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Georg Güntner | Salzburg Research; Herbert Beilschmidt | Oracle Austria GmbH

The Open Semantic Enterprise - Enterprise Data meets Web Data
                               2nd B2B Software Days (TechGate Vienna, 11.04.2013)
Abstract

     The Open Semantic Enterprise.
      Enterprise Data meets Web Data.
      The technologies of the “Web od Data” have reached a degree of maturity and acceptance allowing the
      productive use in enterprises for the support of their business processes. Though the focus is currently on
      the adoption and use of Open (Linked) Data, the underlying principles can also be applied to the closed
      data sources and proprietary data structures usually available in enterprises.
      The workshop outlines the conceptual and architectural approaches to open enterprise data sources and
      interweave them with the Web of Data. It shows concrete application scenarios of an open source
      “semantic toolset” that can be integrated with enterprise information and content management systems to
      open data silos, establish a layer of adaptive integrated views of the enterprise information and support
      decision processes thus paving the way to an “open semantic enterprise”.
      The topical semantic toolset for enterprise content integration includes Apache Stanbol (knowledge
      extraction), Apache Marmotta (Linked Data Platform), the Linked Media Framework (networked
      knowledge) und VIE (interactive knowledge).
      State-of-the-art big data platforms need to process massive quantities of data in batch and in parallel -
      filtering, transforming and sorting it before loading it into an enterprise data warehouse. In order to realize
      an Open Semantic Enterprise, a big data platform has to be optimized for acquiring, organizing, and
      loading unstructured data. Technological approaches such as NoSQL databases and connectors for
      Apache Hadoop complement big data solutions for the open world of a semantic enterprise.

     Georg Güntner, Salzburg Research



©              11.04.2013         "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)          2
Salzburg Research

     Salzburg Research was founded in 1996 as the research organisation of the
      Province of Salzburg (www.salzburgresearch.at)
     Salzburg Research is located at Techno-Z Salzburg and conducts applied research
      and development in the area of information and communication technologies (ICT)
     Salzburg Research employs about 70 researchers and has a turnover of about 5,5
      million €
     Research areas
         Knowledge and media technologies
         Computational logistics
         Spatial-temporal data mining,
          quality aspects in the area of geographic
          information (GI), GI software technologies
         Research and consulting in early phases of
          innovation management
         IT- security and QoS networks
         Salzburg NewMediaLab – The Next
          Generation (COMET)
     The core activities comprise applied research, technological and methodological
      support, co-ordination and networking, know how transfer and scientific studies.



©              11.04.2013      "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   3
Guide through the presentation

                                      • Linked Data Principles
    The Web of Data                   • Foundations (RDF, RDFS, OWL, SPARQL, …)
                                      • Vocabularies (DC, SKOS, SIOC, FOAF, …)


     Open Semantic                    • Open World Mindset
                                      • Data Outlets
       Enterprise                     • Data Inlets


                                      • Case studies
        Solutions                     • Applications
                                      • Conceptual approaches


                                      • Knowledge Extraction
      Technologies                    • Networked Knowledge
                                      • Knowledge Interaction


      The Big Data                    • Linked Open Data Cloud
                                      • Scalability
       Challenge                      • Query, Analysis



©         11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   4
Definition: „Web of Data“




                                                                      The Open Semantic Enterprise
                                                                     Enterprise Data meets Web Data

©         11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   5
The „Web of Data“: Foundations

     There is a wealth of information on the Web.
     It is aimed mostly towards consumption by
      humans as end-users:
      Recognize the meaning behind
       content and draw conclusions,
      Infer new knowledge using
       context and
      Understand background
       information.



