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Knowledge Representation Formalism for Building Semantic Web Ontologies

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Knowledge Representation Formalism for Building Semantic Web Ontologies Knowledge Representation Formalism For Building Semantic Web Ontologies Basak Taylan Department of Computer Science The Graduate Center, CUNY Jan 4, 2018 1 / 71 Outline 1 Introduction Web Evolution Semantic Web 2 Knowledge Representation Knowledge Knowledge Representation Formalism 3 Description Logic 4 Some Applications 2 / 71 Question IS A GIRAFFE BIGGER THAN AN APPLE? 3 / 71 Web Evolution - Motivation [1, 27, 34, 9, 4] 1.8+ billion websites/ 4.6+ billion web pages Textual/graphical resources for mostly human consumption Inadequacy of keyword-based searches No result Irrelevant results Highly dependent on keywords Inability of question answering Inefficiency of manual search Access time 4 / 71 Web Evolution - Web-1.0 [78, 18, 17, 20, 16, 31, 29, 65, 93] 1996-2004 Tim Berners Lee “Read Only, Hypertext Web” Static Content Millions of users One directional HTML Web1.0 version of yahoo Image source: https://ebusinessharper.wordpress.com 5 / 71 Web Evolution - Web-2.0 [31, 79, 29, 65, 93] 2004-2016 O’Reilly and MediaLive International “Read-Write, People-Centric, Participative-Web” Dynamic Content Billions of users https://mariamkhatib.files.wordpress.com/2013/02/picture- 6.png Bi-directional XML/ RSS 6 / 71 Web Evolution - Web-3.0 [31, 79, 29, 65, 93, 56] 2016- Tim Berners Lee “Semantic Web, Executable Web” Dynamic Content + AI Web Learning Trillions of users Multi-User Virtual Environment "Linking Open Data cloud diagram 2017, by Andrejs Abele, John P. McCrae, Paul Buitelaar, Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/" RDF/RDFS/OWL 7 / 71 Web Evolution - Web-3.0 DBpedia Example 8 / 71 Web Evolution - Web-4.0 [29, 93, 48] Near Future “Symbiotic Web” (human-machine interaction) Facial recognition for IoT The ultra-intelligent personal agent with personality selection Mind controlled interfaces Small screenless smart devices 9 / 71 Semantic Web [19, 107, 4, 52, 29, 65] Extension of current Web Layered structure Machine and human processable Improved results compared to absolute keyword-based searches Semantic Web Layer Cake [4] Search engines as question answering platform A common framework for data to be shared and reused across application, enterprise, and community boundaries 10 / 71 Unicode And Universal Resource Identifier(URI) [69] Unicode : Standardized encoding for the character settings for the Semantic Web. URI : identifiers for the resources on the Web. 11 / 71 Extensible Markup Language(XML) [4] <h2 > Nonmonotonic Reasoning: Context-Dependent Reasoning </h2 > <i>by <b>V. Marek</b>and <b>M. Truszczynski</b></i> <br> Springer 1993 <br> ISBN 0387976892 <book> < t i t l e > Nonmonotonic Reasoning: Context−Dependent Reasoning </ t i t l e > <author>V. Marek</author> <author>M. Truszczynski</author> <publisher>Springer</publisher> <year >1993</year> <ISBN>0387976892</ISBN> </book> Img source: https://images-na.ssl-images-amazon.com/images/I/41RXVgcRReL._SX327_BO1,204,203,200_.jpg 12 / 71 Resource Description Framework(RDF) [2, 35, 107, 4, 72, 111, 83] Defines web-based resources Consists of Subject(S)+Predicate(P)+Object(O) Similar to entity-relationship or class diagrams is databases XML defines individual objects RDF draws the relations between objects created by XML An RDF syntax: RDF/XML, N3, Turtle, and RDFa. 13 / 71 RDF Graphical Representation Example [2] A simple RDF graph representation [35] RDF statements can be modeled as directed, labelled graphs. Nodes ! S/O , arcs ! predicates 14 / 71 RDF Turtle Representation [35] @prefix s:<http://www.w3.org/employee/> <http://www.w3.org/employee/id1321> <s:hasName> <“Jim Lerners”>. <http://www.w3.org/employee/id1321> <s:authorOf> <http://www.books.org/ISBN0012515866> . <http://www.books.org/ISBN0012515866> <s:hasPrice> <“$62”>. 15 / 71 RDF Graphical Representation Example-2 A simple RDF graph representation [30] 16 / 71 RDF/XML Example <?xml version="1.0"?> <rdf:RDF xmlns:rdf="http://www.w3.org/TR/rdf-sytax-grammer/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:ex="http://example.org/terms/"> <rdf:Description rdf:about="http://www.w3.org/TR/rdf-syntax- grammar" dc:title="RDF1.1 XML Syntax"> <ex:editor> <rdf:Description ex:fullName="Dave Beckett"> <ex:homePage rdf:resource="http://purl.org/net/dajobe/" /> </rdf:Description> </ex:editor> </rdf:Description> </ rdf :RDF > 17 / 71 RDF Schema(RDFS)[4, 2] Language that provides vocabulary used in RDF data models Organizes web objects into hierarchies such as classes, subclasses, properties, domain/range restrictions Class hierarchy for the motor vehicles [4] 18 / 71 RDF Schema(RDFS)[4, 2] <rdf : RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"> <rdfs:Class rdf:ID="motorVehicle"/> <rdfs:Class rdf:ID="van"> <rdfs:subClassOf rdf:resource="#motorVehicle"/> </rdfs:Class> <rdfs:Class rdf:ID="truck"> <rdfs:subClassOf rdf:resource="#motorVehicle"/> </rdfs:Class> <rdfs:Class rdf:ID="passengerVehicle"> <rdfs:subClassOf rdf:resource="#motorVehicle"/> </rdfs:Class> <rdfs:Class rdf:ID="miniVan"> <rdfs:subClassOf rdf:resource="#passengerVehicle"/> <rdfs:subClassOf rdf:resource="#van"/> </rdfs:Class> </ rdf :RDF > 19 / 71 SPARQL Protocol and RDF Query Language(SPARQL)[2, 83, 4] Assume RDF Store consists of following triples: :JamesDean :playedIn :Giant :JamesDean :playedIn :EastOfEden :JamesDean :playedIn :RebelWithoutCause Q1) SELECT ?who WHERE{?who :playedIn :Giant.