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Semantic Web Applications: Past, Present and Future Oscar Corcho Universidad Politécnica de Madrid Florianópolis, August 31st 2010.

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Presentation on theme: "Semantic Web Applications: Past, Present and Future Oscar Corcho Universidad Politécnica de Madrid Florianópolis, August 31st 2010."— Presentation transcript:

1 Semantic Web Applications: Past, Present and Future Oscar Corcho Universidad Politécnica de Madrid Florianópolis, August 31st 2010 (OntoBras 2010) Acknowledgements: Asunción Gómez-Pérez, Jesús Barrasa, Angel López Cima, Oscar Muñoz, Jose Angel Ramos Gargantilla, María del Carmen Suárez de Figueroa, Boris Villazón, Mariano Fernández López, Luis Vilches, Carlos Ruíz Moreno Work distributed under the license Creative Commons Attribution- Noncommercial-Share Alike 3.0

2 Overview Coming to terms: The Web (1.0 and 2.0), the Semantic Web, the Web of Linked Data and all its applications The Web (1.0 and 2.0) Web applications The Semantic Web (pre-SemanticWeb, SW1.0 and SW3.0) Semantic Web Applications Or [Semantic | Web]+ Applications The Web of Linked Data Linked Data Applications Semantic-based Applications preSemanticWeb Applications Annotation Semantic Web 1.0 Applications Annotation, Data Integration and Decision Support Systems Semantic Web 3.0 Applications (Collaborative) Annotation and Data Integration Conclusions and Trends

3 Health and Safety Notice Classification Disclaimer : This is not the only way that applications can be classified or grouped. In fact, many other possibilities exist for the classification of Semantic Web application.

4 Overview Coming to terms: The Web (1.0 and 2.0), the Semantic Web, the Web of Linked Data and all its applications The Web (1.0 and 2.0) Web applications The Semantic Web (pre-SemanticWeb, SW1.0 and SW3.0) Semantic Web Applications Or [Semantic | Web]+ Applications The Web of Linked Data Linked Data Applications Semantic-based Applications preSemanticWeb Applications Annotation Semantic Web 1.0 Applications Annotation, Data Integration and Decision Support Systems Semantic Web 3.0 Applications (Collaborative) Annotation and Data Integration Conclusions and Trends

5 The beginning: Web 1.0 WWW HTTP URI

6 From Web1.0 to Web2.0 More than 30M pages More than 1000M users WWW HTTP, HTML, URI New requirements start arising Cooperation Dynamicity Decentralised change Heterogeneity Multimedia content

7 Web2.0 basic sites and services

8 Web1.0 vs Web2.0 Cooperation Dynamicity Decentralised change Heterogeneity Multimedia content

9 Web Applications Who doesn’t know what is a Web application? Let’s define it A web application is an application that is accessed over a network such as the Internet or an intranet. The term may also mean a computer software application that is… … hosted in a browser-controlled environment (e.g. a Java applet) … or coded in a browser-supported language (such as JavaScript, combined with a browser-rendered markup language like HTML) … and reliant on a common web browser to render the application executable. Some comments Too many technology-related terms in the definition No mentions to the evolution of user-generated content (Web1.0  Web2.0), although it is already well understood.

10 Overview Coming to terms: The Web (1.0 and 2.0), the Semantic Web, the Web of Linked Data and all its applications The Web (1.0 and 2.0) Web applications The Semantic Web (pre-SemanticWeb, SW1.0 and SW3.0) Semantic Web Applications Or [Semantic | Web]+ Applications The Web of Linked Data Linked Data Applications Semantic-based Applications preSemanticWeb Applications Annotation Semantic Web 1.0 Applications Annotation, Data Integration and Decision Support Systems Semantic Web 3.0 Applications (Collaborative) Annotation and Data Integration Conclusions and Trends

11 (Syntactic) Web Limitations A place where computers do the presentation (easy) and people do the linking and interpreting (hard). Why not get computers to do more of the hard work? Resource href

12 What is the Semantic Web? An extension of the current Web… … where information and services are given well-defined and explicitly represented meaning, … … so that it can be shared and used by humans and machines, better enabling them to work in cooperation How? Promoting information exchange by tagging web content with machine processable descriptions of its meaning. And technologies and infrastructure to do this