                                                                                                      by


©         11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)        6
The „Web of Data“: Foundations

     Billions of diverse documents online, but it is not easily
      possible to automatically:
       Retrieve relevant documents.
       Extract information.
       Combine information in a meaningful way.
     Idea:
       Also publish machine processable data on the web.
       Formulate questions in terms understandable for a machine.
       Do this in a standardized way so machines can interoperate.
     The Web becomes a Web of Data
       This provides a common framework to share knowledge
        on the Web across application boundaries.
                                                        by


©           11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   7
The „Web of Data“: Evolution




                                                                                                      by


©         11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)        8
The Evolution of the Web




                                               Attribution:


©         11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   9
The „Web of Data“: Foundations

     Uniform Resource Identifier (URI)
      Compact sequence of characters that identifies an abstract or
       physical resource.

     Examples
     ldap://[2001:db8::7]/c=GB?objectClass?one
     mailto:John.Doe@example.com
     news:comp.infosystems.www.servers.unix tel:+1-816-555-1212
     telnet://192.0.2.16:80/
     urn:oasis:names:specification:docbook:dtd:xml:4.1.2
     http://dbpedia.org/resource/Karlsruhe




                                                                                                      by


©         11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)        10
The „Web of Data“: Foundations

     Vocabularies
      Collections of defined relationships and classes of resources.
      Classes group together similar resources.
     Terms from well-known vocabularies should be reused
      wherever possible.
     New terms should be define only if you can not find
      required terms in existing vocabularies.
     e.g. FOAF, DC, SIOC, SKOS



                                                                                                       by


©          11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)        11
The „Web of Data“: Foundations

     A set of well-known vocabularies has evolved in the
      Semantic Web community. Some of them are:
      Friend-of-a-Friend (FOAF): Vocabulary for describing people.
      Dublin Core (DC): Defines general metadata attributes.
      Semantically-Interlinked Online Communities (SIOC): Vocabulary
       for representing online communities.
      Simple Knowledge Organization System (SKOS):
       Vocabulary for representing taxonomies
       and loosely structured knowledge.




                                                                                                      by


©         11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)        12
The „Web of Data“: Linked Data Principles

     Set of best practices for publishing data on the Web.
     Data from different knowledge domains, self-described,
      linked and accessible.
     Follows 4 simple principles…
     1. Use URIs as names for things.
     2. Use HTTP URIs so that users can look up those names.
     3. When someone looks up a URI, provide useful information,
        using the standards (RDF*, SPARQL).
     4. Include links to other URIs, so that users can discover more
        things.

                                                                                                      by


©         11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)        13
The „Web of Data“: Linked Data Rating



                       Data is available on the Web.

                       Data is available as machine-readable structured data.

                       Non-proprietary formats are used.

                       Individual data identified with open standards.

                       Data is linked to other data provider.




©         11.04.2013    "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   14
Vision: The Open Semantic Enterprise




                                                                      The Open Semantic Enterprise
                                                                     Enterprise Data meets Web Data

©         11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   15
Motivation

                                                                                                  Enterprise data and
                                                                                                   media assets are often
                                                                                                   locked away in content
                                                                                                   silos (usually
                                                                                                   proprietary platforms
                                                                                                   and systems)

                                                                                                  This results in
                                                                                                   redundancy of content
                                                                                                   and metadata, efforts
                                                                                                   and costs




©         11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)                         16
Motivation

                                                                                                  Given
                                                                                                   heterogeneous,
                                                                                                   incomplete datasets with
                                                                                                   different formats and
                                                                                                   data models

                                                                                                  Required
                                                                                                   unified data
                                                                                                   representation with
                                                                                                   connected datasets,
                                                                                                   with context information
                                                                                                   from the domain and
                                                                                                   with additional
                                                                                                   information from the
                                                                                                   Web




©         11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)                           17
Motivation

                                                                                                  Solution
                                                                                                   „Integration“ on several
                                                                                                   layers (e.g. content,
                                                                                                   metadata, user
                                                                                                   interfaces/ portals,
                                                                                                   services, applications)

                                                                                                  Results
                                                                                                   Positive effects on the
                                                                                                   efforts and costs for the
                                                                                                   creation, preservation,
                                                                                                   interaction,
                                                                                                   enhancement,
                                                                                                   personalisation.




