Dreams awakenings and paradoxes of ontologies
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Dreams, awakenings and paradoxes of ontologies. Joost Breuker University of Amsterdam Leibniz Center for Law. Some background about me. studied cognitive psychology at the University of Amsterdam (1963-1969); PhD in 1979 research in cognitive science (1966-1979)

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Dreams, awakenings and paradoxes of ontologies

Joost Breuker

University of Amsterdam

Leibniz Center for Law

Some background about me

  • studied cognitive psychology at the University of Amsterdam (1963-1969); PhD in 1979

  • research in cognitive science (1966-1979)

    • Artificial Intelligence in Amsterdam (chess, story understanding)

      • first in Europe (continent) (Frijda, de Groot)

    • psycho-linguistics and text understanding (Levelt, van Dijk)

    • educational research & AI (Intelligent Teaching Systems)

  • research in AI: European projects (1983-now)

    • SWI/HCL (UvA): knowledge engineering

      • CommonKADS methodology (Wielinga, Schreiber, van Harmelen) (NB: 91-92@USP in Brazil)

      • Intelligent help systems

    • Leibniz Center for Law: `computational legal theory’

    • University of Montpellier-II/LIRMM: GRID and eLearning (Cerri, Eisenstadt)


  • Philosophical background

  • Leibniz’ dreams

  • Tim Berners-Lee’s dreams

  • What do ontologies stand for anyway

    • Ontological reasoning

    • Knowledge and semantics

  • Conclusions and morals

Ontology and ontologies: some obvious mismatches

  • Ontology: ”the theory or study of being as such; i.e., of the basic characteristics of all reality.” (Encyclopedia Britannica)

  • Existence/reality

  • Metaphysics

  • Qualities/properties/correspondences

  • Ontology in ontologies:

    • Sowa (2000), chapter 2

    • DOLCE

    • …Formal ontology…

  • Ontologies: “terms for shared understanding”

  • Knowledge

  • Physics, etc

  • Concepts (Classes)

Top of DOLCE


Sowa’s (2000) synthesis of 2500 years of philosophical ontology

And LRI-Core (LKIF-Core)

However, there are far more interesting roots of ontologies in philosophy than in its metaphysical speculations


Artificial Universal Language (lingua univeralis philosophica)

  • Middle age: eg Ockham

    • Babel is due to the fact the semantics do not transpire in the word-image or syntax

    • The artificial language should therefore focus on the semantics (vs Latin, Esperanto, …)

    • A revival in the 17th century

John Locke (1632-1704) on the language delusion

John Locke (1632-1704) on the language delusion

17th century philosophers

  • many proposals, but most noteworthy:

    • John Wilkins

    • Gottfried Wilhelm Leibniz

      For more see: J.L. Borges, The analytic language of John Wilkins; Umberto Eco (1995) The search for the perfect language; Steve Pinker (1994) Words and Rules

The first ontological engineer: John Wilkins (1614-1672)

The first ontological engineer: John Wilkins (1614-1672)

  • Basic assumptions:

    • `to repair the ruins of Babel’

    • There is a limited number of primitive categories (elements)

    • Other concepts are combinations of primitives (molecules)

  • --> taxonomy/lattice

  • Classified about 2000 concepts

    • 40 top categories and many subcategories

    • A word is constructed by assigning a fixed syllable/letter to a string in descending this `graph’

    • Eg: Zita = animal (Z) + beast (i) + canine (t) + dog (a)

    • NB: the basis for `Roget’s thesaurus’ (1852, now)

  • An example: on measurement

    distinguishing `count/`mass’ nouns…

    …even including a definitions of NIL and of Concept


    “whatever can be named but cannot be thought”


    “thought in so far as it is a thought of something”

    Gottfried Leibniz (1646-1716)