13 Need to Add “Semantics” Agreement on the meaning of annotations Shared understanding of a domain of interest Formal and machine manipulable model of a domain of interest An ontology is an engineering artifact, which provides: A vocabulary of terms A set of explicit assumptions regarding the intended meaning of the vocabulary. Almost always including concepts and their classification Almost always including properties between concepts Besides... The meaning (semantics) of such terms is formally specified New terms can be formed by combining existing ones Can also specify relationships between terms in multiple ontologies

14 Ontology Languages A large amount of work on Semantic Web has concentrated on the definition of a collection or “stack” of languages. Used to support the representation and use of metadata Basic machinery that we can use to represent the extra semantic information needed for the Semantic Web RDF(S) Integrating information sources Associating metadata to resources (bindings) OWL Integration RDFS RDF XML Annotation Integration Inference Reasoning over the information we have Could be light-weight (taxonomy) Could be heavy-weight (logic-style) SWRL

15 The evolution of the Semantic Web Cooperation Dynamicity Decentralised change Heterogeneity Multimedia Semantic Web 1.0Semantic Web 3.0pre-Semantic Web No standardised formats e.g., (KA) 2 RDFS, OWL Semantic Web Challenge

16 [Semantic | Web]+ Applications (I) No definition in Wikipedia… ;-( Why [Semantic | Web]+ application?

17 [Semantic | Web]+ Applications (II) Why [Semantic | Web]+ application? Most of them are focused on the use of semantics In fact, probably it would be better to use Semantic [Web]* application However, many of them are not so Web-oriented E.g., very common in data integration approaches apps_to_watch.php A key element [of a Semantic Web App] is that the apps all try to determine the meaning of text and other data, and then create connections for users. Besides, data portability and connectibility are keys to these new semantic apps - i.e. using the Web as platform.

18 Overview Coming to terms: The Web (1.0 and 2.0), the Semantic Web, the Web of Linked Data and all its applications The Web (1.0 and 2.0) Web applications The Semantic Web (pre-SemanticWeb, SW1.0 and SW3.0) Semantic Web Applications Or [Semantic | Web]+ Applications The Web of Linked Data Linked Data Applications Semantic-based Applications preSemanticWeb Applications Annotation Semantic Web 1.0 Applications Annotation, Data Integration and Decision Support Systems Semantic Web 3.0 Applications (Collaborative) Annotation and Data Integration Conclusions and Trends

19 What is the Web of Linked Data? An extension of the current Web… … where information and services are given well-defined and explicitly represented meaning, … … so that it can be shared and used by humans and machines, better enabling them to work in cooperation How? Promoting information exchange by tagging web content with machine processable descriptions of its meaning. And technologies and infrastructure to do this And clear principles on how to publish data data

20 What is a Linked Data application Again, no definition yet Linked Data is a term used to describe a recommended best practice for exposing, sharing, and connecting pieces of data, information, and knowledge on the Semantic Web using URIs and RDF. So every element from the definition of SW application applies

21 Overview Coming to terms: The Web (1.0 and 2.0), the Semantic Web, the Web of Linked Data and all its applications The Web (1.0 and 2.0) Web applications The Semantic Web (pre-SemanticWeb, SW1.0 and SW3.0) Semantic Web Applications Or [Semantic | Web]+ Applications The Web of Linked Data Linked Data Applications Semantic-based Applications preSemanticWeb Applications Annotation Semantic Web 1.0 Applications Annotation, Data Integration and Decision Support Systems Semantic Web 3.0 Applications (Collaborative) Annotation and Data Integration Conclusions and Trends

22 The Web

23 Semantic Webs

24 The web Metadata Ontologies

25 The Web of Data

26

27 Resources Metadata Alignments Onto. - Schema Data Sources Ontologies

28 Overview Coming to terms: The Web (1.0 and 2.0), the Semantic Web, the Web of Linked Data and all its applications The Web (1.0 and 2.0) Web applications The Semantic Web (pre-SemanticWeb, SW1.0 and SW3.0) Semantic Web Applications Or [Semantic | Web]+ Applications The Web of Linked Data Linked Data Applications Semantic-based Applications preSemanticWeb Applications Annotation Semantic Web 1.0 Applications Annotation, Data Integration and Decision Support Systems Semantic Web 3.0 Applications (Collaborative) Annotation and Data Integration Conclusions and Trends

29 Annotation-focused applications: key characteristics Available at all stages (pre-Semantic Web, SW1.0 and SW3.0), although predominantly in the early ones Single (usually small) ontologies, many of them built manually Centralised ontologies Instances stored in a centralised manner, together with the ontologies, or in separate files/DBs Low heterogeneity and relatively small scale Homogeneous quality in data