©         11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)                            18
Foundations of Smart Enterprises

     Seven pillars for Smart Enterprises
      (cf. „Open Semantic Enterprise“, Michael K. Bergman)


      1. Graph-based data model (RDF)
      2. (Open) Linked Data technologies
      3. Adaptive ontologies
      4. Ontology-driven applications
      5. Web-oriented architecture
         (from linked documents
         to linked data)
      6. Layered approach
      7. Open World Mindset


       … moreover, …. people (!)

                          See www.mkbergman.com/859/seven-pillars-of-the-open-semantic-enterprise


©            11.04.2013         "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   19
The Open Semantic Enterprise: Layered Approach




                       See www.mkbergman.com/859/seven-pillars-of-the-open-semantic-enterprise


©         11.04.2013         "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   20
The „Open Semantic Enterprise“: Evolution or Revolution?

     Does this mean open data or open source ? NO, but …
       They are suitable for these purposes with many open source tools available.
       They can equivalently be applied to internal, closed, proprietary data and
        structures.
       The techniques can themselves be used as a basis for bringing external
        information into the enterprise.
     Is there a requirement replacing current systems and assets? NO, …
       The practices can be applied equally to public or proprietary information.
       They can be tested and deployed incrementally at low risk and cost.
       Learn-as-you-go approach and active and agile adaptation.
       Accomplished with minimal disruption
     Change Management
       Embracing the open semantic enterprise is fundamentally a people process.
       Leadership and vision is necessary to begin the process.

                          See www.mkbergman.com/859/seven-pillars-of-the-open-semantic-enterprise


©            11.04.2013         "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   21
Implementation of the Vision in Enterprises


                           Institutional “Content Silos”                                                                                                                                                                                                                                                                                        Trusted Content Providers
                               Media- and document archives                                                                                                                                                                                                                                                                                                Partner organisations
                                  Web content (Wikis, Blogs)                                                                                                                                                                                                                                                                                             Syndication, RSS-Feeds
                                        Newsgroups, eMails                                                                                                                                                                                                                                                                                                              Agencies
                                                                                                                                                                                                                                                                                                                                                                    Web Content
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                                                                                                                                                                                               Knowledge Space                                                                                                          Communities, Social Networks
                                                                                                                                                                        Linked Data, Open Data,                                                                                                                         Customers, subscribers, employees, prosumers
                                                                                                                                                                                     Taxonomies



©             11.04.2013                                      "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)                                                                                                                                                                                                                                         22
Toolset to implement an Open Semantic Enterprise




                                                                      The Open Semantic Enterprise
                                                                     Enterprise Data meets Web Data

©         11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   23
Toolset for Open Semantic Enterprises (1)

     The „Toolset“ for Smart Enterprises comprises Open Source tools and
      frameworks, that can easily be integrated into existing applications
      without replacing them

     Knowledge Extraction (Enhancement)
         Natural language processing (NLP)
         Entity linking und disambiguation
         Content classification
         Metadata extraction
     Networked Knowledge (Linked Media Platform)
         Implementing the Read-/Write-Webs
          based on the Linked Data Principles
         Linked Data Platform (Apache Marmotta)
         Data Federation
         Caching
         Versioning
         Reasoning




©             11.04.2013      "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   24
Architecture of the Linked Data Platform




©         11.04.2013       "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   25
Toolset for Open Semantic Enterprises (2)

     The „Toolset“ for Smart Enterprises comprises Open Source tools
      and frameworks, that can easily be integrated into existing
      applications without replacing them

     Knowledge (Inter-)Activation
       Decoupling of the CMS and the semantic interaction
       Semantic content editing
       Knowledge based navigation
       Semantic search

     Open Source: Apache License 2.0 (permissive)