    • Lingua Characteristica Universalis

    • More formal: numbers instead of pronounceable characters

      • eg animal=2, rational=3, human  6

        • philosophy = 5, philosopher -> 30

      • Calculus Ratiocinator

        • calculus is binary (1679) and based on prime numbers

        • this calculation can be performed mechanically

    Leibniz’ mechanical calculator

    • Portable

    • Inspired by Pascal, but also multiplication

    Gottfried Leibniz (1646-1716)

    • Lingua Characteristica Universalis

    • More formal: numbers instead of pronounceable characters

      • Eg animal=2, rational=3, human  6

        • Philosophy = 5, philosopher -> 30

      • Calculus Ratiocinator

        • calculus is binary (1679) and based on prime numbers

        • this calculation can be performed mechanically

    • Basic assumptions

      • All our ideas are compounded from a very small number of simple ideas, which form the alphabet of human thought (cf Pinker, 2008)

      • Complex ideas proceed from these simple ideas by a uniform and symmetrical combination, analogous to arithmetical multiplication.

    Leibniz’ ontological engineering:

    • Methodology:(De Arte Combinatoria, 1666)

      • Systematic identification of all simple concepts

      • Careful choice of `signs’ (eg prime numbers)

      • Rules for combination

    • Reuse: Wilkins’ top ontology as a starting point

    • Knowledge representation with inference engine(multiplication/resolution)

      • Prime numbers as signs

      • Binary calculus

      • (later: too simple…)

    • Mechanical rendering was planned

    Leibniz’ dream

    “Once the characteristic numbers of most notions are determined, the human race will have a new kind of tool, a tool that will increase the power of the mind much more than optical lenses helped our eyes, a tool that will be as far superior to microscopes or telescopes as reason is to vision”

    from: Leibniz, Philosophical Essays *)

    …even in service of dispute and justice: “Calculemus”

    • "The only way to rectify our reasonings is to make them as tangible as those of the Mathematicians, so that we can find our error at a glance, and when there are disputes among persons, we can simply say: Let us calculate [calculemus], without further ado, to see who is right.[16]"(The Art of Discovery 1685, Wiener 51, Wiener, Philip, 1951. Leibniz: Selections. Scribner.)

    • "...the plan I have had for a long time to reduce all human thinking to a calculation, such as we know it in algebra or the ars combinatoria...so that many arguments could be solved, the certain could be distinguished from the uncertain and even grades of probability could be measured. Then if two were arguing, they could say to each other `Let us calculate’“ (letter to Protestant Pietist Jacob Spener, July 1687)

    Dream or self delusion?

    • Medal on calculator:

      • Motto

        “superior to man”

      • Inscription:

        “The model of creation discovered by G.W.L”

        “one is enough for deriving everything from nothing”

    …publish or perish?

    • Known for…

      • “Theodicy” ( Voltaire Candide)

      • Invention of calculus (quarrel with Newton)

    • But wrote…

      • 43 volumes published from 1923 on: still going on

      • 15,000 letters, 1000 correspondents (e-mail…), 200K pages

    • Some justice two centuries later

      • Russell (1900)  inventor of formal logic

      • Physics: basis for relativity

      • Mechanical reasoning (Turing, Wiener, AI)

      • Ontologies for reasoning

      • Mechanized normative (legal) reasoning

      • Mechanized dispute resolution….

    Tim Berners-Lee’s dreams

    • The inventor of the Web

      • starting in 1990 at CERN (Geneva)

      • HTML and web-browsers

      • … and the rest is history…

    • “Weaving the Web: the past, present and future of the World Wide Web by its inventor Tim Berners-Lee” (1999, Orion Business Books)

    What the Web is intended for…

    Dream part 1

    “The Web is more a social creation than a technical one. I designed it for the social effect -- to help people work together -- and not as a technical toy. The ultimate goal of the Web is to support and improve our weblike existence in the world.” [p 133]

