational ontology [Masolo et al., 2002]: </li></ul><ul><ul><li>currently includes about 200 domain-independent concepts and relations with a rich axiomatic characterization. </li></ul></ul><ul><ul><ul><li>Necessary axioms for concepts </li></ul></ul></ul><ul><ul><ul><li>Ground axioms and cross-relational axioms for relations </li></ul></ul></ul><ul><ul><li>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. </li></ul></ul><ul><ul><li>is a descriptive (as opposed to prescriptive ) ontology, because it helps categorizing an already formed conceptualization. </li></ul></ul><ul><ul><li>Download site of first deliverable: </li></ul></ul><ul><ul><ul><li>http://ontology.ip.rm.cnr.it/research/ </li></ul></ul></ul>
  21. 22. A view from FCO
  22. 23. DOLCE Top-Level
  23. 24. Incoherence detection: formalized ASFA BTs <ul><li>(DEFCONCEPT |Trap fishing@asfa| </li></ul><ul><ul><li>:IS-PRIMITIVE (:AND |Catching methods@asfa| |Fishing@asfa|)) </li></ul></ul>
  24. 25. Incoherence detection: inherited axioms <ul><li>(defconcept |Trap fishing@asfa| </li></ul><ul><li>:is-primitive (:and Asfa-Domain^|Fishing@asfa| Asfa-Domain^|Catching methods@asfa| </li></ul><ul><li>(:some Descriptions^Encompasses Fos-Core^Fishing-Zone) </li></ul><ul><li>(:some Descriptions^Encompasses Fos-Core^Aquatic-Resource) </li></ul><ul><li>(:some Descriptions^Encompasses Fos-Core^Aquatic-Organism) </li></ul><ul><li>(:some Descriptions^Encompasses Fos-Core^Gear) </li></ul><ul><li>(:some Descriptions^Encompasses Fos-Core^Vessel) </li></ul><ul><li>(:some Plans^Method-Of Fos-Core^Fishery) </li></ul><ul><li>(:some F-Participation^Product Fos-Core^Commodity) </li></ul><ul><li>(:some Dolce^Duration Dolce^Time-Interval) </li></ul><ul><li>(:some Dolce^Temporal-Location Fos-Core^Fishing-Season) </li></ul><ul><li>(:some F-Participation^Instrument Fos-Core^Vessel) </li></ul><ul><li>(:some F-Participation^Instrument Fos-Core^Gear) </li></ul><ul><li>(:some F-Participation^Instrument Everyday^Device) </li></ul><ul><li>(:some F-Participation^Has-Target Fos-Core^Aquatic-Resource) </li></ul><ul><li>(:some F-Participation^Has-Target Fos-Core^Aquatic-Organism) </li></ul><ul><li>(:some Places^Participant-Place Fos-Core^Fishing-Zone) </li></ul><ul><li>(:some Plans^Has-Method Fos-Core^Fishing-Technique) </li></ul><ul><li>(:some Descriptions^Referenced-By Fos-Core^Fishing-Regulation) </li></ul><ul><li>(:some Fos-Core^Managed-By Fos-Core^Management-Method))) </li></ul>
  25. 26. Incoherence detection: incoherence reason <ul><li>? (print-concept-outline '|Catching methods@asfa| :direction :up) </li></ul><ul><li>|Catching methods@asfa| </li></ul><ul><li>: FISHING-TECHNIQUE </li></ul><ul><li>: : TECHNIQUE </li></ul><ul><li>: : : Method </li></ul><ul><li>: : : : S-DESCRIPTION </li></ul><ul><li>: : : : : DESCRIPTION </li></ul><ul><li>: : : : : : NON-PHYSICAL-ENDURANT </li></ul><ul><li>: : : : : : : ENDURANT </li></ul><ul><li>: : : : : : : : ENTITY </li></ul><ul><li>: : : : : : : : : THING </li></ul><ul><li>? (print-concept-outline '|Fishing@asfa| :direction :up) </li></ul><ul><li>|Fishing@asfa| </li></ul><ul><li>: CAPTURE-FISHERY </li></ul><ul><li>: : FISHERY </li></ul><ul><li>: : : ACTIVITY </li></ul><ul><li>: : : : Action </li></ul><ul><li>: : : : : ACCOMPLISHMENT </li></ul><ul><li>: : : : : : EVENT </li></ul><ul><li>: : : : : : : PERDURANT </li></ul><ul><li>: : : : : : : : ENTITY </li></ul><ul><li>: : : : : : : : : THING </li></ul>
