Semantic Web ME-E4300 Semantic Web,

Transcription

1 Semantic Web ME-E4300 Semantic Web, Jouni Tuominen Semantic Computing Research Group (SeCo),

2 Outline The idea of Semantic web Core of Semantic web Metadata, ontologies, reasoning Review of the technological solutions and standards Application domains 2

3 Fundamental barrier for the development of the web: machine-processability The web contents are created for human readers HTML, PDF, JPEG, Machine mediates and displays, but does not understand contents of the web E.g., a Finnish text article A web service machine helps human Requires machine- understandability of the contents A fundamental contradiction 3

4 How can web build a more intelligent web? 1. Applications are programmed to be more intelligent The contents stay as they are The machines operate more human-like 2. Contents are represented in a more intelligent way The contents are easier to understand Machines stay as stay are In practice, both ways are needed More intelligent systems process more intelligently represented contents 4

5 Approach 1: More intelligent applications Automatic interpretation of natural language is difficult Free form of the documents Semantics of the content Non-textual contents Pictures, sound, music, video, software, How to interpret algorithmically? More than the document itself is needed for interpretation Context + common sense needed Fundamental problems of artificial intelligence, easy for humans! Great scientific and technological challenges 5

6 Approach 2: Contents represented in a more intelligent way The foundation of Semantic web The information is stored in a way that even a stupid understands it! Human helps the machine - Machine can also help in this (user-friendly tools for semantic content creation) The development began in the beginning of the 2000s W3C Semantic Web Activity 2001 W3C Web Services Activity

7 Web generations 1G WWW: WWW pages for human interpretation HTML language 2G WWW: Structures for human/machine interpretation XML language 3G WWW: Semantic Web Meanings for human/machine use RDF(S) language 4G WWW: Ubiquitous web for humans and machines Semantics = new foundation for intelligent web services Semantic = understandable to machines 7

8 Limitations of non-semantic web: case MuseumFinland <artifact> <id>nba:h26069:467</id> <target>cup and plate</target> <material>porcelain</material> <creationlocation>germany</creationlocation> <creator>meissen</creator> </artifact> This metadata cannot answer the following questions: Find all vessels? Find all ceramic products? Find artifacts manufactured in Europe? Does the city of Meissen manufacture ceramics? 8

9 Semantic web solution: ontologies NBA-H :object cup and plate ; :object_concept object:cup ; :object_concept object:plate ; :material porcelain ; :material_concept object:porcelain ; :creationplace Germany ; :creationplace_concept place:germany ; :creator Meissen :creator_concept actor:meissen. Find all vessels? Find all ceramic products? Find artifacts manufactured in Europe? Does the city of Meissen manufacture ceramics? NBA-H place:germany creationlocation_concept object_concept object_concept material_concept... material:porcelain place ontology loc:partof object:cup object:plate place:europe place:meissen rdfs:subclassof... object ontology... object:vessel rdfs:subclassof actor ontology material ontology actor:meissen 9

10 Case Rijksmuseum Amsterdam: CHIP Demonstrator Example in Turtle notation VRA metadata schema (extension of Dublin Core) (Aroyo et al., 2007) A resource in the TGN ontology / vocabulary 10

11 Amsterdam in TGN 11

12 An Ontology Concept Hierarchy: Standard Upper Merged Ontology SUMO 12

13 Technological basis of Semantic web

14 The classical layer cake model Reasoning/ logic Trust Metadata Vocabularies/ ontologies (Tim Berners-Lee) 14

15 Metadata level

16 Why isn t XML alone sufficient for the basis of Semantic web? Interpretation of XML languages has to be defined in a domain-specific way Combining different XML languages is often difficult We need a markup language, whose interpretation is: - Commonly agreed - Cross-domain - Machine- understandable The semantics of XML is only in human brain - <ADDRESS> <NAME>Peter Programmer</NAME> <PHONE> </PHONE> </ADDRESS> - <ADDRESS> <NAME>Peter Programmer</NAME> <PHONE> </PHONE> </ADDRESS> 16

17 The Semantic web solution: RDF Resource Description Framework General metadata description language for web resources Relational model, not a syntax (as opposed to XML) - RDF description = directed graph Semantics is defined based on logic Syntax/serialization - XML-based RDF/XML, especially for machines - Simple triple notations (N3, Turtle, N-triples) for humans Standardized and commonly used - W3C draft W3C recommendation RDF 1.0, W3C recommendation RDF 1.1,