} RebelWithoutCause A1) JamesDean playedIn playedIn Q2) SELECT ?what WHERE{:JamesDean :playedIn ?what} JamesDean EastOfEden playedIn A2) Giant, EastOfEden, RebelWithoutCause Giant A simple RDF relation [2] 20 / 71 Web Ontology Language(OWL) [93, 74, 46, 45] An ontology is a language for representation of terms and their relations Provides standard vocabulary for machine processable Web. More expressive than XML, RDF, RDF(S). OWL extends RDF for describing properties and classes (disjointness, cardinality, equality, symmetry, transitivity, inverse, enumerated classes, etc.) Current version OWL2 is extended version of OWL. 21 / 71 OWL Examples Examples are taken from:"LDK R Logics for Data and Knowledge Representation Web Ontology Language (OWL) Fausto Giunchiglia and Biswanath Dutta Fall’2011." 22 / 71 Rules, Proof, Logic, Trust [4, 93] RIF/SWRL enables us to write rules beyond RDFS and OWL. Logic ,Proof, and Trust layers are used for validation of trustability of inputs. Digital signatures are used to verify origin of the sources for input data. Trust layer will be created by validation of trusted agents through digital signatures, certifications and other kinds of knowledge. 23 / 71 Knowledge [24, 54, 63, 102, 37, 55, 103, 44] Informally, the relation between knower and the known Zagzebski: “a state of a person’s being in cognitive contact with reality” Declarative: propositions of “what” is known (facts or assertions) Procedural: information about “how” to do things Heuristic: “shallow knowledge”, includes uncertainty, a chess player’s good guess Meta-knowledge: knowledge about how to use other knowledge 24 / 71 Knowledge Representation(KR) [23, 73, 32] Despite their computational power, computers cannot learn from the scratch. Computers should given information prior to task requires intelligence. i.e. Diagnosing a disease requires knowing characteristics of the disease in advance And information should be stored for future use. Need for representing and storing information lead study of KR Formalism. 25 / 71 Knowledge Representation 26 / 71 Semantic Nets [97, 26, 86, 99, 96, 13, 61, 6] 27 / 71 Conceptual Graphs(CG) [94, 95] Semantic Networks are insufficient in representing all features of logic. “On Fridays, Bob drives his Chewy to St. Louis” (8x1:Friday)(9x2:Drive)(9x3:Chevy)(9x4:Old) [Person Bob] [Chevy *x1] [Old *x2] (Poss Bob ?x1) (Attr (Person(Bob) ^ City("St. Louis") ^ PTim(x2,X1) ?x1 ?x2) [[Friday @every*x3] [Drive *x4] [City "St. Louis"] ^ Agnt(x2,Bob) ^ Poss(Bob,x3) ^ Thme(x2,x3) ^ (PTim ?x4 ?x3) (Agnt ?x4 Bob) (Thme ?x4 ?x1) (Dest Attr(x3,x4) ^ Dest(x2,"St. Louis")) ?x2 "St. Louis")] 28 / 71 Frames [76, 40, 64, 43, 49, 12, 42, 12, 43, 82] Data structure to represent stereotyped knowledge Inspired from human memory and cognition: new information brings a frame from the memory and gets it updated Sub/super-class relations are enabled by constructors Frame (frame-name <slot-name1 filler1> Individual Generic <slot-name2 filler2 > (objects) (categories) ... ) 29 / 71 Frames [40] The Transportation Knowledge Base 30 / 71 KL-ONE Systems [110, 22, 7, 25, 108, 89, 90, 98] Descendent of semantic nets and frames To overcome ambiguity and inconsistency of semantic nets and frames Ancestor of DLs Subsumption is intractable or undecidable even for Truck and TrailerTruck concepts defined in KL-ONE simple languages 31 / 71 Propositional Logic [88, 60, 81, 80, 87] Proposition: A statement evaluates to T/F Atom:Simplest proposition denoted with P,Q,etc. Complex statements can be created from atoms with logical connectors ^, _,:,!, and $ Not very expressive φ :φ φ ^ φ _ φ ! φ $ T T F T T T T Checking validity of a logical T F F F T F F formula with n propositional F T T F T T F n variables requires having 2 lines in F F T F F T T truth table 32 / 71 First-order Logic [14, 88, 101, 106, 53, 47] Extends PL with quantifiers Building blocks: Constant symbols(objects) : john, 2, paris,... Predicates(Relations): City(paris), CapitalOf(paris,france), ... Functions: maps individuals to individuals fatherOf(Mary) = John, plus(2,3)=5, etc. Highly expressive Suffers from complexity 33 / 71 Frame Logic (F-Logic) [58, 3, 57,
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