    What the Semantic Web is intended for

    Dream part 2

    “In communicating between people using the Web, computers and networks have as their job to enable the information space, and otherwise get out of their way. But doesn’t it make sense to also bring computers more onto action, to put their analytic power to work. In part two of the dream, that is just what they do. The first step is putting data on the web in a form that machines can naturally understand, or converting it to that form. This creates what I call a Semantic Web -- a web of data that can be processed directly or indirectly by machines.” [p191]

    A decade later (W3C): infrastructural standards for SW

    • Semantics are represented by ontologies

    • Ontologies are represented by a KR formalism

      • Note: ontologies were specifications in KE (using Ontolingua; CML, cf UML in SE)

      • On top of a layer cake of data-handling formalisms

      • KR formalism is intended for reasoning

        • Even suitable for blind trust (OWL-DL is decidable)

    Legal ontologies (from Nuria Casellas, 2008/9)

    Legal ontologies (from Nuria Casellas, 2008/9)

    HOWEVER, in practice

    • Not one of these ontologies is used for reasoning

    • The reasoner is only used for consistency checking: relevant for

      • Large ontologies

      • And/or if there are many properties etc.

      • Eg SUMO…

    • Use:

      • Information management (documents)

        • That is also what the current Semantic Web efforts are about (not only in legal domains)

      • Core ontologies (reuse?)

    When is REASONING with ontologies required?

    • In NLP text understanding

    • In modeling situations

      • Modeling situations = understanding

      • However: standard approach in KS: frames/decision trees with user-system dialogues that capture the stereotypical situations in a particular domain/task

      • If one has to make sure that all possible situations are handled, we need ontological reasoning for constructing a model, understandable by the machine.

        • Qualitative and Model-based reasoning

        • Legal reasoning about cases

    For instance: TRACS (1990 – 1994)

    • Testing a new Dutch traffic code (RVV-90)

      • art. 3 Vehicles should keep to the right

      • art. 6 Two bicycles may ride next to each other

      • art. 33 A trailer should have lights at the back

    • Questions

      • Consistent?

      • Complete?

      • In what respect different from RVV-66 (old one)?

    • These can only be answered when we can model all possible situations distinguished by the law

    Traffic participants: a part of the ontology (`world knowledge’)




    driver of

    motor vehicle





    bus driver

    lorry driver

    car driver

    Simple example of ontological reasoning

    • Ontology (T-Box)

      • Subsumes (Physical_object, Car)

      • Right-of (Physical_object, Physical_object)

      • Inv(Right_of, Left_of)

    • Case description (A-Box)

      • car1 is-a Car

      • car2 is-a Car

      • Right_of (car1, car2)

    • Classification (eg Pellet) 

      • Left_of (car2, car1) (A-Box)

    • …simple as that, but necessary

    Btw: some surprising results

    Tram on tramway

    Car on bicycle lane

    Superior to man??

    OWL-DL (2) is suitable for more than ontology alone…


      • Normative reasoning simultaneously with ontological reasoning using OWL-DL

      • … more at course on Wednesday


        Saskia van de Ven, Joost Breuker, Rinke Hoekstra, Lars Wortel, and Abdallah El-Ali. Automated legal assessment in OWL 2. In Legal Knowledge and Information Systems. Jurix 2008: The 21st Annual Conference, Frontiers in Artificial Intelligence and Applications. IOS Press, December 2008.

        András Förhécz and György Strausz, Legal Assessment Using Conjunctive Queries, Proceedings LOAIT 2009

    …but we were talking `semantics’..

    • What is the relation between knowledge and semantics?

    • Semantics is the result of applying knowledge to data:

      • It gives meaning to data (signs)  information

      • In the process of understanding, those `properties’ of terms are selected that make up a coherent `macro-structure’ (model).

      • It is contextualized knowledge

    • Can that be captured in an ontology?

    Knowledge, ontology and meaning

    • What we know about terms vs what terms mean in a particular context (domain, document, phrase,..)

