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The Nature of Information

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Presentation at the Commission of the Göttingen Academy of Sciences, University of Göttingen

Presentation at the Commission of the Göttingen Academy of Sciences, University of Göttingen

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  • 1. Pragmatic Web 4.0 Kommission „Die Natur der Information“ Göttinger Akademie, 8.11.2013 Prof. Dr. Adrian Paschke Corporate Semantic Web (AG-CSW) Institut für Informatik, Freie Universität Berlin paschke@inf.fu-berlin http://www.inf.fu-berlin/groups/ag-csw/ and Department of Information Systems Poznan University of Economics paschke@inf.fu-berlin
  • 2. Agenda  What is Semantics?  Declarative Knowledge Representation in IT  The Semantic Web – An Introduction  Semantic Web and it’s Relations  What comes next?
  • 3. What is Semantics? The Problem of Machine Meaning Interpretation and Machine Understanding
  • 4. Data vs. Information  Data  A “given,” or fact; text, a number, a statement, or a picture, …  The raw materials in the production of information  Information  Data that has been put into a meaningful and useful context.
  • 5. Example Data vs. Information data 95 information My score on the final exam is 95% knowledge I have passed the exam with excellent mark bdb data representation, e.g. relational DB data + context + information representation data / information + meaning interpretation
  • 6. Search Results from Publication Database Title  Lorenz P, Transcriptional repression mediated by the KRAB domain of the human Author C2H2 zinc finger protein Kox1/ZNF10 does not require histone deacetylation. Biol Chem. 2001 Apr;382(4):637-44.  Fredericks WJ. An engineered PAX3-KRAB transcriptional repressor inhibits the malignant Journal Year phenotype of alveolar rhabdomyosarcoma cells harboring the endogenous PAX3-FKHR oncogene. However, for a machine things look different! Mol Cell Biol. 2000 Jul;20(14):5019-31.
  • 7. Results from Publication Database  Lorenz P, Transcriptional repression mediated by the KRAB domain of the human C2H2 zinc finger protein Kox1/ZNF10 does not require histone deacetylation. Biol Chem. 2001 Apr;382(4):637-44.  Fredericks WJ. An engineered PAX3KRAB transcriptional repressor inhibits the malignant phenotype of alveolar rhabdomyosarcoma cells harboring the endogenous PAX3-FKHR oncogene. Mol Cell Biol. 2000 Solution: Tags (XML)? Jul;20(14):5019-31.
  • 8. Results from Publication Database  <author>Lorenz P</author><title>Transcriptional repression mediated by the KRAB domain of the human C2H2 zinc finger protein Kox1/ZNF10 does not require histone deacetylation. </title> <journal>Biol Chem </journal><year>2001<year>  <author>Lorenz P</author><title>Transcriptional repression mediated by the KRAB domain of the human C2H2 zinc finger protein Kox1/ZNF10 does not require histone deacetylation. </title> <journal>Biol Chem </journal><year>2001<year> However, for a machine things look different!  ...
  • 9. Results from Publication Database  <author>Lorenz P</author><title>Transcriptional repression mediated by the KRAB domain of the human C2H2 zinc finger protein Kox1/ZNF10 does not require histone deacetylation. </title> <journal>Biol Chem </journal><year>2001<year>  <author>Lorenz P</author><title>Transcriptional repression mediated by the KRAB domain of the human C2H2 zinc finger protein Kox1/ZNF10 does not require histone deacetylation. </title> <journal>Biol Chem </journal><year>2001<year> Solution: Use Semantic Knowledge Representation
  • 10. Example: Traffic Light Syntax – Semantics - Pragmatics  Syntax  green (bottom); yellow; red  Semantics  green = go; …; red = stop  Pragmatics  If red and no traffic then allowed to go
  • 11. Example: Question-Answer Interaction Syntax – Semantics - Pragmatics  Syntax  “What time is it?” (English)  Semantics  Question about current time (Meaning)  Pragmatics  An answer to the question is obligatory (even if time is unknown) (Understanding and Commitment)
  • 12. Example - XML Syntax vs. Semantics Adrian Paschke is a lecturer of Logic Programming <course name=“Logic Programming"> <lecturer>Adrian Paschke</lecturer> </course> <lecturer name=“Adrian Paschke"> <teaches>Logic Programming</teaches> </lecturer> Opposite nesting (syntax), same meaning (semantics)!
