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1 Ontologies Piek Vossen VU University Amsterdam.

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1 1 Ontologies Piek Vossen VU University Amsterdam

2 2 Overview Ontologies versus lexicons Ontological starting points Comparison of available ontologies Identity criteria Basic Formal Ontology

3 3 Why ontologies? Lexicons of the future will depend on ontologies; –Semantic data in lexicon partially reflects world knowledge; –World knowledge is stored externally in for example the Open Data Cloud: network of RDF data resources Lexicons contain linguistic knowledge that is not in encyclopedia

4 4 World knowledge in Wordnet POS: v ID: ENG v BCS: 1 Synonyms: sell:1 Definition: exchange or deliver for money or its equivalent Domain: commerce SUMO/MILO: Selling -> [hypernym] exchange:1, change:7, interchange:1 transfer:5exchange:1, change:7, interchange:1 POS: v ID: ENG v BCS: 2 Synonyms: buy:1, purchase:1 Definition: obtain by purchase; acquire by means of a financial transaction Domain: commerce SUMO/MILO: Buying -> [hypernym] get:1, acquire:1get:1, acquire:1

5 5 SUMO Selling –(documentation Selling EnglishLanguage "A FinancialTransaction in which an instance of Physical is exchanged for an instance of CurrencyMeasure.")documentationSellingEnglishLanguageFinancialTransactionPhysical CurrencyMeasure Buying –(documentation Buying EnglishLanguage "A FinancialTransaction in which an instance of CurrencyMeasure is exchanged for an instance of Physical.")documentationBuyingEnglishLanguageFinancialTransactionCurrencyMeasurePhysical FinancialTransaction –(documentation FinancialTransaction EnglishLanguage "A Transaction where an instance of Currency is exchanged for something else.")documentationFinancialTransactionEnglishLanguage TransactionCurrency

6 6 Lexicon ontology mapping Lexicon: sell: subj(x), direct obj(z),indirect obj(y) buy: subj(y), direct obj(z),indirect obj(x) Ontology: (and (instance x Human)(instance y Human) (instance z Entity) (instance e FinancialTransaction) (source x e) (destination y e) (patient z e) The same process but a different perspective by subject and object realization: marry in Russian two verbs, apprendre in French can mean teach and learn

7 7 Linking Open Data

8 8 Evolution of the web

9 9 Knowledge pyramid GOOGLE INDEX social networks web social computer networks RDF databases RDF databases RDF databases RDF databases social computer & human networks

10 10 Ontologies versus Lexicons Lexicon contain the knowledge about words and expressions that are necessary to effectively communicate in a language; Lexicon interacts with grammar and discourse model; Lexical knowledge is part of general knowledge of the world; Lexical knowledge is subconscious knowledge (like playing piano) whereas our knowledge of the world is of a higher level (like theory of harmony);

11 11 Ontologies versus lexicons Language is an instrument for communication: –utterances are never completely descriptive –Minimal & sufficient information for a communicative effect (Gricean maxims)

12 12 News paper headings & captions Vrij Nederland Geknipt voor u Veel vrouwen verdienen minimumloon Herder bijt schaap Zwembad loopt leeg Dames lopen uit Winkelende vrouw raakt geld kwijt Dode zwemmer Vrouw draagt kruis paus Eieren gooien terug op braderie

13 13 Ontologies versus lexicons Speakers/writers make assumptions about the addressee: –Knowledge of the world (Schank ('70): grammar does not exist, conceptual dependencies) –Knowledge of language –Knowledge about the communicative settings

14 14 Ontologies versus lexicon Multilingual perspective sheds light on the delineation of lexical and world knowledge: –water = substance & mass noun –sand = substance & mass noun but granular –grass = substance & mass noun but granular –rice, bran (Dutch plural: zemelen), chives (Dutch uncount: bieslook) = substance? & mass noun or plural, oats (Dutch haver, havervlokken, havermeel) –forest = group noun, one, two forests (Dutch bos = group and mass, een, twee bossen, veel bos) Linguistic variation around border cases: –limited forms -> symbolic –infinite & analogue reality

