21 Years of Applied Ontology

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Invited presentation at Carnegie Mellon University

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  • Thank for this explanation. I didn't know I do ontology from 20 years. Now on Justice (good faith calculation), Nutrition (food decision), Communication (project temmplate), Politics (lying control)... I discovered that only 2 month ago.
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21 Years of Applied Ontology

  1. 1. Twentyone Years of Applied Ontology 1993-2014 Nicola Guarino National Research Council, Institute for Cognitive Science and Technologies (ISTC-CNR) Laboratory for Applied Ontology (LOA) www.loa.istc.cnr.it ! Thanks to Giancarlo Guizzardi and all the LOA people!
  2. 2. The most cited paper in applied ontology Presented at International Workshop on Formal Ontology, March 1993, Padua, Italy – Organized by LADSEB-CNR (N. Guarino and R. Poli)
  3. 3. Carving the reality at its joints: good ontologists like good butchers [Plato] 3
  4. 4. …but, despite reality resists, still many possibilities are open! 4
  5. 5. 4 Applied Ontology: an emerging interdisciplinary area • 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)
  6. 6. Focusing on content
  7. 7. 15 Do we know what to REpresent? • First analysis,! • THEN representation…! Unfortunately, this is not the current practice…! • Computer scientists have focused on the structure of representations and the nature of reasoning more than on the content of such representations! Essential ontological promiscuity of AI: any agent creates its own ontology based on its usefulness for the task at hand (Genesereth and Nilsson 1987) No representation without ontological analysis!
  8. 8. Logic is neutral about content ...but very useful to describe the formal structure (i.e., the invariances) of content
  9. 9. 9 Kinds of knowledge" (Carnap - Meaning and Necessity) logical Fido is black! synthetic either Fido is black or Fido is not black! If Jack is a bachelor, then he is not married analytic terminological (assertional) Terminological knowledge is about relationships between terms and concepts
  10. 10. 7 The problem: subtle distinctions in meaning ! The e-commerce case:! ! “Trying to engage with too many partners too fast is one of the main reasons that so many online market makers have foundered” The transactions they had viewed as simple and routine actually involved many subtle distinctions in terminology and meaning”! ! Harvard Business Review, October 2001
  11. 11. 9 Subtle distinctions in meaning... • What is an application to a public administration?! • What is a service?! • What is a working place?! • What is an unemployed person?! • What is a customer?! • What is an organization?! • What is a contract? The key problems! • content-based information access (semantic matching)! • content-based information integration (semantic integration)
  12. 12. Semantic Interoperability is considered to be the problem of this decade…[currently] costing productivity, lives and billions of dollars annually…the overall human and financial cost to society from our failure to share and reuse information is many times the cost of the systems’ operation and maintenance [OMG, SIMF]
  13. 13. When subtle distinctions are important:! fine prints An ontology is like a contract's fine print, one of those things which require a very precise technical jargon, which you might ignore in many cases, but which can save your business in critical situations. 13
  14. 14. Product Classification • U.S. Product Classification: • Dolls: Representing Only a Human-Being (12%) • Toys: Anything that does not represent only a human being (6%)
  15. 15. What is an ontology
  16. 16. 19 Philosophical ontologies • Ontology: the philosophical discipline! ! • Study of what there is (being qua being...)! ...a liberal reinterpretation for computer science: ! ! content qua content, independently of the way it is represented! ! • Study of the nature and structure of “reality”! ! • A (philosophical) ontology: a structured system of entities assumed to exists, organized in categories and relations
  17. 17. Computational ontologies 20 Specific (theoretical or computational) artifacts expressing the intended meaning of a vocabulary in terms of primitive categories and relations describing the nature and structure of a domain of discourse Gruber: “Explicit and formal specifications of a conceptualization” Computational ontologies, in the way they evolved, unavoidably mix together philosophical, cognitive, and linguistic aspects.! Ignoring this intrinsic interdisciplinary nature makes them almost useless.