  • 13. Syntax – Semantics - Pragmatics  Syntax  about form  Semantics  about meaning  Pragmatics  about use.
  • 14. Information, Knowledge, Wisdom Connectedness Intelligence / Wisdom understanding principles Pragmatics Knowledge Understanding patterns Sematics Information / Content Understanding relations Data Syntax Understanding
  • 15. Declarative Knowledge Representation in IT
  • 16. Semantic Technologies for Declarative Knowledge Representation 1. Rules  Describe derived conclusions if premium(Customer) and reactions from given then discount(10%) information (inference) 2. Ontologies equal with Customer  Ontologies represent the conceptual knowledge of a domain (concept semantics) is a Partner Client
  • 17. What is an Ontology? (in IT) An Ontology is a formal specification  Executable, Discussable of a shared  Group of persons conceptualization  About concepts; abstract class of a domain of interest  e.g. an application, a specific area, the “world model” [Gruber 1993] - T.R. Gruber, Toward Principles for the Design of Ontologies Used for Knowledge Sharing, Formal Analysis in Conceptual Analysis and Knowledge Representation, Kluwer, 1993.
  • 18. What is a Rule? (in IT) 1. Rules • • Derivation rules (deduction rules): establish / derive new information Reaction rules that establish when certain actions or effects should take place : • Condition-Action rules (production rules) • Event-Condition-Action (ECA) rules + variants (e.g. ECAP). • Messaging Reaction Rules (event message reaction rules) 2. Constraints • • • Structural constraints (e.g. deontic assignments). Integrity constraints and state constraints Process and flow constraints [Paschke, A., Boley, H.]: Rule Markup Languages and Semantic Web Rule Languages, in Handbook of Research on Emerging Rule-Based Languages and Technologies: Open Solutions and Approaches, IGI Publishing, ISBN:1-60566-402-2, 2009
  • 19. Example: Ontology and Rules Ontology Object is_a-1 Person is_a-1 is_a-1 knows has Topic described_in Prior Art Document related_to related_to is_a-1 Patent Application Priority Skill Patentee Technique described_in Teaching writes is_a-1 Patent date becomes granted RULES: Topic Patentee writes described_in Patent Application Document is_about Topic Topic Patentee Patentee is_about knows has Document Topic Skill
  • 20. Ontologies and their relatives informal formal semantics expressiveness Based on AAAI’99 Ontologies Panel – McGuiness, Welty, Ushold, Gruninger, Lehmann
  • 21. Many Ontology Languages           No special ontolgy languages, Entity Relationship Modell but might be used to describe ontologies UML with OCL Frames, F-Logic Predicate Logic Common Logic formal logic Description Logic specialized SHOE, XOL, OML, SKOS, OBO, SBVR, … Web Ontology Languages RDFS, DAML+OIL -> OWL ODM Ontology Transformation …
  • 22. Logic and Knowledge Representation in IT  Declarative Knowledge Representation express what is valid, the responsibility to interpret this and to decide on how to do it is delegated to an automated interpreter / reasoner  Formal logic-based languages for the representation of knowledge with a clear semantics
  • 23. Main Requirements of a Logic-based Ontology / Rule Language in IT  a well-defined syntax  a formal semantics  sufficient expressive power  efficient reasoning support  convenience/adequacy of expression syntax
  • 24. Logic-based Knowledge Representation  First Order Logic  Expressive syntax: constants, functions, predicates, equality, quantifiers, variables  Objects and relations are semantic primitives represented as predicate formula  But: reasoning not efficient and undecidable  Solution: Restriction to Subsets of FOL  Horn Logic (Logic Programming / Rules)  Descripition Logics (Ontologies)  But: convenience of expression: only formal syntax + semantics, but not a Web representation format => Semantic Web Syntax and Semantic Web Data Model needed
  • 25. The Semantic Web An Introduction and Overview
