Ontological Analysis! and Conceptual Modelling! an introduction

Transcription

1 Ontological Analysis! and Conceptual Modelling! an introduction Nicola Guarino Italian National Research Council Institute for Cognitive Science and Technologies (ISTC-CNR) Laboratory for Applied Onotlogy (LOA) 1

2 Summary Applied Ontology and ontological analysis! The basic tools of formal ontological analysis: unity, essence, identity, dependence! The impact of ontological analysis on the practice of conceptual modeling: OntoClean, OntoUML! The Dolce ontology and its key distinctions: endurants, perdurants, qualities 2

3 Applied Ontology and Ontological Analysis Applied Ontology builds on philosophy, cognitive science, linguistics and logic with the purpose of understanding, clarifying, making explicit and communicating people's assumptions about the nature and structure of the world.! This orientation towards helping people understanding each other distinguishes applied ontology from philosophical ontology, and motivates its unavoidable interdisciplinary nature. ontological analysis: study of! content (of these assumptions) as such! (independently of their representation) 3 4

4 Kinds of knowledge logical Fido is black! Fido is black or Fido is not black! If Jack is a bachelor, then he is not married synthetic (assertional) analytic terminological Terminological knowledge is about intended meaning of terms 4

5 Knowledge representation, conceptual modeling, and ontological analysis Data modeling: encoding knowledge about the world in order to easily and efficiently access it! KR: encoding knowledge about the world in order to easily and efficiently access it and use it to generate new knowledge! CM: describe some aspects of the world for the purpose of understanding and communication! Ontological analysis: systematic way to understand and make explicit the world assumptions behind a certain description:! How do we believe there world is, when we say! This rose is red! John is married with Mary! John is a student! My name is Nicola 5

6 Ontological analysis as a detective lens Most of our true statements about the world are vastly approximate! What makes them true?! Where?! When?! Who?! Why?!! Truth-making as the main phenomenon studied by ontological analysis 6

7 What is an ontology

8 Conceptualization Perception Reality relevant invariants (and changes) within and across presentations: D, R State of State of affairs affairs Presentations Phenomena Language L Ontological commitment K (selects D D and R R) Models M D (L) Bad Ontology Interpretations I ~Good Ontology Intended models for each I K (L) 8 Ontology models

9 Ontology Quality: Precision and Correctness Good Less good High precision, max correctness Low precision, max correctness BAD WORSE Max precision, low correctness Low precision, low correctness 9

10 Different kinds of relations Woods What s in a link? (1975): JOHN HEIGHT: 6 FEET KISSED: MARY! "no longer do the link names stand for attributes of a node, but rather arbitrary relations between the node and other nodes different notations should be used 10

11 Structuring relations: a broader picture JOHN HEIGHT: 6 FEET RIGHT-LEG: LEG#1 MOTHER: JANE JOB: RESEARCHER KISSED: MARY structuring relationships intrinsic quality part role relational quality external relation We need different primitives to express different structuring relationships among concepts We need to represent non-structuring relationships separately Current description logics tend to collapse EVERYTHING! 11

12 The semantic web architecture [Tim Berners Lee 2000] 12

13 The Semantic Web Architecture (revised) 13

14 The basic tools of formal ontological analysis Theory of Essence and Identity! Theory of Parts (Mereology)! Theory of Unity and Plurality! Theory of Dependence! Theory of Composition and Constitution! Theory of Properties and Qualities The basis for a common ontology vocabulary Idea of Chris Welty, IBM Watson Research Centre, while visiting our lab in

15 Formal Ontology Theory of formal distinctions and connections within:! entities of the world, as we perceive it (particulars)! categories we use to talk about such entities (universals)!! Why formal?! Two meanings: rigorous and general! Formal logic: connections between truths - neutral wrt truth! Formal ontology: connections between things - neutral wrt reality!! NOTE: represented in a formal language is not enough for being formal in the above sense!!! Analytic ontology may be a better term to avoid this confusion 15

16 The first steps of ontological analysis Conceptualization C (relevant invariants across situations: D, R) State of State of affairs Phenomena affairs Language L Ontological commitment K (selects D D and R R) 1. Be clear about the domain of discourse 2. Choose the relevant conceptual relations 3. Choose the primitive relations 4. Choose meaningful names for these 5. Define non-primitive relations in terms of primitive relations 6. Axiomatize primitive relations 16