30 Annotation in the pre-Semantic Web (KA) 2

31 O1O1 O2O2 OiOi OjOj Portal Administrators Ontologies and Software Extranet Users Agents Permission-based Semantic Driven User Oriented External resources Semantic Web Portals

32 Extranet view

33 Content Edition

34 Workpackage Deliverable has associated has Q.A. partneris generated by Organization Semantic-based Visualisation

35 Extranet View (RDF lives behind)

36 Overview Coming to terms: The Web (1.0 and 2.0), the Semantic Web, the Web of Linked Data and all its applications The Web (1.0 and 2.0) Web applications The Semantic Web (pre-SemanticWeb, SW1.0 and SW3.0) Semantic Web Applications Or [Semantic | Web]+ Applications The Web of Linked Data Linked Data Applications Semantic-based Applications preSemanticWeb Applications Annotation Semantic Web 1.0 Applications Annotation, Data Integration and Decision Support Systems Semantic Web 3.0 Applications (Collaborative) Annotation and Data Integration Conclusions and Trends

37 Data integration applications: key characteristics Available at later stages (SW1.0 and SW3.0). Still single (usually small) ontologies, many of them built manually Although sometimes mappings between local and global ontologies Still centralised ontologies Instances live in distributed DBs, with a focus on run- time queries, although also data warehousing approach Medium heterogeneity and medium scale Heterogeneous quality in data

38 Migrating IGN (Instituto Geográfico Nacional) sources 38

39 NC NGN BCN200 BCN25 Query: ¿Edif. Religioso de Soria? Response: Catedral Soria Ig. Sto. Tomás Catedral Soria Ermita N.S. Nieves Catedral Soria Soria Cated. Ig. Sto. Cated. Soria Cated. NS Nieves Edif. Religioso Construcción Rel. Catedral Ermita IGN Catalogue Integration: Exploitation of Mappings

40 Slide 40 UN FAO Example

41 Alignments between ontologies and the DB Land areas Fishing areas Biological entities Fisheries commodities Vessel types and size Gear types R 2 O Document R 2 O Document R 2 O Document R 2 O Document R 2 O Document R 2 O Document FAO FIGIS DB

42 Overview Coming to terms: The Web (1.0 and 2.0), the Semantic Web, the Web of Linked Data and all its applications The Web (1.0 and 2.0) Web applications The Semantic Web (pre-SemanticWeb, SW1.0 and SW3.0) Semantic Web Applications Or [Semantic | Web]+ Applications The Web of Linked Data Linked Data Applications Semantic-based Applications preSemanticWeb Applications Annotation Semantic Web 1.0 Applications Annotation, Data Integration and Decision Support Systems Semantic Web 3.0 Applications (Collaborative) Annotation and Data Integration Conclusions and Trends

43 Decision support applications: key characteristics Again, available at later stages (SW1.0 and SW3.0). Still predominantly single (usually small) ontologies, many of them built manually But mostly heavyweight (they are the ones taking decisions) Heavy use of logic Still centralised ontologies Instances may live together with the ontologies, in distributed DBs, or in separate RDF files/triplestores. Annotation phases are common Medium heterogeneity and low/medium scale Heterogeneous quality in data

44 Satellite Image Processing Space Segment Ground Segment DMOP files Product files SATELLITE FILES:

45 Comparison between planning and product generation... Instr#n (RA_2) planning DMOP_File#n(StartTime)DMOP_File#n(StopTime) DMOP_File#(n+1) StartTime DMOP#(n+1)_ File (StopTime) DMOP_er (ORBIT_NUMBER, ELAPSED_TIME) Instr#1 planning DURATION PRODUCT_FILE Start_time (SENSING_START) PRODUCT_FILE Stop_time (SENSING_STOP)... Instr#n(RA_2) Product Generation RA2_CAL_1P Stop_time (SENSING_STOP) RA2_CAL_1P Start_time (SENSING_START) PRODUCT_data_gap...