  • 26. Semantic Web – An Introduction  "The Semantic Web is an extension of the current web in which information is given welldefined meaning, better enabling computers and people to work in cooperation."  Tim Berners-Lee, James Hendler, Ora Lassila, The Semantic Web  „Make the Web understandable for machines“ W3C Stack 2007
  • 27. Main Building Blocks of the Semantic Web 1. 2. 3. 4. Explicit Metadata on the WWW Ontologies Rule Logic and Inference Semantic Tools ,Semantic Web Services, Software Agents
  • 28. The (current) W3C Semantic Web Stack Ontologies RDF Query Language Rules Semantic Web Information Model Standard Internet Technologies W3C Semantic Web Stack since 2007
  • 29. Overview on the Semantic Web Technologies  URI/IRI: Web Resource Identifiers  Note: Representational State Transfer (REST) – Resources are abstraction from an information/knowledge (e.g. „weather in Göttingen“)  XML: Syntactic basis  RDF: Resource Description Framework  RDF as Web data model for facts and metadata  RDF schema (RDFS) as simple ontology language (mainly taxonomies)  SPARQL as a RDF query language  Linked Data – data publishing method
  • 30. Overview on the Semantic Web Technologies (2)  Ontology  RDF Schema (RDFS) and Web Ontology Language (OWL)  Rules / Logic  Rule Interchange Format (RIF, RuleML)  Proof  Generation of proofs-, interchange of proofs, validation  Trust  Digital signatures  recommendations, ratings  Semantic Web Applications & Interfaces  e.g. Semantic Search, Semantic Agents, …
  • 31. W3C Semantic Web (state: 2013)       IRIs + CURIE (Compact URI) RDF 1.1, HTML+RDFa 1.1, RDB2RDF SPARQL 1.1 RIF 1.0 (second edition) OWL 2.0 (second edition) Linked Open Data  RDF 1.1, Turtel, JSON-LD 1.1, …  Provenance  Prov-DM, Prov-N, Prov-O, …
  • 32. The (current) W3C Semantic Web Architecture W3C Semantic Web Stack since 2007
  • 33. Example: RDF diagram http://www.inf.fu-berlin.de/~adrianp/index.htm dc:Creator Adrian Paschke Subject (= Ressource): http://www.inf.fu-berlin.de/~adrianp/index.htm Predicate (= Property Attribute): dc:Creator Object (= Value): Adrian Paschke resource-property-value triple = RDF triple = RDF statement Read: <Ressource> has <Property> <Value>
  • 34. Extended RDF Diagram http://www.inf.fu-berlin.de/~adrianp/index.htm c:Creator http://www.inf.fu-berlin/Id/123 c:Name Adrian Paschke c:Email adrian.paschke@inf.fu-berlin.de
  • 35. RDF/XML-Version <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:s="http://description.org/schema/"> <rdf:Description about=" http://www.inf.fu-berlin.de/~adrianp/ "> <s:Creator rdf:resource="http:// www.inf.fu-berlin.de/Id/123 "/> </rdf:Description> <rdf:Description about=" http:// www.inf.fu-berlin.de/Id/123 "> <s:Name>Adrian Paschke</s:Name> <s:Email>adrian.paschke@inf.fu-berlin.de</s:Email> <rdf:Description> </rdf:RDF>
  • 36. RDF for Metadata Vocabulary Example: Dublin Core in RDF <?xml version="1.0"?> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dublin_core/schema/"> <rdf:Description rdf:about="responder.ruleml.org"> <dc:creator>A. Paschke</dc:creator> <dc:title>Rule Responder</dc:title> </rdf:Description> </rdf:RDF>
  • 37. Example: FOAF 0.1 – Metadata Vocabulary (in RDF)
  • 38. RDFa – RDF in HTML
  • 39. Linked Open Data Cloud
  • 40. Metadata Problem Domains  Syntax:  Which representation and interchange format for metadata? (Microformats, RDF, RDFa, Microdata)  Semantics:  Which metadata are allowed for Web resources (expressiveness, metadata vocabulary, schema)  Association problem:  How to connect metadata with resources? (who defines the metadata, are metadata separated from the content (RDF vs. RDFa), etc.)
  • 41. The W3C Semantic Web Architecture W3C Semantic Web Stack since 2007
  • 42. RDF Triple Stores  A specialized database for RDF triples  Supports a query language  SPARQL is the W3C recommendation  Triple stores might be in memory or provide a persistent backend  Presistence provided by an underlying relational DBMS (e.g., mySQL) or a custom DB for efficiency.
