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

3. Summary Aim and methods of FOS project Sources, tools, and work done Uses of foundational ontologies Some fishery conceptual schemas Demo (not in this section) Exploitation scenarios

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.

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.

6. Ontological engineering OntoDevelop ComponentData <- 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 techniqueOntology component Ontologies INSTR PRODUCT INSTRUMENT Ontological procedure Tool TARGET-OF

7. Different uses of ontologies Reference ontologies (development time) –establish consensus about meaning of terms (in general) –higher expressivity (non-stringent computational reqs.): task to be undertaken only once for cooperation process types Application ontologies (run time) –offer terminological services for semantic access, checking constraints between terms –limited expressivity (stringent computational reqs.) –can be derived from reference ontologies Mutual understanding more important than mass interoperability –understanding disagreements in the context of common criteria –establish trustable mappings among application ontologies

8. Conceptual tools from OntoLab DOLCE Foundational Ontology, a set of cognitively motivated categories to support domain analysis. 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. 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. 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.

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

10. Existing “ontologies” Controlled terminologies or axiomatic theories? Terminologies need re-engineering –Low detail (e.g. DAML DB, …) –Low formalization (e.g. thesauri, …) –Inexplicable or non-explicit distinctions (e.g. bottom-up domain specifications) Heterogeneity –How to negotiate, integrate, merge?

11. Methodology types Linguistic ontology development –lexicographic treatment of domain terminologies Community ontology development –negotiating an intersubjective agreement among the members of a community of interest Cognitive ontology development –axiomatic theories and cognitive invariants to be used in performing domain analysis

12. Basic activities in FOS Catalog building 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

13. The world of conceptual modeling continent country material_place name extension name population entity relationship attribute (1,2) (m,n)

14. The world of relational tables continent-table country-table material-place-table

15. Ontological layers

16. Foundation Ontology FOS core FOS integratedFOS merged FIGIS Reference TablesASFA FIGIS DTD ONE FISH AGROVOC

17. Integration themes Lexical normalization available for free by reusing thesauri (refinement needed?) Documentation inherited from sources 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 One Fish to be linked after a complete fishery ontology is available, since it is constituted by subject hierarchies

18. Merging  now merging FIGIS and ASFA  started machine ontology learning on ASFA and FIGIS DTDs -ASFA multihierarchies sometimes inconsistent -ASFA RT heuristics started (next slide) -Possible synergy with OntoLearn  started FIGIS DTD semantics analysis, in order to get semantic interoperability with FIGIS XML resources

19. TARGET°PLAYED-BY°MEMBER PLACE Ontology learning from RT relationships PLAYED-BY°MEMBER Aquatic organism PLACE TARGET Aquatic resource Habitat Environment Aquaculture FOS Core Freshwater organism RT Freshwater ecology Inland water environment Freshwater aquaculture ASFA (draft domain ontology from reengineered descriptors)

20. Compatibility  Currently implemented in Loom  translation to other languages started –Loom to KIF or Ontolingua available –Loom to FaCT, DAML+OIL, RDFS built by us –once in some web language, the Fishery Ontology can be used for Semantic Web applications

21. DOLCE DOLCE (Descriptive Ontology for Linguistic and Cognitive Engineering) foundational ontology [Masolo et al., 2002]: –currently includes about 200 domain-independent concepts and relations with a rich axiomatic characterization. Necessary axioms for concepts Ground axioms and cross-relational axioms for relations –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. –is a descriptive (as opposed to prescriptive) ontology, because it helps categorizing an already formed conceptualization. –Download site of first deliverable: http://ontology.ip.rm.cnr.it/research/

22. A view from FCO

23. DOLCE Top-Level

24. Incoherence detection: formalized ASFA BTs (DEFCONCEPT |Trap fishing@asfa|fishing@asfa| :IS-PRIMITIVE (:AND |Catching methods@asfa| |Fishing@asfa|))