17 Mereology: an example of formal ontological analysis Primitive: proper part-of relation (PP) asymmetric transitive Useful definitions: Pxy = df PPxy x=y (generalized part-of) Oxy = df z(pzx Pzy) (overlap) Szxy = df Pxz Pyz w(pwz (Owx Owy)) (mereological sum) Axioms: (weak) supplementation: principle of sum: extensionality: PPxy z (Pzy Ozx) xy z Sxzy x = y w(ppwx PPwy) Excluded models:? 17

18 Sets vs. mereological sums x + y =df ιz Szxy What is the difference between a + b and {a,b}? Sets of concrete things are abstract Sums of concrete things are concrete!! What s the difference between {a} and a? What is {}? if a = {a1,a2} then a1, a2 {a,b} if a = a1 + a2 then a1 and a2 are both parts of a+b 18

19 A Violation of Supplementation Axiom Dov Dory, Words from pictures for dual-channel processing, Communications of the ACM 51,

20 Part, Constitution, and Identity Parts not enough to make the whole: structure creates a new entity Constitution links the two entities Constitution is asymmetric (implies dependence) Mereological extensionality to be lost? a + b K Tower#1 a + b K Tower#1 b D D a b a Two blocks A tower a b a 20 b

21 Dolce: motivating its ontological distinctions

22 DOLCE a 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 rationale to 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

23 The rich ontology of natural language Events are different from objects! Multiple co-located objects! I am talking here! *This bunch of molecules is talking! *What s here now is talking!! This statue is looking at me! *This piece of marble is looking at me! This statue has a strange nose! *This piece of marble has a strange nose! Multiple co-located events! John sings while taking a shower! Individual qualities! - The nurse measured the patient s temperature! - I like the color of this rose! - The color of this rose turned from red to brown in one week 23

24 DOLCE s basic taxonomy Object (endurant)!! Physical!!! Amount of matter!!! Physical object!!! Feature!! Non-Physical!!! Mental object!!! Social object!!! Event (perdurant)!! Static!!! State!!! Process!! Dynamic!!! Achievement!!! Accomplishment Quality!! Physical!!! Spatial location!!!!! Temporal!!! Temporal location!!!!! Abstract!! Abstract!! Quality region!!! Time region!!! Space region!!! Color region!!!!! 24

25 DOLCE taxonomy PT Particular ED Endurant PD Perdurant Q Quality AB Abstract PED Physical! Endurant NPED Non-physical! Endurant AS Arbitrary! Sum EV Event STV Stative TQ Temporal! Quality PQ Physical! Quality AQ Abstract! Quality Fact Set R Region M Amount of! Matter F Feature POB Physical! Object NPOB Non-physical! Object ACH Achievement ACC Accomplishment ST State PRO Process TL SL Temporal! Location Spatial! Location TR Temporal! Region PR Physical! Region AR Abstract! Region APO Agentive! Physical! Object NAPO Non-agentive Physical! Object MOB Mental Object SOB Social Object T S Time! Interval Space! Region ASO Agentive Social Object NASO Non-agentive Social Object SAG Social Agent SC Society 25

26 Objects and Events (Endurants and Perdurants) Objects (3D continuants)! Need a time-indexed parthood relation! Exist in time! Can genuinely change in time! May have non-essential parts! All proper parts are present whenever they are present (wholly presence, no temporal parts)!! Events (4D occurrences)! Do not need a time-indexed parthood relation! Happen in time! Do not change in time (as a whole...)! All parts are essential! Only some proper parts are present whenever they are present (partial presence,temporal parts)!! Objects participate to Events 26

27 Linguistic evidence concerning individual qualities This rose is red! Red is a color! This rose has a color! The color of this rose turned to brown in one week! Red is opposite to green and close to brown! The patient s temperature is increasing! The doctor measured the patient's temperature 27

28 Individual qualities: the DOLCE approach Individual qualities inhere to specific individuals! Qualia are abstract entities representing what (perceived as) exactly resembling individual qualities have in common.! Qualia resulting from comparable individual qualities are organized in quality spaces.! We refer to individual qualities with expressions such as the color of my car! So the color of my car is different from the color of your car, even if they are perceived as having exactly the same shade of red, i.e., the same quale! Qualia resulting from comparable individual qualities are organized in quality spaces. Each quality type has its own quality space.! At different times, the same individual quality can move in its quality space, being perceived as different qualia.! Qualitative properties hold because qualities have certain locations in their quality spaces. 28