  26. 27. Incoherence detection: other annotations <ul><li>(defconcept |Trap fishing@asfa| </li></ul><ul><li>:characteristics (:closed-world :incoherent) </li></ul><ul><li>:annotations ( (INCOHERENCE-REASON &quot;Concept |C|DDO::|Trap fishing@asfa| is a member of two or more disjoint partition classes, </li></ul><ul><li>i.e., it is incoherent. Partition: DDO::$ENTITIES$, Disjoint classes: (|C|DDO::PERDURANT |C|DDO::ENDURANT)&quot;) </li></ul><ul><li>(RT (THE-RELATION '|Bait@asfa| 1)) </li></ul><ul><li>(RT (THE-RELATION '|Bait fishing@asfa| 1)) </li></ul><ul><li>(RT (THE-RELATION '|Crab fisheries@asfa| 1)) </li></ul><ul><li>(RT (THE-RELATION '|Gastropod fisheries@asfa| 1)) </li></ul><ul><li>(RT (THE-RELATION '|Lobster fisheries@asfa| 1)) </li></ul><ul><li>(RT (THE-RELATION '|Trap nets@asfa| 1))) </li></ul><ul><li>:context Asfa-Domain) </li></ul>
  27. 28. Incoherence detection: effects to ontolearning <ul><li>Given that (RT (THE-RELATION '|Crab fisheries@asfa| 1)) </li></ul><ul><li>for |Trap fishing@asfa| </li></ul><ul><li>it can be learnt the following axiom: </li></ul><ul><li>(:some METHOD-OF |Crab fisheries@asfa|) (as technique) </li></ul><ul><li>or the following one: </li></ul><ul><li>(:some PART-OF |Crab fisheries@asfa|) (as fishery) </li></ul>
  28. 29. DOLCE Top-Level
  29. 30. Quality regions in FOS
  30. 31. Basic Relations <ul><li>Parthood </li></ul><ul><ul><li>Between quality regions, or btw perdurants (immediate) </li></ul></ul><ul><ul><li>Between arbitrary objects (temporary) </li></ul></ul><ul><li>Connection, Succession </li></ul><ul><li>Dependence </li></ul><ul><ul><li>Specific/generic constant dependence </li></ul></ul><ul><li>Constitution </li></ul><ul><li>Inherence (between a quality and an entity) </li></ul><ul><li>Q-Location </li></ul><ul><ul><li>Between a quality and its region (immediate, for unchanging ent) </li></ul></ul><ul><ul><li>Between a quality and its region (temporary, for changing ent) </li></ul></ul><ul><li>Participation (btw a perdurant and an endurant) </li></ul><ul><li>Reference (btw a description and a situation) </li></ul>
  31. 32. Descriptions and Situations Template (context-based view)
  32. 33. Descriptions and Situations Template 0,n Functional Role Endurant Region Parameter T-COMP D-TOPOLOGY T-TOPOLOGY LOCATION-OF REQUISITE-FOR Course S-Description K PARTICIPANT-IN MODALITY-ON 1,n REFERENCES 1,n 1,n 1,n 1,n 1,n 1,n Situation Perdurant T-PART 0,n PLAYED-BY 0,n 0,n 0,n 0,n 0,n 0,n 0,n SEQUENCES 1,n 1,n 1,n 1,n 1,n 1,n 0,n VALUED-BY 0,n 0,n 0,n 0,n 0,n
  33. 34. Roles and descriptions
  34. 35. Roles and descriptions (2) 0,n PLAYED-BY *COMMODITY FISH 0,n 0,n 0,n *COMMODITY FISH 0,n 0,n 0,n Aquatic Organism 0,n PLAYED-BY *FISHERY ARTICLE 0,n 0,n 0,n *FISHERY ARTICLE 0,n 0,n 0,n Fishery Product F Commodity OR
  35. 36. Fishery D&S Schema ex. PCP 0,n SEQUENCES 1,n 1,n 1,n 1,n 1,n 1,n Impact Situation Routes, Action sequences, Routines, etc. 0,n VALUED-BY 0,n 0,n 0,n 0,n 0,n 0,n 0,n PLAYED-BY 1,n 1,n 1,n 1,n 1,n 1,n 0,n METHOD-OF 1,n 1,n 1,n 1,n 1,n 1,n 0,n 0,n 0,n 0,n ENVISAGES 0,n 0,n 0,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 Situation Fishery Activities and Phenomena Fishery Objects Fishery Technique Fishery Schedule Fishery Role Fishery Parameter Aquaculture, Aggressive behaviour, Frog culture, Ice fishing Underwater exploitation, Overfishing Catching method, Two boat operated purse seine
  36. 37. Exploitation <ul><li>Enhanced navigation, user profiles </li></ul><ul><li>Unified query system </li></ul><ul><li>Supporting new information services </li></ul><ul><ul><li>Discovering novel patterns </li></ul></ul><ul><ul><li>DTD modelling </li></ul></ul><ul><ul><li>Meaning negotiation </li></ul></ul>

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