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Semantic Web from the 2013 Perspective
 

Semantic Web from the 2013 Perspective

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Lecture at 2nd MakoLab Semantic Day – Theory and Praxis at the Polish Academy of Sciences in Paris, October 3rd, 2013

Lecture at 2nd MakoLab Semantic Day – Theory and Praxis at the Polish Academy of Sciences in Paris, October 3rd, 2013

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    Semantic Web from the 2013 Perspective Semantic Web from the 2013 Perspective Presentation Transcript

    • Semantic Web from the 2013 Perspective 2nd MakoLab Semantic Day – Theoria and Praxis Polish Academy of Sciences, October 3rd, 2013 Prof. Dr. Adrian Paschke Department of Information Systems Poznan University of Economics and Freie Universitaet Berlin paschke@inf.fu-berlin Prof. Dr. Witold Abramowicz Department of Information Systems Poznan University of Economics http://kie.ue.poznan.pl/en
    • Scientific Center of the Polish Academy of Sciences in Paris
    • Poznan University of Economics  specialises in educating economists, managers and specialists in quality management in all sectors of the economy  Research labs  Enterprise platforms and systems  Service science  Next Generation Internet Semantic as a leitmotif
    • Semantic related EU projects  SUPER – Semantics Utilised for Process Management within and between Enterprises  ASG – Adaptive Services Grid  INSEMTIVES – Incentives for Semantics  EASTWEB: building an integrated leading Euro- Asian higher education and research community in the field of the Semantic  USE-ME.GOV - Usability driven open platform for mobile government  T-OWL – Time-determined Ontology based knowledge system for real time stock market analysis  Service Web 3.0  ENIRAF - Enhanced Information Retrieval and Filtering for Analytical Systems  KnowledgeWeb
    • Other semantic related projects  eDW – enhanced Data Warehouse  eVEREst – The System to Support Government’s Estimation of Real Estates’ Value  F-WebS – Filtering of Web services – semantic description of Web services  Adaptive microWorkflow – Acquisition and Filtering of Information for the Needs of Adaptive microWorkflows  EGO – Identity management  Semiramida – ontological representation of legal acts  Integror-S3 – Semantically- Enhanced Execution Engine  eXtraSpec – Advanced data extraction methods for the needs of expert search  ASBK – Adaptive Systems for Corporate Banking  FEMS – Future Energy Management System  DWDI – Deep Web Data Integration
    • Agenda  What is Semantics?  The Semantic Web – An Introduction  Semantic Web and it’s Relations  What comes next?
    • What is Semantics?
    • Search 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 PAX3-KRAB transcriptional repressor inhibits the malignant phenotype of alveolar rhabdomyosarcoma cells harboring the endogenous PAX3-FKHR oncogene. Mol Cell Biol. 2000 Jul;20(14):5019-31. Author Title YearJournal However, for a machine things look different!
    • 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 PAX3- KRAB transcriptional repressor inhibits the malignant phenotype of alveolar rhabdomyosarcoma cells harboring the endogenous PAX3-FKHR oncogene. Mol Cell Biol. 2000 Jul;20(14):5019-31. Solution: Tags (XML)?
    • 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!
    • 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
    • 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
    • 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)
    • 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)!
    • Syntax about form Semantics about meaning Pragmatics about use. Syntax – Semantics - Pragmatics
    • Semantic Technologies for Declarative Knowledge Representation 1. Rules  Describe derived conclusions and reactions from given information (inference) 2. Ontologies  Ontologies described the conceptual knowledge of a domain (concept semantics) Partner Customer is a equal with Client if premium(Customer) then discount(10%)
    • Example: Ontology and Rules Object Person DocumentTopic Patentee Patent Application Patent becomes knows described_in is_a-1 is_a-1 is_a-1 is_a-1 is_a-1 writes related_to Skill has related_to Topic Document Topic Document Patent Application Topic Patentee Topic described_in is_about knows is_about Patentee writes RULES: Patentee Skill has granted Technique Teaching described_in Priority date Prior Art Ontology
    • Main Requirements of a Logic-based Ontology / Rule Language in IT a well-defined syntax a formal semantics efficient reasoning support sufficient expressive power convenience/adequacy of expression syntax