  • 43. Example: SPARQL SELECT  SELECT: SELECT Variables FROM Dataset WHERE Pattern  Examples: PREFIX foaf: <http://xmlns.com/foaf/0.1/> SELECT ?name WHERE ( ?x foaf:name ?name ) PREFIX foaf: <http://xmlns.com/foaf/0.1/> SELECT * WHERE ( ?x foaf:name ?name )
  • 44. The (current) W3C Semantic Web Architecture W3C Semantic Web Stack since 2007
  • 45. Example: RDFS Ontology range range Literal id phone domain involves domain domain Course RDFS range subPropertyOf isTaughtBy Staff Member domain subClassOf Academic Staff Member range subClassOf subClassOf subClassOf Full Professor Associate Professor Assistant Professor rdf:type rdf:type RDF isTaughtBy Semantic Web Adrian Paschke
  • 46. RDF Schema Example <rdf:RDF xml:lang=„en" 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="Professor"> <rdfs:comment>The class of full professors</rdfs:comment> <rdfs:subClassOf rdf:resource=http://www.w3.org/2000/03/example/classes#AcademicStaffMember/> </rdfs:Class> <rdf:Property ID=„id"> <rdfs:range rdf:resource="http://www.w3.org/2000/03/example/classes#Integer" /> <rdfs:domain rdf:resource="#StaffMember" /> </rdf:Property> <rdf:Property ID=„phone"> <rdfs:range rdf:resource="http://www.w3.org/2000/03/example/classes#Integer" /> <rdfs:domain rdf:resource = "#StaffMember" /> </rdf:Property> … </rfd:RDF>
  • 47. OWL vs. RDFS  More Expressiveness  Definition of relations between classes  Definition of constraints and cardinalities  Constraints on properties: exists, forall, cardinality  Definition of equivalences between classes (e.g. different ontologies)  Properties of properties  Boolean combinations of classes and constraints  …
  • 48. Example: OWL Ontology peopleAtUni range id Student involves domain domain Course OWL Staff Member range domain subClassOf equivalentClass subPropertyOf isTaughtBy phone domain unionOf T-Box Model range Literal 1 Faculty Academic Staff Member subClassOf range subClassOf subClassOf disjointWith Professor Assistant Professor Associate Professor rdf:type rdf:type RDF isTaughtBy Semantic Web Adrian Paschke A-Box Model
  • 49. Reasoning with OWL  Semantics of OWL is defined by Description Logics (DL)  Satisfiability: whether the assertions in an TBox and ABox has a model (i.e. non-contradicting)  Subsumption: whether one description is more general than another one  Equivalence: whether two classes denote same set  Consistence: whether its set of assertions is consistent  Instantiation: check if an individual is an instance of class C  Retrieval: retrieve a set of individuals that instantiate C
  • 50. The (current) W3C Semantic Web Architecture W3C Semantic Web Stack since 2007
  • 51. Usage: Rule Interchange Rules Rules serialize de-serial. Data model (OWL, RDF-S, XML-S, XMI, …) Rule system 1 Data <RuleML doc> serialize Application A <XML doc> data Rules Rule system 2 de-serial. Data Application B
  • 52. Example: Rule Markup Language Standards (RuleML)  RuleML 1.0 (Deliberation, Reaction, Defeasible, Modal, …)  Semantic Web Rule Language (SWRL)  Uses RuleML Version 0.89  Semantic Web Services Language (SWSL)  Uses RuleML Version 0.89  W3C Rule Interchange Format (RIF)  Uses RuleML Version 0.91 with frames and slots  OASIS LegalRuleML  Uses RuleML Version 1.0  OMG Production Rules Representation (PRR)  Input from RuleML  OMG Application Programming Interfaces four KBs (API4KB)  Input from Reaction RuleML 1.0
  • 53. Unifying Logic • Not standardized in W3C Semantic Web Stack yet • Which semantics? (e.g., Description Logics, F-Logic, Horn Logic, …) • Which assumptions? (e.g., Closed World, Open World, Unique Name) • … W3C Semantic Web Stack since 2007
  • 54. Example Decision Scenario  You need to wait if the traffic light is not green.  I know that the traffic light is green, so I’m allowed to cross the street  I’m not sure if the traffic light is green, so I’m allowed to cross the street ????