    • There is more to knowledge than ontology

      • Ontology (terminology) provides the basic units for understanding

      • Regular combinations: patterns of concepts

        • Scripts & frames: experience, heuristics, associations

        • Learning: from basic concepts  skill acquisition

        • Meaningful experience can only be based upon understanding!

    …approximately like this



    meaning/ sense








    and other



    knowledge and semantics (cf Levelt, “Speaking”, CUP 1993, fig 3.1)

















    and other



    knowledge and semantics (cf Levelt, “Speaking”, CUP 1993, fig 3.1)










    Context dependency in meaning  sense

    what is this?

    this is a car

    • in traffic: a car is a vehicle, moves, transports,…

    • for the mechanic: a car is a device, has a motor, etc

    • for a car salesman: a car is a commodity, has a price, a colour, accessories,…

    • for an insurance inspector: “is this a car or a wreck?”


    level of





    context dependency

    context dependency of meaning

    • the more abstract, the less properties, and the less possible variation in meaning/sense

    • context dependency: views select properties


    level of





    context dependency

    levels of ontologies

    • top, upper, foundational ontologies

      • the `primitives’ on which we build our knowledge (eg space, time, object, process, substance, etc.)

    • core ontologies:

      • some field of practice, discipline (e.g. medicine, law, etc.)

    • domain ontologies:

      • the domain of interest, e.g. (Dutch) traffic law,


    • In information management no real understanding is required!

      • In IM/KM the issue is `what is the topic of discourse’; not what is said about it (comment): that is for the human reader

      • Retrieval via word-keys from ontology

        • The hierarchical structure allows for covering variations in the specific use of words

        • Domain specific statistical associations may enhance the identification of more complex topics

        • Also: conventional patterns can be identified (cf MetaLex)

      • Adding `semantics’ by annotation

        • Labels from an ontology

    ..Towards real semantics for the SW: from IR to QA?

    • Semantics in IR (Yahoo research)

      • Thematic role structures (case grammar) as annotations

        • Verb (action) as pivot; single phrases, …

        • Automatic parsing of all sentences in a document

        • Full Wikipedia!

      • Results: minimal improvements in recall and precision (Yahoo, Wikipedia)

    • The real SW should search AND answer questions…

      • That still is the dream

    ..which begs the question…

    • Why OWL?

      • RDF, SKOS, UML are sufficient for using ontologies as data-schemas

      • Data-schemas vs ontologies

        • Data schema’s describe database individuals

        • Ontologies provide classes to identify entities in the world

    • Why ontologies, and not simple, somewhat structured lexicons in IM/KM?

      • Eg Wordnet ( Jurwordnet) is sufficient to disambiguate mappings between words and terms (concepts)

      • It would keep the distinction between the lexicon and semantics clean








    and other



    knowledge and semantics (cf Levelt, “Speaking”, CUP 1993, fig 3.1)












    • The mother of ontologies is not Ontology but `lingua philosophica’ and combinatorics

    • Although designed for the Semantic Web, OWL is rather to be used for `ontological reasoning’

      • Ontology as the basic part of a knowledge base

      • OWL-DL is the most suitable solution to perform valid ontological reasoning.

        • To be complemented with a rule formalism for frameworks

        • HARNESS

    • The current (legal) Semantic Web is aimed at information management: not at real semantics

      • Information retrieval vs question answering

    Some morals…

    • Current SW/ontologies research is too easily focused on solutions that are approximate

      • Aiming at practical solutions (OK), but

      • The low hanging fruit is already harvested

      • Combining in projects fundamental research with developing practical solutions

        • At least we should attempt to come closer to real semantics

    My dreams…

    • A flexible mechanism for constraining the meaning of terms in context

      • Multiple classification is too rigid and does not `hide’ properties but add these (-> combinatorics)

    • A top-ontology that really reflects our basic, common sense notions

    • A Semantic Web that can answer questions, in particular `how’ and `why’ questions

    • ....

    In summary: the quest is for


    G.W. Leibniz

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