18 RDF Vocabulary Description Language: RDF Schema Definition of the vocabulary for RDF descriptions Object-oriented approach to web resource descriptions - Classes, instances, properties (Class, type) - Class hierarchies, inheritance (subclassof) - Constraints for the properties (domain, range) The interpretation of RDF(S) is defined based on logic - Enables reasoning W3C draft 2000 W3C recommendation RDF Schema 1.0, W3C recommendation RDF Schema 1.1,

19 RDF(S) Example (Maedche, 2002) 19

20 Metadata schemas Standardized formats for metadata descriptions A set of elements/properties, and Predefined value sets for them Different content types typically require different properties E.g., book vs. song vs. museum item Problems How the element values are represented? - Tarja Halonen vs. Halonen T vs. Sept 11, 2001 vs. 2001/09/11 What do the values mean? - glass, nokia, Pyhäjärvi How can different schema structures be combined? - writer vs. creator as a property 20

21 Example: Dublin Core Set of 15 general properties for different contents Dublin Core Metadata Element Set (ISO Standard 15836) - Title - Creator - Subject - Description - Publisher - Contributor - Data - Type - Format - Identifier - Relation - Source - Language - Coverage - Rights 21

22 Dublin Core (2) DCMI Metadata Terms defines tens of qualifiers/refinements, which specialize the semantics of Dublin Core elements E.g., accessrights < Rights Dumb-down principle A qualified version can always be replaced with a more generic one - I.e., a qualifier can only specify the meaning of an element The element values may have predefined encoding formats Vocabulary encoding scheme - Set of possible terms, e.g., listing of different resource types (text, image, ) Syntax encoding scheme - E.g., date

23 Dublin Core (3) Application Profiles Defines a combination of DC elements and qualifiers + value encoding formats for a given domain (e.g., for library data) Possible own extensions E.g., Visual Resources Association (VRA) Core New elements, such as measurements 23

24 Metadata Schema in HealthFinland 24

25 HealthFinland portal: Maija s eyeglasses PDF document on the web 25

26 Maija s eyeglasses: metadata in RDF form 26

27 Ontology Level

28 What is an ontology? An ontology is an explicit specification of a conceptualization...definitions need to be couched in some common formalism (Gruber, 1993) - Explicit: machine can understand - Common (shared): communication is possible - Formal: precisely defined Defines the concepts/objects and their relations in a given domain A first requirement for the humans and machines to understand each other 28

29 class-def animal class-def plant subclass-of NOT animal class-def tree subclass-of plant class-def branch slot-constraint is-part-of has-value tree class-def leaf EXAMPLE OF AN slot-constraint is-part-of has-value branch ONTOLOGY class-def defined carnivore subclass-of animal slot-constraint eats value-type animal class-def defined herbivore subclass-of animal slot-constraint eats value-type plant OR (slot-constraint is-part-of has-value plant) class-def herbivore subclass-of NOT carnivore class-def giraffe subclass-of animal slot-constraint eats value-type leaf class-def lion subclass-of animal slot-constraint eats value-type herbivore class-def tasty-plant subclass-of plant slot-constraint eaten-by has-value herbivore, carnivore 29

30 Human understandable Machine understandable Ontology types Numbers Thesaurus - relations - NT, BT, RT etc. Glossary - word list - little structure Taxonomy - relations - inheritance - constrains Axiomatized theory - formal system - logic-based Philosophical text Ontological complexity/depth 30

31 W3C standards for Semantic web ontologies/vocabularies SKOS Simple Knowledge Organization System Light-weight semantics E.g., for representing existing glossaries, classification schemes, thesauri OWL Web Ontology Language Rich semantics based on logic Supports more reasoning 31

32 IEEE Standard Upper Merged Ontology (SUMO) Goals Automated reasoning support in knowledge-based applications Interoperability - Define new data elements using SUMO and obtain mutual interoperability - Interoperability between applications using domain specific ontologies (that use SUMO) - Neutral interchange format for different systems Application areas E-commerce E-learning Natural language understanding tasks 32