©            11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   26
Solutions




                                                                      The Open Semantic Enterprise
                                                                     Enterprise Data meets Web Data

©         11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   27
Applications and Use Cases




©         11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   28
Case Studies: Semantic Technologies in the Enterprise

     Various applications (not restricted to enterprise sector)
      are listed, e.g. in the directory of „Semantic Web Case
      Studies and Use Cases” at
      http://www.w3.org/2001/sw/sweo/public/UseCases/
     Sectors:
       automotive (2), broadcasting (2), energy (3), IT industry (5), oil & gas (3),
        publishing (4), telecommunications (4), utilities (1) (out of totally 46 entries
        as of Sep. 2012)
     Some examples:
       Contextual Search for Volkswagen and the Automotive Industry (Link)
       How Ontologies and Rules Help to Advance Automobile Development
        (use case at AUDI) (Link)
       Semantic Web Technologies in Automotive Repair and Diagnostic (use
        case at Renault) (Link)
       Active Knowledge Management for Integrated Operations (use case at
        Statoil) (Link)
       B2B Integration with Semantic Mediation (use case at BT Research) (Link)
       WEASEL: Corporate Semantic Web (use case by Vodafone R&D) (Link)


©             11.04.2013       "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   29
Case Studies: Salzburg NewMediaLab

     Exploitation scenarios in “Salzburg NewMediaLab – The
      Next Generation” (SNML-TNG), a centre of excellent
      technologies in the COMET programme
      (www.newmedialabn.at, labs.newmedialab.at)
     Some examples:
       Concept based annotation in the ORF media
       Semantic search and annotation of media fragments
        in the Red Bull Content Pool
       Search and recommendation in a heterogeneous content pool at
        Salzburger Nachrichten
       Enterprise search at Salzburg AG
       Search and recommendation in a job portal at derStandard.at




©            11.04.2013    "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   30
Scenario: „Wings for the Red Bull Content Pool“

     Search and display of semantically enhanced video fragments




    Data and Information Sources                                     Technologies and concepts
     Information from various enterprise                             Resource Description Framework (RDF)
      data sources                                                    Ontology for Media Resources
     Additionally Web of Data                                        Media Fragments URI
                                                                      SPARQL 1.1 Query Language
                                                                      HTML 5


©             11.04.2013     "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)     31
Scenario: „Wings for the Red Bull Content Pool“

     Source material: videos and text transcripts
      (terminology „concepts“ are manually marked in the screenshot below)




©             11.04.2013      "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   32
Scenario: „Wings for the Red Bull Content Pool“

     Content Enhancement with Apache Stanbol




©           11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   33
Scenario: „Wings for the Red Bull Content Pool“

     Structured metadata in the LMF
     Semantic search and navigation




©           11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   34
Scenario: „Wings for the Red Bull Content Pool“

     HTML5-Player for video fragments (temporal, spacial)
     Time code synchronized visualisation of concepts („catamaran“)




©            11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   35
Scenario: „Wings for the Red Bull Content Pool“

     Annotation with concepts from the „Web of Data“ (DBpedia)
     Interactive extension of the „knowledge base“




©            11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   36
The Big Data Challenge




                                                        The Open Semantic Enterprise
                                                       Enterprise Data meets Web Data

©
11.04.2013                        "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   37
Linked Data is Big Data




                    Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/


©
Linked Data is Big Data

     Linked Data volume by domain (as of Sep. 2011)

                              Number of
     Domain                                      Triples        %        (Out-)Links            %
                               datasets
     Media                       25        1,841,852,061    5.82 %       50,440,705    10.01 %

     Geographic                  31        6,145,532,484   19.43 %       35,812,328     7.11 %

     Government                  49       13,315,009,400   42.09 %       19,343,519     3.84 %

     Publications                87        2,950,720,693    9.33 %      139,925,218    27.76 %