29 Qualities The rose and the chair have the same color: different color qualities inhere to the two objects they are located in the same quality region! Therefore, the same color attribute (red) is ascribed to the two objects 29

30 Qualities Quality attribution Quality Quality space Color-space Rose Red-obj Color Has-part q-location Red-region Has-part Rose1 Color of rose1 Red421 Inheres Has-quale 30

31 Qualities vs. Features Features: parasitic physical entities.! relevant parts of their host! or places! Features have qualities, qualities have no features. 31

32 Abstract vs. Concrete Entities Concrete:! located (at least) in time! Abstract - two meanings:! - Result of an abstraction process (something common to multiple exemplifications)! Not located in space-time (no inherent spatial or temporal location)!! Examples: propositions, sets, symbols, regions, etc.! Quality regions and quality spaces are abstract entities! Mereological sums (of concrete entities) are concrete, the corresponding sets are abstract... 32

33 Physical vs. Non-physical Objects Physical objects! Inherent spatial localization! Not necessarily dependent on other objects!!!! Non-physical objects! No inherent spatial localization! Dependent on agents! mental (depending on singular agents)! FIAT Co. social (depending on communities of agents)! Agentive: a company, an institution! Non-agentive: a law, the Divine Comedy, a linguistic system! Descriptions, an extension of DOLCE 33

34 The four-category ontology Substantial universals Non-substantial universals Kinds characterised by Attributes instantiated by instantiated by Objects characterised by Modes Substantial particulars Non-substantial particulars 34

35 Dolce qualities and the four-category ontology Substantial universals Non-substantial universals Kinds characterised by Quality kinds characterised by Quality spaces atomic parts of instantiated by instantiated by instantiated by Qualia Objects characterised by Qualities manifest as Quality manifesta tions Substantial particulars Non-substantial particulars 35

36 Dolce and the four-category ontology! (a rough attempt) Substantial universals Non-substantial universals Kinds characterised by Properties instantiated by instantiated by exemplified by Objects characterised by Qualities constitutive subjects of Events Substantial particulars Non-substantial particulars 36

37 UFO and OntoUML! (Giancarlo Guizzardi and coll., UFES, Brazil) UFO: A foundational ontology that shares many of the basic intuitions of DOLCE, and includes an ontology of universal largely based on OntoClean! OntoUML: A visual conceptual modeling language whose primitives reflect ontological distinctions put forth by UFO! Besides the OntoUML language itself, the approach includes a number of methodological principles, design patterns and computational tools for doing formal verification, validation (via visual simulation), verbalization and automated transformation to operational languages such as OWL! Adopted in many industrial projects in different domains (telecommunications, government, digital journalism, software engineering, etc.) 37

38 The Association is addressed to: Philosophers who have an interest in applying their analytical tools to technology advancement; cognitive scientists, linguists and terminologists aware of the subtle interplays among ontology, language, and cognition; computer scientists and IT professionals aware of the desperate need of a sound interdisciplinary approach for building future generation sociotechnical systems.

39 The IAOA flagship journal: Applied Ontology Editors in chief: Nicola Guarino ISTC-CNR Mark Musen Stanford University!! IOS Press Amsterdam, Berlin, Washington, Tokyo, Beijing Now indexed by ISI and Scopus. Impact Factor: 1.105

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41 A new discipline (or science) is emerging? Maybe.! See the history of Psychology, Systems Engineering...! See recent proposals for Web Science, Services Science, Data Science??! For sure, a humble, truly interdisciplinary approach is needed, focusing on letting new ideas, approaches, methodologies emerge from the mutual cross-fertilization of different disciplines. 3

42 Current Research topics Methodology Ontology of socio-technical systems! Multi-agent systems, social interaction, and collective intentionality! Ontology of organizations and social roles! Ontology of functions, artefacts, and engineering design! Integrated modelling of organizations, processes, and services! Ontological foundations of service science and value-cocreation! Visual recognition of crisis situations; role of emotions in crisis situations! Role of crises and contradiction in social interaction! Ontology, language, cognition! Ontology, cognition, and natural language semantics! Ontology and lexical resources! Formal semantics of discourse and dialogue relations! Ontology and epistemology of measurement! Perception of visual objects! Perception, social conventions, and ontological constructivism! Principles and methodologies for ontological analysis, conceptual modeling, knowledge representation, and software engineering! Formal ontological analysis: theories of properties, qualities, parts, unity, identity, dependence...! Ontology-driven conceptual modelling