46 Generating files in RDF FILE ; DMOP (generated by FOS Mission Planning System) RECORD fhr FILENAME="DMOP_SOF__VFOS _103709_ _ _ _014048_ _ N1" DESTINATION="PDCC" PHASE_START=2 CYCLE_START=44 REL_START_ORBIT=404 ABS_START_ORBIT=20498 ENDRECORD fhr RECORD dmop_er RECORD dmop_er_gen_part RECORD gen_event_params EVENT_TYPE=RA2_MEA EVENT_ID="RA2_MEA_ " NB_EVENT_PR1=1 NB_EVENT_PR3=0 ORBIT_NUMBER=20521 ELAPSED_TIME= DURATION= ENDRECORD gen_event_params ENDRECORD dmop_er ENDLIST all_dmop_er ENDFILE RECORD ID RECORD parameters RECORD parameters corresponding to other RECORD structure. MS "GOM_OCC_ "

47 The planning files The product files 1 reference ontology for annotating all files RDF files are distributed Distributed Metadata for Planning files Distributed Metadata for Product files 1 Ontology

48 Satellite Use Case (System Infrastructure): S-OGSA Scenario 48 WS-DAIOnt SatelliteDomain Ontology Grid-KP XML Summary File Annotation front-end Atlas MetadataQuery Service QUARC-SG client JSP Annotate file Obtain ontology Create Query Input criteria Select files to be annotated Metadata generation process Metadata querying process RD F Planning file server Germany Product file server Italy GT4 File directory Spain 1a Get file names Get file summaries 2 ONTO-DSI WebDAV 5 RDF File Upload SemanticBinding Service 7 Store 2’ Upload XML Summary file OverlapChecking Service 8 Store (start-time, stop-time, gen-time, EPR) 8 Notify (start- time, stop-time) 9 Destroy (if needed)

49 Fraud detection in car insurance

50 Fraud Diagnosis

51 Overview Coming to terms: The Web (1.0 and 2.0), the Semantic Web, the Web of Linked Data and all its applications The Web (1.0 and 2.0) Web applications The Semantic Web (pre-SemanticWeb, SW1.0 and SW3.0) Semantic Web Applications Or [Semantic | Web]+ Applications The Web of Linked Data Linked Data Applications Semantic-based Applications preSemanticWeb Applications Annotation Semantic Web 1.0 Applications Annotation, Data Integration and Decision Support Systems Semantic Web 3.0 Applications (Collaborative) Annotation and Data Integration Conclusions and Trends

52 Collaborative SW applications: key characteristics Fully-fledged in the last stage (SW3.0). Networks of heterogeneous ontologies Some of them built manual, some automatically Some of the lightweight and some others heavyweight (although normally not used in a heavyweight form) Dynamic finding of ontologies and terms Decentralised ontologies (available in URLs or search engines) Distributed instances living anywhere Annotation and integration phases are common Instances are created by users Large heterogeneity (in domains, quality, provenance, forms – RDF, tags, etc. -, etc.) Large scale

53 GeoBuddies: A pilgrim in St. James’ Way Diverse routes for pilgrims Self-emergent community of pilgrims People that talk about their experiences during the way People that join together in the joy of walking Mobile users People want to Find interesting locations Find community services Provide information

54 GeoBuddies: architecture and main themes Agile methods for Web2.0 data integration Facebook Flickr … Mobile applications exploiting user generated content Evolution of folksonomies and ontologies

55 Servidor de anotaciones El usuario ve un punto de interés y envía una foto con sus correspondientes anotaciones walk sun tired cathedral huge peaceful Las anotaciones se guardan y los objetos se consolidan con bases de datos geográficas y anotaciones existentes BBDD geográficas Motor de recomendaciones (geográfico + tags + ontologías) Servidor de anotaciones (todos los usuarios) Servidor de ontologías mezcla El usuario quiere saber qué puntos de interés le pueden interesar en la zona en la que se encuentra Motor de recomendaciones (sólo geográfico) Camino Personalizado

56 Catalogue Integration in the Geographical domain Monolingual Knowledge bases of IGN (spanish): NC (Nomenclátor Conciso), NGN (Nomenclátor Geográfico Nacional), BCN200 (Base Cartográfica Nacional escala 1: ), BCN25 (Base Cartográfica Nacional escala 1:25.000) Monolingual Knowledge bases of CC.AA. (spanish, basque, galician): Castilla y León, Cataluña, Euskadi, Extremadura, Galicia, La Rioja, Madrid, Murcia, Navarra. Creation of an ontology from IGN resources and creation of mappings with IGN knowledge bases