     Cross-domain                41        4,184,635,715   13.23 %       63,183,065    12.54 %

     Life sciences               41        3,036,336,004    9.60 %      191,844,090    38.06 %

     User-generated content      20         134,127,413     0.42 %        3,449,143     0.68 %

                                 295      31,634,213,770                503,998,829

                                                              cf. http://lod-cloud.net/state/



©
Linked Data is Big Data

     State-of-the-art big data platforms need to process massive
      quantities of data in batch and in parallel - filtering, transforming and
      sorting it before loading it into an enterprise data warehouse. In order
      to realize an Open Semantic Enterprise, a big data platform has to
      be optimized for acquiring, organizing, and loading unstructured
      data. Technological approaches such as NoSQL databases and
      connectors for Apache Hadoop complement big data solutions for the
      open world of a semantic enterprise.




©           11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   40
Oracle Spatial and Graph option
          Graph Features
      Network Data Model graph
               – Manages logical / spatial networks in database
               – Persists link/node structure, connectivity and
                 direction
               – Supports constraints at link and node level
               – Logically partitioning network graphs for scalability


      RDF Semantic graph
            – Enterprise class RDF Graph Database
             – Scales to petabytes of triples – by exploiting Exadata,
                 RAC, SQL*Loader , Parallelism, Label Security
               – W3C standards: RDFS, OWL2 RL, OWL2 EL,
                 SPARQL 1.1, RDB2RDF, RDFa, SKOS
               – SQL, PL/SQL APIs and Java APIs (Jena/Sesame)




41   Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
RDF for Enterprise Integration


Access & Presentation Layer




                                                                                                                                                                 Index
RDF metadata layer
(integrated graph metadata)




Data Servers


                                                                               Event Server   Big Data Appliance      Content Mgmt         BI Server     Data Warehouse




Data Sources / Types

                                                                                                               Human Sourced
                                                                   Machine Generated Data     Social Media         Information       Subscription Services   Transaction Systems




   42   Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
Merging Customer Application Tables




                                    Table 1                                 Table 2



43   Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
Red Application has existing data model




44   Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
Blue Application has existing data model




              But, users need to integrate Red & Blue models


45   Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
Merging RDF models



            Step 1: Merge RDF
            Same nodes (URIs) join automatically




46   Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
Enriching your model with Relationships and
              Rules


            Step 2: Add relationships and rules
            (Relationships are also RDF)




47   Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
Flexible metadata model for new app
               requirements
 Step 3: Define Green model
 (Making use of Red
 & Blue models)




 48   Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
Ease of data integration – no change to
             legacy apps!


          What the Blue app sees:
                    – No difference!




49   Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
Supporting Breadth of Enterprise Data
                End-user and Developer Environments
                                          Developers                                                  Data Scientists                                     Business Users

                        Data Integration                  JDeveloper                      Discovery          Statistics        Mining         Business Intelligence   Dashboards




                                                                                  Semantic Metadata Layer
                                                                    Data Services


                 Big Data Sources                                                                Text               Graph                                                   Applications
                                                                             Statistics                                                 Spatial
                                                                                               Analytics           Analytics
                                            Streaming
                      Structured            Services                                                                                                App Services
                         Data                                                                Natural Lang.         Sound and
                                                                            Data Mining                                                 Images
                                                                                              Processing             Video                              Web-log
                                                                                                                                                      Sessionizatio
                                                  Event
                    Unstructured                                                                                                                         n and               Vertical
                                                Processin           Data Management
                       Data                                                                     ODBC              JDBC                                 Enrichment          Applications
                                                    g

                                                                             NoSQL                    Hadoop                     Relational
                    Social Media                                                                                                                        Sentiment
                                                                                                                                                         Analysis
                                                                                                                                                                            Horizontal
                                                                                                                                                                           Applications


                                                                                                                                                        Reference
                                                                                                                                                       Architecture


                                                                            Compression           Security & Encryption




50   Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
Use Case: Aligning Unstructured Content