  18. 18. Perception Reality Bad Ontology ~Good Ontology Conceptualization Language L Intended models for each IK(L) Ontological commitment K (selects D’⊂D and ℜ’⊂ℜ) Interpretations I Models MD’(L) Ontology models relevant invariants within and across presentation patterns: D, ℜ State of aSfftaaitres of Parfefsaeinrtsation patterns Phenomena
  19. 19. Less good WORSE 22 Ontology Quality: Precision and Correctness Low precision, max correctness Low precision, low correctness Good High precision, max correctness BAD Max precision, low correctness
  20. 20. Why ontological precision is important
  21. 21. Database A: keeping track of fruit stock 24 Variety Quantity Granny Smith 12 Golden delicious 10 Stark delicious 15
  22. 22. Database B: keeping track of juice stock 25 Variety Quantity Granny Smith 12 Golden delicious 10 Stark delicious 15
  23. 23. Why ontological precision is important 26 All interpretations of “apple” Area of false agreement! B - Juice producer’s intended interpretations A - Apple producer’s intended interepretations Interpretations allowed by B’s ontology Interpretations allowed by A’s ontology
  24. 24. When is a precise (and accurate) ontology useful? 27 1. When subtle distinctions are important! 2. When recognizing disagreement is important! 3. When careful explanation and justification of ontological commitment is important! 4. When mutual understanding is more important than interoperability.
  25. 25. Two classic KR problems solved by formal ontology
  26. 26. 29 Problem 1 - Terminological competence How many rock kinds are there? rock igneous rock sedimentary rock metamorphic rock large rock grey rock grey sedimentary rock large grey igneous rock pet metamorphic rock [From Brachman, R ., R. F ikes, et al. 1983. “Krypton: A Functional Approach to Knowledge Representation”, IEEE Computer]
  27. 27. 30 The answer • According to Brachman & Fikes 83: • It’s a dangerous question, only “safe” queries about analytical relationships between terms should be asked • In a previous paper by Brachman and Levesque on terminological competence in knowledge representation [AAAI 82]: • “an enhancement mode transistor (which is a kind of transistor) should be understood as different from a pass transistor (which is a role a transistor plays in a larger circuit)” • These issues have been simply given up while striving for logical simplification and computational tractability • The OntoClean methodology, based on formal ontological analysis, allows us to conclude: there are 3 kinds of rocks
  28. 28. 31 Problem 2 - What’s in a link? • 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
  29. 29. Structured concepts: a broader picture JOHN HEIGHT: 6 FEET RIGHT-LEG: BROKEN MOTHER: JANE KISSED: MARY JOB: RESEARCHER intrinsic quality part role external relation relational quality 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! 32
  30. 30. The Ontological Level • Guarino N. 1994. The Ontological Level. In R. Casati, B. Smith and G. White (eds.), Philosophy and the Cognitive Sciences (by 16th International Wittgenstein Symposium, Kirchberg am Wechsel, Austria, 1993). Vienna, Hölder-Pichler-Tempsky 1994! • Guarino, N. 2009. The Ontological Level: Revisiting 30 Years of Knowledge Representation. In Alex Borgida, Vinay Chaudhri, Paolo Giorgini, Eric Yu (eds.), Conceptual Modelling: Foundations and Applications. Essays in Honor of John Mylopoulos, Springer Verlag 2009 Level Primitives Interpretation Main feature Logical Predicates, functions Arbitrary Formalization Epistemological Structuring relations Arbitrary Structure Ontological Ontological relations Constrained (meaning postulate s ) Meaning Conceptual Conceptual relations Subjective Conceptualization Linguistic Linguistic terms Subjective Language dependence
  31. 31. 34 The formal tools of ontological analysis • Theory of Parts (Mereology) ! • Theory of Unity and Plurality! • Theory of Essence and Identity! • 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 2000
  32. 32. 35 The semantic web architecture [Tim Berners Lee 2000]
  33. 33. The Semantic Web Architecture (revised) 36
  34. 34. 37 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
  35. 35. Knowledge representation, conceptual modeling, and ontological analysis • Data models encode knowledge about the world in order to easily and efficiently access it! • Conceptual models describe some aspects of the world for the purpose of understanding and communication! • Knowledge representations encode knowledge about the world in order to easily and efficiently access it and use it to generate new knowledge! • Computational ontologies characterize the language used to talk about the world in order to reduce ambiguities and misunderstandings • Ontological analysis: systematic way to understand and make explicit the world assumptions behind a certain description:! • How do we believe the world is, when we say ! • This rose is red! • John is married with Mary! • John is a student! • My name is Nicola 38 different role of axioms possible expansions of domain ! and vocabulary