    • The Semantic Web An Introduction
    • Semantic Web – An Introduction  "The Semantic Web is an extension of the current web in which information is given well- defined 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
    • Main Building Blocks of the Semantic Web 1. Explicit Metadata on the WWW 2. Ontologies 3. Rule Logic and Inference 4. Semantic Tools ,Semantic Web Services, Software Agents
    • The (current) W3C Semantic Web Stack W3C Semantic Web Stack since 2007 Ontologies Rules Semantic Web Information Model RDF Query Language Standard Internet Technologies
    • Overview on the Semantic Web Technologies  URI/IRI: Web Resource Identifiers  RDF  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  Ontology  Expressive ontology languages  Web Ontology Language (OWL)
    • Overview on the Semantic Web Technologies (2)  Rules / Logic  Extension of the ontology languages, e.g. with rules  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, …
    • 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, …
    • Linked Open Data Cloud
    • Unifying Logic W3C Semantic Web Stack since 2007 • Not standardized in W3C Semantic Web stack yet • Which semantics? (e.g., Description Logics, F-Logic, Horn Logic, Common Logic,…) • Which assumptions? (e.g., Closed World, Open World, Unique Name, …) • …
    • Proof and Trust • Proof Markup Languages, Justifications and Argumentations, Provenance Proofs • Claims can be verified, if there are evidences from other (trusted) Internet sources • Semantic Reputation Models • …
    • Use Cases / Applications / Tools  Application Programming Interfaces  Semantic-enriched Search  Content management  Knowledge management  Business intelligence  Collaborative user interfaces  Sensor-based services  Linking virtual communities  Grid infrastructure  Semantic Multimedia data management  Semantic Web Services  etc. see e.g.SWEO’s use case collection http://www.w3.org/2001/sw/sweo/public/UseCases/ More about applications and use cases this afternoon…
    • The Semantic Web and it‘s relations
    • Other Semantic Standards/Specifications ISO/IEC JTC 1/SC 32 ISO/IEC 11179 Metadata Registries Metadata Registry Terminology Thesaurus Taxonomy Data Standards Ontology Structured Metadata Terminology CONCEPT Referent Refers To Symbolizes Stands For “Rose”, “ClipArt Rose” ISO TC 37 Semantic Web W3C Modeling MOF ODM PRR SBVR API4KB OntoIOP OMG Node Node Edge Subject Predicate Object Graph RDF(S) / OWL SPARQL,RIF Logic Common Logic Prolog ISO, RuleML,… FOL RuleML F-Logic Metadata
    • Ontology Definition Metamodel  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 M2 M1 M3 MOF XMI Of UML UML XMI Of User Model MOF UML M0User Instances User Ontology User UML Model MOF XMI Of ODM ODM Ontology XMI Of User Model ISO Topic Maps ISO CL W3C RDFS W3C OWL UML 2 (+OCL) Example: OMG Ontology Definition Metamodel (ODM)
    • 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
    • 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)
    • 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)
    • Corporate Semantic Web Corporate Semantic Web Corporate Semantic Engineering Corporate Semantic Search Corporate Semantic Collaboration Public Semantic Web Corporate Business Information Systems Business Context
    • 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)
    • What comes next?
    • Challenges for the Semantic Web Syntax Sematics Pragmatics Data Understanding Connectedness Information / Content Knowledge Intelligence / Wisdom Understanding relations Understanding patterns understanding principles Ontologies (Logic) Rules (Logic) ??? (Human Logic + Machine Logic)
    • Pragmatic Web Ubiquitous Open Web Platform for the Pragmatic Web 4.0 Monolithic Systems Era Desktop Computing Desktop World Wide Web 1.0 Connects Information Syntactic Web Semantic Web 2.0 Connects Knowledge Social Semantic Web 3.0, Web of Services & Things, Corporate Semantic Web Connects People, Services and Things Ubiquitous Pragmatic Web 4.0 Connects Intelligent Agents and Smart Things Semantic Web Ubiquitous autonomic Smart Services and Things Pragmatic Agent Ecosystems Machine Understanding Ubiquitous Next Generation Agents and Social Connections Syntactic Web Semantic Web Pragmatic Web HTML XML RDF Smart Agents Content Producer Passive Active Consumer Smart Content Smart Content Smart Web TV Massive Multi-player Web Gaming Situation Aware Real-time Semantic Complex Event Processing W3C Open Web Platform
    • Thank you … Questions? AG Corporate Semantic Web, FU Berlin paschke@inf.fu-berlin http://www.inf.fu-berlin/groups/ag-csw/ http://www.corporate-semantic-web.de http://www.pragmaticweb.info