                               Oracle Big Data Appliance                                              Oracle RDF          Oracle Advanced
                                    (Entity extract and annotate as RDF)                            Semantic Graph           Analytics




                                                                                                                    InfiniBand
                                                                                       InfiniBand




                                                                                  Bulk Load
                                                                                  RDF triples          RDF Models
                                           Unstructured Documents



            Stream                                Acquire                   Organize                  Analyze & Visualize



51   Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
Enterprise Collaboration – Social Collaboration
                Graph                   I                                                        nfo
                                                                                                                      Communities
                                                                               People


                                                                                            RDF Store
Semantic
GRAPH
(Metadata)


                                                                                          Social Graph




                                        e.g. “product catalog”,                                                                     e.g. “job roles”,
                                                                                        e.g. ”web content”, “wiki
                                        “Directory”                                                                                 “customer accounts”
                                                                                        topics”, “expertise”
Extenders /
                                        Enterprise Vocabulary                                                                   Entity and Property
Connectors                                                                              User Entered Tags
                                        Import                                                                                  Extraction
                                                                                         Import



Physical Data                                Structured Data
                                                                                                                             Transactional
                                                                                                                              Applications
                                                                                               Content Repositories



   52   Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
Oracle Capabilities


            Scalability: Persistent storage scales to hundreds of billion triples
                     – Leading competitors are in-memory DBs
                     – Parallelism, compression, Exadata
            Security: Label Security on triples
            Native inferencing capability
            Supports combined query of graph, relational, text, spatial data
            Query: SQL, SPARQL or combined query
            Platforms: SQL and NoSQL storage
            Built-in analysis tools: Advanced Analytics
            Growing ecosystem of 3rd party tools partner




53   Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
Application Areas for Semantic Graph

  Intelligence, Law Enforcement
           – Threat analysis, asset tracking, integrated justice
  Health Care and Bio-Informatics
           – Integrated patient records, bio-surveillance, genomics
  Finance
           – Fraud detection, Compliance Management
  Web and Social Network Solutions
           – Recommender, Social Network Analysis, Activity Analysis
  Media, Games, Content Management
           – Media metadata, content re-purposing




54   Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
Conclusions and Questions




                                                        The Open Semantic Enterprise
                                                       Enterprise Data meets Web Data

©
11.04.2013                        "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   55
Summary

                                     • Linked Data Principles
    The Web of Data                  • Foundations (RDF, RDFS, OWL, SPARQL, …)
                                     • Vocabularies (DC, SKOS, SIOC, FOAF, …)


     Open Semantic                   • Open World Mindset
                                     • Data Outlets
       Enterprise                    • Data Inlets


                                     • Case studies
       Solutions                     • Applications
                                     • Conceptual approaches


                                     • Knowledge Extraction
     Technologies                    • Networked Knowledge
                                     • Knowledge Interaction


     The Big Data                    • Linked Open Data Cloud
                                     • Scalability
      Challenge                      • Query, Analysis



©        11.04.2013   "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   56
References

       IKS-Projekt (EU FP7 – Integrated Project)
             Website: www.iks-project.eu
             Demos: www.iks-project.eu/Demos
       Salzburg NewMediaLab – The Next Generation (K-Projekt)
             Website: www.newmedialab.at
             Labs (Demo-Bereich): labs.newmedialab.at
       Apache Stanbol
             Project Repository: stanbol.apache.org
             Demos: www.iks-project.eu/Demos
       Apache Marmotta
             Project Repository: marmotta.incubator.apache.org
       Apache Lucine/Solr
             Project Repository : lucene.apache.org/solr/
       Linked Media Framework
             Linked Media Principles: www.newmewdialab.at/LinkedMediaPrinciples
             Google Code-Repository: www.newmewdialab.at/LMF, code.google.com/p/LMF
       VIE
             Project Repository: viejs.org
             Demos: www.iks-project.eu/Demos
       Weitere Technologien
             PoolParty: www.poolparty.biz
             LD-Path: www.newmedialab.at/LDPath, code.google.com/p/ldpath/
       Weitere Information
             Open Semantic Enterprise: www.mkbergman.com/859/seven-pillars-of-the-open-semantic-enterprise