15 15 Autonomous & Language-Specific voorwerp {object} lepel {spoon} werktuig{tool} tas {bag} bak {box} blok {block} lichaam {body} Wordnet1.5Dutch Wordnet bag spoon box object natural object (an object occurring naturally) artifact, artefact (a man-made object) instrumentality blockbody container device implement tool instrument

16 16 Artificial ontology: better control or performance, or a more compact and coherent structure. introduce artificial levels for concepts which are not lexicalized in a language (e.g. instrumentality, hand tool), neglect levels which are lexicalized but not relevant for the purpose of the ontology (e.g. tableware, silverware, merchandise ). What properties can we infer for spoons? spoon -> container; artifact; hand tool; object; made of metal or plastic; for eating, pouring or cooking Linguistic versus Artificial Ontologies

17 17 Linguistic ontology: Exactly reflects the relations between all the lexicalized words and expressions in a language. Captures valuable information about the lexical capacity of languages: what is the available fund of words and expressions in a language. What words can be used to name spoons? spoon -> object, tableware, silverware, merchandise, cutlery, Linguistic versus Artificial Ontologies

18 18 Wordnets versus ontologies Wordnets: autonomous language-specific lexicalization patterns in a relational network. Usage: to predict substitution in text for information retrieval, text generation, machine translation, word- sense-disambiguation. Ontologies: data structure with formally defined concepts. Usage: making semantic inferences.

19 19 Ontological starting points What is being defined: realists versus conceptualists –scientific definition of the world –cognitive, cultural perception and interpretation How much room for different perspectives? Engineering point of view: what is required by applications? Top level ontologies versus domain ontologies Principles for ontology design Sharing, re-use, interoperability

20 20 Comparing available ontologies Mascardi, Cordì, and Rosso (2008) 7 different Upper Ontologies: BFO, Cyc, DOLCE, GFO, PROTON, Sowas ontology, and SUMO, software engineering criteria: –Number of Dimensions. –Implementation language(s) –Modularity. –Use in Applications. –Alignment with WordNet. –Licensing.

21 21 Basic Formal Ontology BFOhttp//www. bfo DevelopersSmith, Grenon, Stenzhorn, Spear (IFOMIS) Dimensions36 classes related via is_a relation, ModulesSNAP snapshot ontologies indexed by times & SPAN single videoscopic ontology Applicationsbiomedical domain and used in building an ontology for clinic-genomic trials on cancer. Alignment wordnet NO LanguageOWL LicenseFree

22 22 Cyc Developers Cycorp Dimensions300,000 concepts,, 3,000,000 assertions (facts and rules), 15,000 relations Modules The microtheory approach supports modularity ApplicationsDomains of NLP, e.g.: WSD and Q&A, network risk assessment, terrorism-related Alignment wordnet Links to 12,000 synsets LanguageCycL, OWL LicenseCommercial, OpenCyce for research

23 23 DOLCE DOLCE.html DevelopersGuarino et al. of the LOA Dimensions100 concepts, 100 axioms Modules It is not currently divided into modules (planned). ApplicationsLOIS Project, SmartWeb, Language Technology for eLearning AsIsKnown Alignment wordnet Links to 100 synsets LanguageFirst Order Logic, KIF, OWL LicenseFree

24 24 GFO DevelopersOnto-Med Research Group Dimensions79 classes, 97 subclass relations, 67 properties Modules3-layered architecture: abstract top level, abstract core level, and basic level. Several ontological modules, incl. functions and roles ApplicationsOntological foundation of conceptual modelling and Biomedical science: Gene Ontology, Celltype Ontology, Chemical Entities of Biological Interest Ontology, GFO-Bio. Alignment wordnet NO LanguageFirst Order Logic and KIF (forthcoming); OWL Licensereleased under the modified BSD Licence

25 25 PROTON DevelopersOntotext Lab, Sirm Dimensions300 concepts, 100 properties Modules3 levels including 4 modules. ApplicationsDifferent domains and purposes, e.g. semantic annotation, knowledge management systems in legal and telecommunications domain (projects MediaCampaign, ISTWorld, Business Data Ontology for Semantic Web Services) Alignment wordnet NO LanguageOWL Lite LicenseFree

