Towards a Reference Library of Upper Ontologies the DOLCE point of view

1. Towards a Reference Library of Upper Ontologiesthe DOLCE point of view Nicola Guarino Head, Laboratory for Applied Ontology Institute for Cognitive Sciences and Technology, National Research Council Trento, Italy Thanks to all LOA people! www.loa-cnr.it

2. Summary • Lightweight ontologies vs. axiomatic ontologies • Why a Reference Library of (axiomatic) Upper Ontologies • What might the library look like • How to build such a library: a common framework • A common minimal vocabulary (meta-ontology?) • A common strategy to elicit the (hidden) assumptions behind each UO • Common guidelines to express Ontology Design Rationale • Common metrics to compare ontologies • How to evaluatethe practical utility of the library and of the single modules. • (DOLCE’s basic choices)

3. Lightweight ontologies vs. axiomatic ontologies different roles of ontologies • Lightweight ontologies • Intended meaning of terms mostlyknown in advance • Taxonomic reasoningis the main ontology service • Limited expressivity • On-line reasoning (stringent computational requirements) • Axiomatic ontologies • Negotiate meaningacross different communities • Establish consensusabout meaning of a new term within a community • Explain meaningof a term to somebody new to community • Higher expressivity required to express intended meaning • Off-line reasoning(only needed once, before cooperation process starts)

4. When are axiomatic ontologies useful? • When subtle distinctions are important • When recognizing disagreementis important • When rigorous referential semantics is important • When general abstractions are important • When careful explanation and justification of ontological commitment is important • When mutual understanding is important.

5. Why a Reference Library of Upper Ontologies Understand disagreements Maximize agreements Promote interoperability A starting point for building new ontologies A reference point for easy and rigorous comparison among different ontological approaches A common framework for analyzing, harmonizing and integrating existing ontologies and metadata standards

6. The WFOL architecture (WonderWeb FP5 project) Mappings with Lexicons Space of ontological choices 4D 3D Top Formal Links Between Visions & Modules Bank Space of application areas Law Single Module Single Vision

7. A common minimal vocabulary(meta-ontology?) • What is an ontology • Common terms • Property vs. relation • Property vs. quality (harder…) • Primitive/defined relation • Conceptual relation vs. extensional relation • …

8. Common strategy to elicit hidden assumptions • Systematically explore hidden intra- and inter-categorial relationships • How is subprocess related to part? • What are the possible relations within processes? • Use general issues of formal (philosophical) ontology to elicit the assumptions made (possibly by means of simple NL questions) • Common examples (such as the statue and the clay…) • Exploit formal meta-properties (OntoClean-like)

9. Formal Ontological Analysis • Theory of Essence and Identity • Theory of Parts (Mereology) • Theory of Wholes • Theory of Dependence • Theory of Composition and Constitution • Theory of Properties and Qualities The basis for a common ontology vocabulary

10. Common guidelines to express ontology design rationale • Identify issues • List possible alternatives • Carefully justify and position the choices made with respect to possible alternatives • Basic options should beclearly documented • Clear branching pointsshould allow for easy comparison of ontological options • Tradeoffs with respect to: • Choice of domain • Choice of relevant conceptual relations • Choice of primitives • Choice of axioms

11. Less good BAD WORSE Good Comparing ontolgoies: precision and coverage High precision, max coverage Low precision, max coverage Max precision, limited coverage Low precision, limited coverage

12. Evaluating Upper Ontologies • Methodology-oriented evaluation • Impact on principles and best practices for conceptual modeling • Application-oriented evaluation • Impact on the development of domain ontologies • Impact on the adaptability of domain ontologies to different domains • Cognitive and linguistic evaluation • Impact on multilingual linguistic resources • Experiments based on linguistic corpora • Psychological experiments on cognitive adequacy

13. Aldo Gangemi’s contribution Ontology patterns Role of socially constructed entities (D&S)

14. Information objects: a foundation for content description [a semiotic ontology design pattern]

15. Qualities in DOLCE: the basic pattern Quality attribution Quality Quality space Color-space Red-obj Rose Has-part Color Red-region q-location Has-part Color of rose1 Rose1 Red421 Inheres Has-quale

16. Concepts and descriptions • Reify social conceptsto be able to predicate on them Social concepts and roles as first-class-citizensCN(x): “x is a social concept” • Reify contexts or concept definitions, called here descriptions Deal with the social, relational, and contextualnature of social conceptsDS(x): “x is a description”DF(x,y): “the concept x is defined by the description y” US(x,y): “the concept x is (re)used in the description y • Introduce a temporalized classification relation to link concepts to the entities they classify Account for the dynamic behavior of social concepts CF(x,y,t): “at the time t, x is classified by the concept y”

17. Underlying assumptions • Descriptions: • are created by intentional agents at the time of their first encoding in an expression of a ‘public’ language • cease to exist when their last physical support ceases to exist • have a unique semantic content (different, but semantically equivalent, expressions can be associated to the same description) • have an internal structure intimately related to the logical structure of their semantic contents; partially accounted by means of the predicate US • Concepts: • are statically linked to descriptions: they cannot change their definitions • inherit the temporal extension of their definitions

18. Extra slides

19. State of affairs State of affairs Conceptualization C (relevant invariants across situations: D, ) Situations Ontological commitmentK Models MD(L) Ontology Tarskian interpretationI Intended models IK(L) Ontology models IK(L) Ontologies andintended meaning Language L

20. Why precision is important MD(L) Area of falseagreement! IB(L) IA(L)

21. DOLCEa Descriptive Ontology for Linguistic and Cognitive Engineering • Strong cognitive/linguistic bias: • descriptive (as opposite to prescriptive) attitude • Categories mirror cognition, common sense, and the lexical structure of natural language. • Emphasis on cognitive invariants • Categories as conceptual containers: no “deep” metaphysical implications • Focus on design rationaleto allow easy comparison with different ontological options • Rigorous, systematic, interdisciplinary approach • Rich axiomatization • 37 basic categories • 7 basic relations • 80 axioms, 100 definitions, 20 theorems • Rigorous quality criteria • Documentation

22. DOLCE’s basic taxonomy Endurant Physical Amount of matter Physical object Feature Non-Physical Mental object Social object … Perdurant Static State Process Dynamic Achievement Accomplishment Quality Physical Spatial location … Temporal Temporal location … Abstract Abstract Quality region Time region Space region Color region … …

23. DOLCE's Basic Ontological Choices • Endurants (aka continuants or objects) and Perdurants (aka occurrences or events) • distinct categories connected by the relation of participation. • Qualities • Individual entities inhering in Endurants or Perdurants • can live/change with the objects they inhere in • Instance of quality kinds, each associated to a Quality Space representing the "values" (qualia) that qualities (of that kind) can assume.Quality Spaces are neither in time nor in space. • Multiplicative approach • Different Objects/Events can be spatio-temporally co-localized: the relation of constitution is considered.