57 Geobuddies Networks of Ontologies Generation of the Phenomen ontology from IGN catalogues using linguistic analysis Art ontologies, Building ontologies and artistic styles built from standardized resources Community building ontologies built from Web resources Instances are distributed and kept in their original sources Alignments between ontologies and resources are first class citizens Organization Ontology Art Personalization Ontology Buildings Ontology Artistic Styles Community Services Geographical Ont. Core

58 NC NGN BCN200 BCN25 Query: ¿Edif. Religioso de Soria? Response: Catedral Soria Ig. Sto. Tomás Catedral Soria Ermita N.S. Nieves Catedral Soria Soria Cated. Ig. Sto. Cated. Soria Cated. NS Nieves Edif. Religioso Construcción Rel. Catedral Ermita IGN Catalogue Integration: Exploitation of Mappings

59 Users annotate with their own tags -The system provides hints about commonly used tags on a predictive style (like SMSs) -Tag clouds can be generated out of this, based on geographical information, services or in general Tags are indexed according to ontologies Predictive tags are enriched with ontologies Users request information using their own tags -The system provides hints about commonly used tags on a predictive style (like SMSs) -Collaborative filtering techniques can be used to recommend the most closely-related tags -Requests can be extended with ontology-based annotations When folksonomies meet ontologies

60 Overview Coming to terms: The Web (1.0 and 2.0), the Semantic Web, the Web of Linked Data and all its applications The Web (1.0 and 2.0) Web applications The Semantic Web (pre-SemanticWeb, SW1.0 and SW3.0) Semantic Web Applications Or [Semantic | Web]+ Applications The Web of Linked Data Linked Data Applications Semantic-based Applications preSemanticWeb Applications Annotation Semantic Web 1.0 Applications Annotation, Data Integration and Decision Support Systems Semantic Web 3.0 Applications (Collaborative) Annotation and Data Integration Conclusions and Trends

61 Reflections: which are the characteristics of these applications in terms of…? Ontologies Single versus network of ontologies? Are ontologies built from scratch or reusing knowledge- aware resources? Are mappings used for solving conceptual mistmaches? Instances Where are the data/instances? Instances are in the ontology Instances are in independent RDF files or databases Data are kept in the original sources Are instances distributed or centralized? Have instances a very high rate of changes? Heterogeneous provenance of instances Degrees of data quality Permissions

62 Where are the instances? or

63 Reflections: which are the characteristics of these applications in terms of…? Amount of semantic markup Conceptual Heterogeneity (semantic markup based on different ontologies) Interoperability with other semantic resources Open to Web resources Open to Web services Web 2.0 like Mobile devices Geo-spatial information

64 Conclusions We are moving into a new generation of semantic applications Open to web resources Open to semantic resources and Linked Data Open to the physical world and having an impact on it. (I have not talked too much about this: check at where … data integration at large scale and user-generated annotations are some of the main challenges that are being faced and... everything combined with 1.Social communities 2.Mobile devices 3.Ubiquitous computing

65 Semantic Web Applications: Past, Present and Future Oscar Corcho Universidad Politécnica de Madrid Florianópolis, August 31st 2010 Acknowledgements: Asunción Gómez-Pérez, Jesús Barrasa, Angel López Cima, Oscar Muñoz, Jose Angel Ramos Gargantilla, María del Carmen Suárez de Figueroa, Boris Villazón, Mariano Fernández López, Luis Vilches, Carlos Ruíz Moreno Work distributed under the license Creative Commons Attribution- Noncommercial-Share Alike 3.0

66 66Kick off Meeting - Brussels, 13 Feb 2008 SemSorGrid4Env 1.Development of an integrated information space where new sensor networks can be easily discovered and integrated with existing ones and possibly other data sources (e.g., historical databases), 2.Rapid development of flexible and user-centric decision support systems that use data from multiple autonomous independently deployed sensor networks and other applications.

67 LinkedGeoData

68 Data repository Ontology learner Alignment repository Ontology discovery and ranking Ontology evaluator Ontology editor Ontology browser Ontology adaptation operators ONTOLOGY DEVELOPMENT & MANAGEMENT ONTOLOGY CUSTOMIZATION SEMANTIC WEB SERVICES Manual annotation Ontology populator Query answering Ontology merger Instance editor Mediator generator Ontology transformer ONTOLOGY ALIGNMENT ONTOLOGY INSTANCE GENERATION QUERYING AND REASONING DATA MANAGEMENT Key aspects (Apps): Semantic Web Framework ONTOLOGY EVOLUTION Ontology view customization Ontology localization and profiling Alignment manipulation Ontology matcher Semantic query processor Semantic query editor Ontology repository Information directory manager Automatic annotation Metadata registry Alignment editor Ontology translator Data translator Web Service discoverer Web Service selector Web Service composer Web Service choreography engine Web Service process mediator Web service repository Web Service grounding Web Service profiling DATA & METADATA MANAGEMENT Ontology evolution strategy Ontology versioner Ontology evolution manager Ontology evolution visualizer