25. Incoherence detection: inherited axioms (defconcept |Trap fishing@asfa| :is-primitive (:and Asfa-Domain^|Fishing@asfa| Asfa-Domain^|Catching methods@asfa| (:some Descriptions^Encompasses Fos-Core^Fishing-Zone) (:some Descriptions^Encompasses Fos-Core^Aquatic-Resource) (:some Descriptions^Encompasses Fos-Core^Aquatic-Organism) (:some Descriptions^Encompasses Fos-Core^Gear) (:some Descriptions^Encompasses Fos-Core^Vessel) (:some Plans^Method-Of Fos-Core^Fishery) (:some F-Participation^Product Fos-Core^Commodity) (:some Dolce^Duration Dolce^Time-Interval) (:some Dolce^Temporal-Location Fos-Core^Fishing-Season) (:some F-Participation^Instrument Fos-Core^Vessel) (:some F-Participation^Instrument Fos-Core^Gear) (:some F-Participation^Instrument Everyday^Device) (:some F-Participation^Has-Target Fos-Core^Aquatic-Resource) (:some F-Participation^Has-Target Fos-Core^Aquatic-Organism) (:some Places^Participant-Place Fos-Core^Fishing-Zone) (:some Plans^Has-Method Fos-Core^Fishing-Technique) (:some Descriptions^Referenced-By Fos-Core^Fishing-Regulation) (:some Fos-Core^Managed-By Fos-Core^Management-Method)))

26. Incoherence detection: incoherence reason ? (print-concept-outline '|Catching methods@asfa| :direction :up) |Catching methods@asfa| : FISHING-TECHNIQUE : : TECHNIQUE : : : Method : : : : S-DESCRIPTION : : : : : DESCRIPTION : : : : : : NON-PHYSICAL-ENDURANT : : : : : : : ENDURANT : : : : : : : : ENTITY : : : : : : : : : THING ? (print-concept-outline '|Fishing@asfa| :direction :up) |Fishing@asfa| : CAPTURE-FISHERY : : FISHERY : : : ACTIVITY : : : : Action : : : : : ACCOMPLISHMENT : : : : : : EVENT : : : : : : : PERDURANT : : : : : : : : ENTITY : : : : : : : : : THING

27. Incoherence detection: other annotations (defconcept |Trap fishing@asfa| :characteristics (:closed-world :incoherent) :annotations ( (INCOHERENCE-REASON "Concept |C|DDO::|Trap fishing@asfa| is a member of two or more disjoint partition classes, i.e., it is incoherent. Partition: DDO::$ENTITIES$, Disjoint classes: (|C|DDO::PERDURANT |C|DDO::ENDURANT)") (RT (THE-RELATION '|Bait@asfa| 1)) (RT (THE-RELATION '|Bait fishing@asfa| 1)) (RT (THE-RELATION '|Crab fisheries@asfa| 1)) (RT (THE-RELATION '|Gastropod fisheries@asfa| 1)) (RT (THE-RELATION '|Lobster fisheries@asfa| 1)) (RT (THE-RELATION '|Trap nets@asfa| 1))) :context Asfa-Domain)

28. Incoherence detection: effects to ontolearning Given that (RT (THE-RELATION '|Crab fisheries@asfa| 1)) for |Trap fishing@asfa| it can be learnt the following axiom: (:some METHOD-OF |Crab fisheries@asfa|) (as technique) or the following one: (:some PART-OF |Crab fisheries@asfa|) (as fishery)

29. DOLCE Top-Level

30. Quality regions in FOS

31. Basic Relations Parthood –Between quality regions, or btw perdurants (immediate) –Between arbitrary objects (temporary) Connection, Succession Dependence –Specific/generic constant dependence Constitution Inherence (between a quality and an entity) Q-Location –Between a quality and its region (immediate, for unchanging ent) –Between a quality and its region (temporary, for changing ent) Participation (btw a perdurant and an endurant) Reference (btw a description and a situation)

32. Descriptions and Situations Template (context-based view)

33. Descriptions and Situations Template 0,n Functional Role EndurantRegion Parameter

34. Roles and descriptions

35. 0,n PLAYED-BY *COMMODITY FISH 0,n *COMMODITY FISH 0,n Aquatic Organism 0,n PLAYED-BY *FISHERY ARTICLE 0,n *FISHERY ARTICLE 0,n Fishery Product F Commodity Roles and descriptions (2) OR

36. Fishery D&S Schema ex. PCP 0,n SEQUENCES 1,n Impact Situation Routes, Action sequences, Routines, etc. 0,n VALUED-BY 0,n PLAYED-BY 1,n 0,n METHOD-OF 1,n 0,n ENVISAGES 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 RegionFishery SituationFishery Activities and PhenomenaFishery Objects Fishery TechniqueFishery ScheduleFishery RoleFishery Parameter Aquaculture, Aggressive behaviour, Frog culture, Ice fishing Underwater exploitation, Overfishing Catching method, Two boat operated purse seine

37. Exploitation Enhanced navigation, user profiles Unified query system Supporting new information services –Discovering novel patterns –DTD modelling –Meaning negotiation