  • 55. Open World vs. Closed Word Assumption  Open World Assumption (typical for ontologies)  explicitly prove the truth of negation  Closed World Assumption (typical for rules / logic programs)  if we do not know (from our closed knowledge base) we assume falsity  This difference has practical implications  Traffic light example:  Under open world assumption we need to explicitly prove that the light is not red => cross street  Under closed world assumption it is enough if we prove that there is no information if the light is red => cross street  Who is responsible if an accident happens?
  • 56. Unique-Names Assumption isTaughtBy domain Course  range 1 Academic Staff Member A course is taught by at most one staff member. The course „Ontologies in IT“ is taught by „Prof. Paschke“ and „Prof. Wecel“ OWL does not adopt the unique-names assumption of database systems/logic programs (rules)  If two instances have a different name or keys/IDs does not imply that they are different individuals  An OWL reasoner does not flag an error  Instead it infers that the two resources are equal  A rule reasoner / deductive database does flag an error
  • 57. Proof and Trust • Proof Markup Languages, Justifications and Argumentations • Claims can be verified, if there are evidences from other (trusted) Internet sources • Semantic Reputation Models
  • 58. Example Scenario – eCommerce E-Shop Review Relying Party Reseller Bob Delivery Service Buyer Monitored Delivery Performance Business Owner/Seller/Factory used for service management used for buying decisions Semantic Reputation Object Semantic Web Reputation and Trust Management http://www.corporate-semantic-web.de/rule-responder.html Other Buyers
  • 59. Use Cases / Applications / Tools            Semantic-enriched Search Content management Knowledge management Business intelligence Collaborative user interfaces Sensor-based services Linking virtual communities Grid infrastructure Multimedia data management Semantic Web Services Etc. see e.g.SWEO’s use case collection http://www.w3.org/2001/sw/sweo/public/UseCases/
  • 60. Semantic Search Engine Gene Ontology
  • 61. Example: Semantic MediaWiki
  • 62. Example: What is located in California?
  • 63. Example: Semantic Desktop Systems  Combine desktop systems with Semantic Web Technologies  Extract, manage, visualize and use semantic and contextual associations respectively metadata for Personal Information Management (PIM)  e.g. Gnowsis, Nepomuk, Beagle++, Social Semantic Desktop, Haystack
  • 64. Example: Job Portal Semantic Recommendation d (Java, C++) = d (Java, Object Oriented) + d (C++, Object Oriented) = (0.25-0.0.0625) + (0.25-0.0625) = 0.375 sim(Java, C++) = 1 – 0.375 = 0.625 (Semantic Similarity is 0,625) Example: Query „Job offers for Java Programmer“ + expanded with Personal Skill Profile (Java + C++ Knowledge) => also recommend job offers for C++ programmer (see Semantic Matchmaking Framework: http://www.corporate-semantic-web.de/technologies.html
  • 65. Finding Experts in Wikis
  • 66. Example: Museum DBPedia Deutschland Semantic Wikipedia Germany www.de.dbpedia.org Semantic Annotation and Semantic Content Enrichment
  • 67. The Semantic Web and it‘s relations
  • 68. Other Semantic Standards/Specifications Metadata Terminology Modeling ISO/IEC 11179 Metadata Registries CONCEPT Terminology Thesaurus Taxonomy Ontology Data Standards Logic Graph RDF(S) / OWL Metadata Registry Structured Metadata Semantic Web Refers To Referent Symbolizes “Rose”, Stands For “ClipArt Rose” MOF ODM PRR SBVR API4KB OntoIOP Node Subject Edge Predicate ISO TC 37 OMG F-Logic RuleML Common Logic Node Object SPARQL,RIF ISO/IEC JTC 1/SC 32 FOL W3C Prolog ISO, RuleML,…
  • 69. Example: OMG Ontology Definition Metamodel (ODM) Ontology Definition Metamodel MOF MOF XMI Of UML MOF XMI Of ODM UML ODM User UML Model UML XMI Of User Model User Ontology Ontology XMI Of User Model ISO Topic Maps M2 M1 User Instances UML 2 (+OCL) M3 M0 ISO CL W3C RDFS W3C OWL  ODM brings together the communities (SE+KR) by providing:  Broad interoperation within Model Driven Architecture  MDA tool access to ontology based reasoning capability  UML notation for ontologies and ontological interpretation of UML
