1 Ontologies Piek Vossen VU University Amsterdam

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1 Ontologies Piek Vossen VU University Amsterdam Slide 2 2 Overview Ontologies versus lexicons Ontological starting points Comparison of available ontologies Identity criteria Basic Formal Ontology Slide 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 Slide 4 4 World knowledge in Wordnet POS: v ID: ENG20-02177556-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: ENG20-02143689-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 Slide 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 Slide 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 Slide 7 7 Linking Open Data http://richard.cyganiak.de/2007/10/lod/ Slide 8 8 Evolution of the web Slide 9 9 Knowledge pyramid GOOGLE INDEX social networks web......... social computer networks RDF databases RDF databases RDF databases RDF databases social computer & human networks Slide 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); Slide 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) Slide 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 Slide 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 Slide 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 Slide 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 Slide 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 Slide 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 Slide 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. Slide 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 Slide 20 20 Comparing available ontologies Mascardi, Cord, 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. Slide 21 21 Basic Formal Ontology BFOhttp//www. ifomis.org/ 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 Slide 22 22 Cyc http://www.cyc.com/ 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 Slide 23 23 DOLCE http://www. loa-cnr.it/ 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 Slide 24 24 GFO http://www.onto-med.de/ontologies/gfo.html 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 Slide 25 25 PROTON http://proton.semanticweb.org/ 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 Slide 26 26 John Sowa http://www.jfsowa.com/ontology/ 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 Slide 27 27 SUMO/MILO SUMOhttp://www.ontologyportal.org/ 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 Slide 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 Slide 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 Slide 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. Slide 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 Slide 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 Slide 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 Slide 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 Slide 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 Slide 36 36 Typology of meta properties -O-I+/-D+RCATEGORYLOCATION, ENTITY -O-I+D-RUNDESIRABLE -O-I+D~RFORMAL ROLEPART, PATIENT -O-I-D-RATTRIBUTIONRED -O+I-D-RATTRIBUTION&TYPERED PERSON +O+I+/-D+RTYPEFLOWER, PERSON +O+I-D-RUNDESIRABLE +O+I-D~RPHASE SORTALCATERPILAR +O+I+D-RX +/-O+I+D~RMATERIAL ROLESTUDENT, FOOD -O+I+D-RUNDESIRABLE -O+I+/-D+RMERELY ESSENTIAL SORTALINVERTEBRATE MAMMAL +O-IINCOHERENT O = carries its own identity I = carries a identity condition, possibly inherited Slide 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 Slide 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 Slide 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 Slide 40countibility 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"> 40 Singularity and Plurality An individual is a singular whole iff its unifying relation is the t...