33 Standard Upper Merged Ontology SUMO 33

34 SUMO principal distinctions 34

35 SUMO Object: 35

36 Cyc ontology 36

37 OpenCyc > 200,000 concepts > 2,000,000 assertions Linked to other semantic knowledge bases Available for download 37

38 AAT Art & Architecture Thesaurus - maintained by J. Paul Getty Trust - 7 main classes, concepts MAO ontology (museum domain, Finnish) abstract concepts agents events materials items archive and library material organisms environments 38

39 Universal List of Artist Names ULAN 120,000 instances 293,000 names 39

40 Geonames Classes: 9 feature classes, 645 feature codes Instances: 8 million geographical names, 6.5 million unique features, 2.2 million populated places, 1.8 million alternate names Registries and Wiki used for populating the ontology 40

41 TGN Thesaurus of Geographical Names 912,000 records 1.1 million names, place types, coordinates, and descriptive notes Places important for the study of art and architecture 41

42 Finnish Ontologies: ONKI 42

43 ONKI -> Finto 43

44 Metadata + Ontologies = Linked Data (Web of Data)

45 Finnish Biography center and libraries collect historical data of people person name occupation birth place P1 Akseli Gallen-Kallela artist Lemu P2 Gustaf Mannerheim marshal Askainen name Akseli Gallen-Kallela person type P1 occupation artist birth place Lemu type name Gustaf Mannerheim P2 occupation marshal birth place Askainen 45

46 Museum catalogues paintings Work name creator time Topic W1 Portrait of Mannerheim Akseli Gallen-Kallela 1929 Gustaf Mannerheim W2 Aino Triptych Akseli Gallen-Kallela 1891 Aino, Kalevala name Akseli Gallen-Kallela... creator W1 type painting time 1929 topic name Gustaf Mannerheim 46

47 Land survey maintains place registries municipality Askainen Helsinki Lemu Turku province Varsinais-Suomi Uusimaa Varsinais-Suomi Varsinais-Suomi municipality Lemu type type province part-of type part-of... part-of Varsinais-Suomi Finland Askainen Turku part-of 47

48 National library builds ontologies KOKO ontology endrant subclassof subclassof concept perdurant subclassof abstract physical object subclassof place subclassof subclassof time occupation municipality person artist province painting marshal 48

49 Semantic RDF graph combines them all: Web of Data concept subclassof endurant subclassof perdurant abstract subclassof physical object subclassof place subclassof subclassof time occupation municipality name Akseli Gallen-Kallela type person type type P1 occupation artist birth place Lemu creator type painting W1 type province type subclassof... topic time 1929 part-of Varsinais-Suomi part-of Finland P2 Gustaf Mannerheim name occupation marshal part-of part-of birth place Askainen Turku 49

50 Linked Data Web of Data Utilization of distributed work Aggregating massive cross-domain contents Linked Open Data thinking Semantic portals 50

51 Rule level

52 The idea of rules RDF/OWL semantics is based on logic Logic: new information can be derived from old by reasoning 52

53 SUMO knowledge representation Developed in KIF (Knowledge Interchange Format) A version of first order predicate logic Other versions exist (e.g., OWL) Size 1006 terms 4142 axioms 814 rules 53

54 Rule Markup Language RuleML Standardized XML notation for rules 54

55 Application example: MuseumFinland recommends Inference rules tell machine about the world E.g., that student s cap is related to parties E.g., that entities are related to each other if their superclasses are related to each other Etc. Based on the graph of metadata+ontologies, machine can: Reason interesting new relations between museum items, and Provide them to end users as recommendation links 55

56 Application example: MuseumFinland 56

57 Application domains of Semantic web Interoperability Information retrieval Recommender systems Knowledge management E-business and web services Profiling and customization 57

58 What is new? PROGRAMMING Object-oriented modeling ARTIFICIAL INTELLIGENCE Description logic semantics XML syntax, RDF data model,... WWW TECHNOLOGIES 58

59 What is the Semantic web? Content perspective: A new metadata layer on the web describing its contents in terms of shared vocabularies, i.e., ontologies Web as a global database system Web of Pages vs. Web of Data Application perspective: Machine-understandable web The meaning (semantics) of contents accessible to machines Enables human usage - Intelligent web services - Semantic interoperability Technological perspective: Next layers above XML W3C standards: RDF(S), OWL, SPARQL, etc. Metadata Ontology Rules 59

60 What s next on this course? Lectures on Wednesdays at in lecture hall (TUAS) Assignment support sessions on Thursdays at in computer class (TUAS) DL for Assignment DL for Assignment DL for Assignment 3 60