69 Data repository Ontology learner Ontology evaluator Ontology editor ONTOLOGY DEVELOPMENT & MANAGEMENT QUERYING AND REASONING DATA & METADATA MANAGEMENT Key aspects (Apps): Dimensions of the SWF For each dimension, we have identified: Components Component dependencies Existing implementations Ontology repository Ontology browser Semantic query processor Ontology view customization ONTOLOGY CUSTOMIZATION Ontology Development & Management 14 Ontology browser implementations found Three types of implementations: 9 pure ontology browsers: Brownsauce, BrowseRDF, Disco, Drive RDF Browser, Horus, Longwell, Tabulator, RDF Gravity, Welkin 5 Ontology development tool plugins: Jambalaya, OntoSphere3D, OntoViz, OWLViz, TGVizTab Ontology development tools: Included in the Ontology Editor component

70 Overview Ontologies, Semantic Web, the Web of Data and Corporate Semantics From the Web to the Semantic Web Ontologies: Types and Languages Semantic Web, the Web of Data and Corporate Semantics Semantic-based Applications Annotation: Corporate Semantic Webs Data Integration Decision Support Systems Conclusions and Trends Including the real world Combining Semantic Web and Web2.0

71 Hard Work using the Syntactic Web… Find images of Oscar Corcho …Rafael Corchuelo … … José Luis Arjona …

72 What’s the Problem? Typical web page markup consists of: Rendering information (e.g., font size and colour) Hyper-links to related content Semantic content is accessible to humans but not (easily) to computers…

73 Information we can see… Universidad Rey Juan Carlos Organización Universitaria Alumnos Investigación RR.II. Deporte Eventos y actividades… (en la universidad/fuera…?) Noticias Tipos de personas a los que va dirigido Alumnos Profesores Personal de Administración y Servicios …

74                       … Information a machine can see…

75 Solution: XML markup with “meaningful” tags?                       

76 But What About…?                       

77 Still the Machine only sees…                         

78 Overview Ontologies, Semantic Web, the Web of Data and Corporate Semantics From the Web to the Semantic Web Ontologies: Types and Languages Semantic Web, the Web of Data and Corporate Semantics Semantic-based Applications Annotation: Corporate Semantic Webs Data Integration Decision Support Systems Conclusions and Trends Including the real world Combining Semantic Web and Web2.0

79 Types of ontologies Catalog/ID Thesauri “narrower term” relation Formal is-a Frames (properties) General Logical constraints Terms/ glossary Informal is-a Formal instance Value Restrs. Disjointness, Inverse, part-Of...  Lassila O, McGuiness D (2001) The Role of Frame-Based Representation on the Semantic Web. Technical Report. Knowledge Systems Laboratory. Stanford University. KSL-01-02

80 A catalog Consoles and Accessories (1150) Amiga (12) Amstrad (28) Atari (22) Commodore (13) Microsoft (31) Xbox (20) Xbox360 (11) Nintendo (338) GameBoy (47) GameBoy Advance (40) GameBoy Color (16) Gamecube (38) GameBoy Micro (2) Nintendo 64 (74) SuperNintendo (51) Nintendo DS (52) Nintendo wii (16) Catalog: finite list of terms. It can provide an unambiguous interpretation of terms. (E.g unambiguously denotes “consoles and accessories“.)

81 A glossary of terms Action - Proceeding taken in a court of law. Synonymous with case, suit lawsuit. Adjudication - A judgment or decree Adversary system - Basic U.S. trial system in which each of the opposing parties has opportunity to state his viewpoints before the court. Plaintiff argues for defendant's guilt (criminal) or liability (civil). Defense argues for defendant's innocence (criminal) or against liability civil) Affidavit - A written or printed declaration or statement under oath Affirm - The assertion of an appellate court that the judgment of the lower court is correct and should stand. Glossary: list of terms and meanings, which are expressed as natural language statements.