26 26 John Sowa DevelopersSowa Dimensions30 classes, 5 relationships, 30 axioms ModulesNot explicitly divided into modules ApplicationsInspired many other upper ontologies, Alignment wordnet NO Language1st Order Modal Language,KIF LicenseFree

27 27 SUMO/MILO SUMO DevelopersNiles, Pease, Menzel Dimensions20,000 terms, 60,000 axioms (incl.domain ontologies) ModulesMId-Level Ontology, and ontologies for a range of specialized domains ApplicationsMany papers report on usage (from academic to govern-ment, to industrial), among which NLP, pure representation and reasoning. Alignment wordnet All synsets of WN3.0 LanguageSUO-KIF, LicenseOWL

28 28 Ontoclean Guarino - Welty Methodology for designing and building ontologies that ease re-use and integration Intuitions on how we, as cognitive agents, interact with the world (sensory system, cognition & culture) Purpose to design ontologies for information systems

29 29 Basic Notions Identity through an essential (intrinsic) property, e.g. DNA, a persons brain What properties can change while maintaining identity Other ways of establishing identity: –Being a member of a class: does not keep the invidividual members apart –Global unique Ids: hacks that does not explain how two descriptions can be the same

30 30 Identity criteria (Guarino and Welty) Rigidity: to what extent are properties of an entity true in all or most worlds? E.g., a man is always a person but may bear a Role like student only temporarily. Thus manhood is a rigid property while studenthood is anti-rigid Essence: which properties of entities are essential? For example, shape is an essential property of vase but not an essential property of the clay it is made of. Unicity: which entities represent a whole and which entities are parts of these wholes? An ocean or river represents a whole but the water it contains does not.

31 31 Individuals and Concepts The term "meta-property" adopted here is based on a fundamental distinction within the domain of discourse: individuals or particulars vs. concepts or universals Meta-level properties induce distinctions among concepts, while object-level properties induce distinctions among individuals

32 32 Rigidity A property is essential to an individual iff it necessarily holds for that individual A property is rigid (+R) iff, necessarily, it is essential to all its instances. A property is non-rigid (-R) iff it is not essential to some of its instances, and anti-rigid (~R) iff it is not essential to all its instances Person vs Student

33 33 Identity A property carries an identity criterion (+I) iff all its instances can be (re)identified by means of a suitable sameness relation. A property supplies an identity criterion iff such criterion is not inherited by any subsuming property Person vs. Student

34 34 Dependence An individual x is constantly dependent on y iff, at any time, x can't be present unless y is fully present, and y is not part of x. Ex: Hole/Host A property P is constantly dependent (+D) iff, for all its instances, there exists something they are constantly dependent on. Here Dependent = Constantly Dependent

35 35 Types vs. Roles A rigid property that supplies an identity criterion and is not (notionally) dependent is called a type. An anti-rigid property that is notionally dependent is called a role. It is a material role if it carries (but not supplies) an identity criterion, and a formal role otherwise. Person vs. Student vs. Part


37 37 Typology of meta properties property Formal Property -I Category: -I,+R Attribute: -I,-R,-D Formal role:-I,~R,+D Material role:+I,+D,~R Phase sortal:+I,-D,~R Type&Attribute:+I,-D,-R Type:+I,+R Merely essential sortal:+I+R Role ~R,+D Anti- Essential ~R Non- Essential -R Essential ~R Sortal +I entity, location red, male part, patient student, food caterpilar red apple apple, person invertebrate mammals non = not essential to some anti = not essential to all

38 38 Extensionality An individual is said to be extensional iff, necessarily, everything that has the same proper parts is identical to it: amount of matter A property is extensional (+E) iff, necessarily, all its instances are extensional A property is anti-extensional (~E) iff, necessarily, all its instances are non-extensional, so that they can possibly change some parts while keeping their identity: persons and their bodies