  36. 36. Ontological analysis as a detective lens • Most of our true statements about the world are approximate! • What makes them true?! • Where…?! • When…?! • Who…?! • Why…?! • Ontological analysis as a search for Truth-makers 39
  37. 37. A bit of history - Community building initiatives • 1993: 1st Int. workshop on Formal Ontology & Information Systems! • 1998: 1st FOIS conference! • 2002: Ontolog forum! • 2005: Applied Ontology (IOS Press)! • 2005: ECOR, NCOR, JCOR...! • 2006: First public discussion on an ontology association at FOIS ! • 2008: Public assembly at FOIS (Saarbrucken)! • 2011: Applied Ontology gets official ISI recognition! • 2009-2012: Several focused conferences (FOMI, WOMO...)! • 2012: IAOA permanent co-organizer of Ontology Summit! • 2013: ! • IJCAI invites authors of best FOIS papers; ! • Philosophical community (J. Lowe) organizes a conference on applied ontology! ! • In parallel: various consortia focusing mainly on Semantic Web 40
  38. 38. A new discipline is emerging
  39. 39. A hint from the past: The emergence of psychology as a science • 1874: Franz Brentano publishes Psychology from an Empirical Standpoint! • 1879: Wilhelm Wundt establishes the world’s first psychological laboratory at the University of Leipzig! • 1883: First psychology lab in America established at Johns Hopkins! • University authorities give Wundt's Leipzig laboratory formal recognition! • 1889: First international congress of Psychology! • Alexius Meinong founds Laboratory of Psychology in University of Graz! • First Chinese translation of a Western psychology book! • 1892: American Psycgological Association founded (42 members)! • 1894: Stumpf called to serve as professor of philosophy in Berlin with the explicit task of establishing there an institute of psychology 42
  40. 40. Typical reactions to the founding of a new discipline If one is interested in the relations between fields which, according to customary academic divisions, belong to different departments, then he will not be welcomed as a builder of bridges, as he might have expected, but will rather be regarded by both sides as an outsider and troublesome intruder. ! R. Carnap, "My Work in Philosophy Begins". 18
  41. 41. 20 From metaphysics to ontology as a science Metaphysics (phil.)! ! The science of being! Ontology (phil.) ! ! A theory of the types of entities existing in reality, and of the relations between these types! Ontologies (tech.)! ! Standardized classification systems which enable data from different sources to be combined! Ontology (science)! ! The science which develops theories of the types of entities existing in (people’s assumptions about) given domains of reality, and of the relations between these types including: ways of testing such theories, ways of using such theories, e.g. in supporting reasoning about empirical data collected by other sciences
  42. 42. 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 socio-technical systems.
  43. 43. From the Statute “The Association is a non-profit organization the purpose of which is to promote interdisciplinary research and international collaboration at the intersection of philosophical ontology, linguistics, logic, cognitive science, and computer science, as well as in the applications of ontological analysis to conceptual modeling, knowledge engineering, knowledge management, information-systems development, library and information science, scientific research, and semantic technologies in general.” 5
  44. 44. IAOA: a unique combination of key aspects 1. Interdisciplinarity! 2. Cooperation between academy, industry, and communities of practice (with an eye on education)! 3. Scientific authoritativeness! 4. Openness! 5. Legal status! 6. Transparent governance
  45. 45. The IAOA flagship journal: Applied Ontology Editors in chief: Nicola Guarino ISTC-CNR Mark Musen Stanford University ! ! IOS Press Amsterdam, Berlin, Washington, Tokyo, Beijing www.applied-ontology.org Now indexed by ISI and Scopus. Impact Factor: 1.105
  46. 46. 49
  47. 47. Is applied ontology successful?
  48. 48. “The fast-­‐growing science of ontology could exert a greater impact on humanity than the rise of the Internet…ontologies allow for data to interoperate and for machines to make inferences. The report calls for more education and training opportunities for ontologists, and for better means of connecting ontologists with organizations that need them.
  49. 49. In 2014, the Financial Research Advisory Committee (FRAC) of the US Treasury’s Office of Financial Research (OFR) unanimously recommended the adoption of ontology as a significant part of the OFR’s initiatives to meet the requirements put forth by the Dodd-Frank Act
  50. 50. DataModelingGuide(DMG)ForAnEnterpriseLogicalDataModel,V2.3;15March2011 Data Modeling Guide (DMG) For An Enterprise Logical Data Model (ELDM) Version 2.3 March 15, 2011 The U.S. Government has rights in this document in accordance with DFAR 252.227-7013 Rights in Technical Data – Noncommercial Item (Nov 1995) under contract number H98230-09-C-1180.
  51. 51. Ontologies and (big) data
  52. 52. 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
  53. 53. 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