82 Thesauri Agricultural economics MT 6.35 Agriculture FR Agroéconomie SP Economía agraria NT1 Agricultural credit NT1 Agricultural development NT2 Subsistence agriculture NT1 Agricultural markets NT1 Agricultural planning NT1 Agricultural policy NT2 Agricultural prices NT2 Food security NT1 Agricultural production NT1 Agricultural statistics NT2 Food statistics NT1 Land economics NT2 Agrarian structure NT3 Land reform NT2 Farm size NT2 Land reclamation NT2 Land tenure NT2 Land value RT Agricultural enterprises RT Agroindustry RT Rural economy Thesaurus: list of terms that specifies what terms are preferred, the relation narrower term, etc. (e.g. “agricultural credit“ is a narrower term [NT] than “agricultural economics“) Agricultural industry USE Agroindustry A searching system uses ”agroindustry” when the query includes ”agrocultural industry”,

83 Informal is-a Term hierarchies: they provide a general notion of generalization and specialization.

84 Formal is-a Strict subclass hierarchies: if A is a subclass of B, then if an object is an instance of A necessarily implies that the object is instance of B. … …

85 Logical constraints Ontologies with general logical constraints: they include constraints, explicit formal definitions, etc. TRAVEL PLACE Has arrival placeHas departure place travel  (= 1 departurePlace.place)  (= 1 arrivalPlace.place)  (  hasTransportMean.string)  Gómez-Pérez A, Fernández-López M, Corcho O (2003) Ontological engineering. Springer-Verlag, London

86 Overview Ontologies, Semantic Web, the Web of Data and Corporate Semantics From the Web to the Semantic Web Ontologies: Types and Languages Semantic Web, the Web of Data and Corporate Semantics Semantic-based Applications Annotation: Corporate Semantic Webs Data Integration Decision Support Systems Conclusions and Trends Including the real world Combining Semantic Web and Web2.0

87 Ontology Languages Work on Semantic Web has concentrated on the definition of a collection or “stack” of languages. Used to support the representation and use of metadata Basic machinery that we can use to represent the extra semantic information needed for the Semantic Web RDF(S) OWL RDFS RDF XML SWRL

88 RDF RDF stands for Resource Description Framework W3C Recommendation: Provides a simple data model based on triples Statements are triples: Can be represented as a graph: Statements describe properties of resources A resource is any object that can be pointed to by a URI The generic set of all names/addresses that are short strings that refer to resources a document, a picture, a paragraph on the Web, a book in the library, a real person, isbn:// Properties themselves are also resources (URIs) Oscar Session28 presents

89 RDF: Linking Statements The subject of one statement can be the object of another Such collections of statements form a directed, labeled graph The object of a triple can also be a “literal” (a string) Oscar InvitedTalk presents Asun preparedBy hasHomePage “Oscar Corcho” hasName preparedBy

90 RDF Syntax RDF has an XML syntax that has a specific meaning: Every Description element describes a resource Every attribute or nested element inside a Description is a property of that Resource We can refer to resources by URIs Oscar Corcho

91 RDFS: RDF Schema RDF Schema is another W3C Recommendation It extends RDF with a schema vocabulary that allows you to define basic vocabulary terms and the relations between those terms Class, type, subClassOf, Property, subPropertyOf, range, domain it gives “extra meaning” to particular RDF predicates and resources this “extra meaning”, or semantics, specifies how a term should be interpreted The combination of RDF and RDF Schema is normally known as RDF(S)

92 RDFS simple example Event Personal_EventLocal_EventRegional_Event Person ProfessorResearcher subClassOf involves xsd:date eventDate

93 RDF(S) Inference Lecturer Academic Person rdfs:subClassOf rdf:subClassOf rdfs:subClassOf rdf:type rdfs:Class rdf:type

94 RDF(S) Inference Oscar Lecturer rdf:type rdfs:Class Academic rdfs:subClassOf rdf:type

95 OWL Basics (on top of RDF and RDFS) Set of constructors for concept expressions Booleans: and/or/not A Session is a TheoreticalSession or a HandsonSession Slides are not the same as Code Quantification: some/all Sessions must have some EducationalMaterial Sessions can only have Presenters that have developed Grid applications or Grid middleware Axioms for expressing constraints Necessary and Sufficient conditions on classes A Session that hasEducationalMaterial Code is a HandsonSession. Disjointness TheoreticalSessions are disjoint with HandsonSessions Property characteristics: transitivity, inverse