  • 70. Example: Rule Markup Language Standards (RuleML)  RuleML 1.0 (Deliberation, Reaction, Defeasible, Modal, …)  Semantic Web Rule Language (SWRL)  Uses RuleML Version 0.89  Semantic Web Services Language (SWSL)  Uses RuleML Version 0.89  W3C Rule Interchange Format (RIF)  Uses RuleML Version 0.91 with frames and slots  OASIS LegalRuleML  Uses RuleML Version 1.0  OMG Production Rules Representation (PRR)  Input from RuleML  OMG Application Programming Interfaces four KBs (API4KB)  Input from Reaction RuleML 1.0
  • 71. Social Semantic Web The concept of the Social Semantic Web subsumes developments in which social interactions on the Web lead to the creation of explicit and semantically rich knowledge representations. (Wikipedia)
  • 72. Corporate Semantic Web Corporate Semantic Web (CSW) address the applications of Semantic Web technologies and Knowledge Management methodologies in corporate environments (semantic enterprises). (www.corporate-semantic-web.de)
  • 73. Corporate Semantic Web Public Semantic Web Corporate Semantic Web Business Context Corporate Semantic Engineering Corporate Semantic Search Corporate Semantic Collaboration Corporate Business Information Systems
  • 74. Pragmatic Web  The Pragmatic Web consists of the tools, practices and theories describing why and how people use information. In contrast to the Syntactic Web and Semantic Web the Pragmatic Web is not only about form or meaning of information, but about interaction which brings about e.g. understanding or commitments. (www.pragmaticweb.info)
  • 75. Pragmatic Agent Web The Pragmatic Agent Web utilize the Semantic Web with multiple interacting intelligent agents which collaborate on the Web and put independent meta data, ontologies and local data into a pragmatic context such as communicative situations, organizational norms, purposes or individual goals and values. Duration & Connectedness Intelligence Knowledge Pragmatic Semantic Information Syntax Data (Machine) Understanding
  • 76. Pragmatic Agent Web (2)  Utilize the heterogenous Semantic Web resources, meta data and meaning representations with intelligent agents and web-based services with the ability to understand the others intended meaning (pragmatic competence)  Formal Logic Representation vs. (Controlled) Natural Language Representation  Collaborate in a communicative conversation-based process where content and context is interchanged in terms of messages (relation of signs) between senders and receivers (interpreters/users).  Loosley-coupled vs. de-coupled interactions  Fixed negotiation and coordination protocols vs. free conversations  Pragmatic layer/wrapper around semantic/content e.g. by KQML / ACL like speech-act primitives (e.g. assert(content), retract(content), query(kb))  Model, negotiate and control shared and invividual meanings  requires learning and knowledge adaption / updates
  • 77. What comes next?
  • 78. Challenges for the Semantic Web Connectedness Intelligence / Wisdom understanding principles Pragmatics Knowledge Understanding patterns Sematics Information / Content Understanding relations Data Ontologies (Logic) Rules (Logic) Syntax ??? (Human Logic + Machine Logic) Understanding
  • 79. Ubiquitous Pragmatic Web 4.0 Pragmatic Agent Ecosystems Machine Understanding Situation Aware Real-time Semantic Complex Event Processing Ubiquitous Pragmatic Web 4.0 Pragmatic Web Connects Intelligent Agents and Smart Things Massive Multi-player Web Gaming Ubiquitous autonomic Smart Services and Things Smart Web TV Social Semantic Web 3.0, Web of Services & Things, Corporate Semantic Web Connects Semantic Web Smart Content People, Services and Things Semantic Web 2.0 Connects Knowledge Syntactic Web World Wide Web 1.0 Smart Content Passive Active Desktop Computing Syntactic Web Semantic Web Consumer Smart Agents XML RDF Monolithic Systems Era HTML Desktop Content Producer Connects Information Pragmatic Web Ubiquitous Next Generation Agents and Social Connections
  • 80. Thank you … Questions? http://www.corporate-semantic-web.de http://www.pragmaticweb.info AG Corporate Semantic Web, FU Berlin paschke@inf.fu-berlin http://www.inf.fu-berlin/groups/ag-csw/