39 39 Unity An individual is unified by a (suitably constrained) relation R iff it is a mereological sum of entities that are bound together by R. Ex. the relation having the same boss may unify a group of employees in a company -> establishes a group An individual w is a whole under R iff it is maximally unified by R, in the sense that R is internal to w, and no part of w is linked by R to something that is not part or w A property P is said to carry unity (+U) if there is a common unifying relation R such that all the instances of P are essential wholes under R. A property carries anti-unity (~U) if all its instances can possibly be non-wholes. If every instance of P is an essential whole, but there is no unifying relation common to all instances of P, then we mark P with the property *U

40 40 Singularity and Plurality An individual is a singular whole iff its unifying relation is the transitive closure of the relation "strong connection", like that existing between two 3D regions that have a surface in common. Topological wholes of this kind have a special cognitive relevance, which accounts for the natural language distinction between singular and plural -> countibility A plural individual is a sum of singular wholes that is not itself a singular whole. Plural individuals may be wholes themselves or not. In the former case they will be called collections; in the latter case pluralities A piece of coal is a singular whole. A lump of coal is a topological whole, but not a singular whole, since the pieces of coal merely touch each other, with no material connection. It is therefore a plural whole

41 41 Useful property kinds CO = countable properties, carry +I & +U ME = extensional IC, pluralities amounts of matter UT = topological unity, physical or topological connection, parts of an apple, imply +U UM = morphological unit, UT + shape, e.g. ball, imply +U UF = functional unity, composed for a purpose, e.g. bikini, imply +U

42 42 Messy taxonomy entity:-I-U-D+R Location Amount of matter RedAgent Group Country Physical Object Living being Fruit Food Apple Red Apple Caterpillar Butterfly Animal Vertebrate Person Organization Group of people Social entity Legal entity

43 43 Methodology Analyse each property according to meta- properties Remove all properties except for categories and essential sortals Remove subsumption between incompatible identity conditions Add Phasal sortals Add attributes, roles and mixed types

44 44 Some conflicts car -> physical object + amount of matter animal -> living being + physical object organization -> group of people (+ME) + social entity (-ME) + legal agent

45 45 Cleaner taxonomy entity:-I-U-D+R Location +O-U-D+R Amount of matter +O-U-D+R Group +O~U-D+R Physical Object +O+U-D+R Living being +O+U-D+R Fruit Apple Animal Vertebrate:+I Person Organization +O+U-D+R Group of people:+I Social entity -I+U-D+R

46 46 Clean taxonomy entity:-I-U-D+R Location Amount of matter Red Agent Group Region Physical Object Living being Fruit Food Apple Red Apple CaterpillarButterfly Animal Vertebrate Person Organization Group of people Social entity Legal entity Country Lepidopteran +o-u-d+r +o+u -d+r +i-o~u+d~r +i+o+u-d-r +l+u-d~r +o+u-d+r +i-o~u-d+r +o+u-d+r

47 47 Basic Formal Ontology Realist approach to ontology, based on science: –independent of our linguistic, comceptual, theoretical, cultural representations –reality existed before humans Perspectivalism: –there are many different representations that are equally good: -> different levels of granularity (atoms, molecules, organisms, ecosystems, galaxies) Fallibilism: science can be wrong Adequate: given the domain choose the adequate granularity

48 48 Substances and processes exist in time in different ways substance t i m e process

49 49 Snapshot Video ontology ontology substance t i m e process

50 50 SNAP vs SPAN Objects vs. events Continuants vs. occurrents Nouns vs. verbs In preparing an inventory of reality we keep track of these two different kinds of entities in two different ways

51 51 SNAP and SPAN anatomy and physiology

52 52 SNAP: Entities existing in toto at a time

53 53 SPAN: Entities extended in time

54 54 SNAP-SPAN Participation Perpetration (+agentive) Initiation Perpetuation Termination Influence Facilitation Hindrance Mediation Patiency (-agentive)

55 55 Realization (SNAP-SPAN) the execution of a plan, algorithm the expression of a function the exercise of a role the realization of a disposition

56 56 Material examples: SPAN SNAP expression of an emotion utterance of a sentence application of a therapy course of a disease increase of temperature

57 57 SPAN SNAP Involvement Creation Sustaining in being Destruction Demarcation Blurring Degradation

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