96 OWL Ontology Example. BioPAX ontology

97 Reasoning Tasks OWL based on a well understood Description Logic (SHOIN(D n )) Formal properties well understood (complexity, decidability) Known reasoning algorithms Implemented systems (highly optimised) Because of this, we can reason about OWL ontologies Subsumption reasoning Allows us to infer when one class is a subclass of another Can then build concept hierarchies representing the taxonomy. This is classification of classes. Satisfiability reasoning Tells us when a concept is unsatisfiable i.e. when it is impossible to have instances of the class. Allows us to check whether our model is consistent. Instance Retrieval/Instantiation What are the instances of a particular class C? What are the classes that x is an instance of?

98 Reasoning Tasks. Classification

99 CIDEM: Ontology-based access to DBs Attibute Direct Mapping Attibute Mapping with transformation (Regular Expression) Relation Mapping w. Transformation (Regular Expression) Relation Mapping w. Transformation (Keyword search)

100 Ontology-based Access to Documents

101 g ES Lombard ES (It) q ES r ES p ES a ES c ES i ES n ES e ES h ES l ES o ES f ES d ES m ES b ES Requester ES Responding ES ES not involved Job Seeker’s Candidacy Employer Job Vacancy LEGEND Helping Job Seekers on their way EuropeanEmploymentMediatorsMarketplace Local Matching algorithm EURES ES (Int) Local Matching algorithm Private ES (Int) Local Matching algorithm Wallonia ES (Be) Local Matching algorithm Catalonia ES (Es)

102 Ms Centralized network of ontologies 1. Build a reference ontology Federated network of ontologies 1.Build a reference ontology for the domain 2.Build local ontologies 3.Build mappings between the core and local ontologies 4.Build mappings between the local ontologies and the data sources Ms 2. Build mappings between the reference ontology and the data sources

103 The SEEMP network of ontologies Labour Regulatory Ontology Skill Ontology Language Ontology Occupation Ontology Geography Ontology Time Ontology Education Ontology Driving License Ontology Compensation Ontology Economic Activity Ontology Job Offer Ontology Job Seeker Ontology has work condition / is associated with has contract type / is associated with is located in / has salary / is associated with requires education / is associated with has activity sector / is associated with has nationality from / is nation of resides in / is residence of has salary / has contract type / is associated to has work condition / is associated to has location / is associated with has activity sector / is associated with has activity sector / is associated with has job category / is associated with has job category / Is associated with has education / is education of has mother tongue / is mother tongue of speaks / is spoken by has language proficiency / belongs to LE FOREM + BLL + EURES EURES ISO 6392 CEF ISCO-88 COM ONET EURES ISO 3166 EURES DAML Time Ontology FOET ISCED97 NACE Rev. 1.1 European Legislation ISO 4217 Ad hoc wrapper External Sources is associated with has job category / is associated to has date of birth / is date of birth of has begin date / is begin date of Competence Ontology subClass-Of requires competence / is associated with has competence / is competence of

104 Dbpedia Mobile

105 Ontology-based Access to DBs 1.Build a new ontology from 1 DB schema and 1 DB 2.Align the ontology built with approach 1 with a legacy ontology 3.Align an existing DB with a legacy ontology a) Massive dump (semantic data warehouse) b) Query-driven 4.Align an ontology network with n DB schemas and other data sources a) Massive dump (semantic data warehouse) b) Query-driven new ontology existing ontology

106 Ontology-based Access to Databases BDR Modelo Relacional Personal Organización Pregunta: Nombre de los profesores de la universidad UPM * Un profesor es una persona cuyo puesto es “docente” * Una universidad es una organización de tipo “3” Consulta: valores de la columna nombre de los registros de la tabla Personal para los que el valor de la columna puesto is “docente” que estén relacionados con al menos un registro de la tabla Organización con el valor “3” en la columna tipo y “UPM” en la columna nombre. ? Procesado de la consulta de acuerdo a la descripción formal de correspondencia Ontología Profesor Doctorando Universidad Procesador

107 Align existing data sources with legacy ontologies Punto Europeo PuntoGPS PuntoAsiatico = Aeropuertos f (Aeropuertos) Ontología O 2 Modelo Relacional M 1 PuntoEuropeo PuntoEspañol Estación Centro Comunicaciones Aeropuerto = f (Aeropuertos) Ontología O 1 RC (O 2,M 1 ) RC (O 1,M 1 )


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