©                    11.04.2013             "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)   57
DI Herbert Beilschmidt
    Principal Sales Consultant
    Oracle Austria GmbH
    Wagramer Straße 19 | 1223 Wien, Austria
    Tel. +43 1 33777 0 | Fax +43 1 33777 33
    herbert.beilschmidt@oracle.com




                                                                        This work is licensed under a
                                                                                  Creative Commons
    DI Georg Güntner                                                       Attribution-ShareAlike 3.0
    Head of Salzburg NewMediaLab – The Next Generation                             Unported License.

    Salzburg Research Forschungsgesellschaft m.b.H.
    Jakob-Haringer-Straße 5/3 | Salzburg, Austria         The Open Semantic Enterprise
    Tel. +43 662 2288-401 | Fax +43 662 2288-222         Enterprise Data meets Web Data
    georg.guentner@salzburgresearch.at

©

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The open semantic enterprise enterprise data meets web data

  • 1. Georg Güntner | Salzburg Research; Herbert Beilschmidt | Oracle Austria GmbH The Open Semantic Enterprise - Enterprise Data meets Web Data 2nd B2B Software Days (TechGate Vienna, 11.04.2013)
  • 2. Abstract  The Open Semantic Enterprise. Enterprise Data meets Web Data. The technologies of the “Web od Data” have reached a degree of maturity and acceptance allowing the productive use in enterprises for the support of their business processes. Though the focus is currently on the adoption and use of Open (Linked) Data, the underlying principles can also be applied to the closed data sources and proprietary data structures usually available in enterprises. The workshop outlines the conceptual and architectural approaches to open enterprise data sources and interweave them with the Web of Data. It shows concrete application scenarios of an open source “semantic toolset” that can be integrated with enterprise information and content management systems to open data silos, establish a layer of adaptive integrated views of the enterprise information and support decision processes thus paving the way to an “open semantic enterprise”. The topical semantic toolset for enterprise content integration includes Apache Stanbol (knowledge extraction), Apache Marmotta (Linked Data Platform), the Linked Media Framework (networked knowledge) und VIE (interactive knowledge). State-of-the-art big data platforms need to process massive quantities of data in batch and in parallel - filtering, transforming and sorting it before loading it into an enterprise data warehouse. In order to realize an Open Semantic Enterprise, a big data platform has to be optimized for acquiring, organizing, and loading unstructured data. Technological approaches such as NoSQL databases and connectors for Apache Hadoop complement big data solutions for the open world of a semantic enterprise.  Georg Güntner, Salzburg Research © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 2
  • 3. Salzburg Research  Salzburg Research was founded in 1996 as the research organisation of the Province of Salzburg (www.salzburgresearch.at)  Salzburg Research is located at Techno-Z Salzburg and conducts applied research and development in the area of information and communication technologies (ICT)  Salzburg Research employs about 70 researchers and has a turnover of about 5,5 million €  Research areas  Knowledge and media technologies  Computational logistics  Spatial-temporal data mining, quality aspects in the area of geographic information (GI), GI software technologies  Research and consulting in early phases of innovation management  IT- security and QoS networks  Salzburg NewMediaLab – The Next Generation (COMET)  The core activities comprise applied research, technological and methodological support, co-ordination and networking, know how transfer and scientific studies. © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 3
  • 4. Guide through the presentation • Linked Data Principles The Web of Data • Foundations (RDF, RDFS, OWL, SPARQL, …) • Vocabularies (DC, SKOS, SIOC, FOAF, …) Open Semantic • Open World Mindset • Data Outlets Enterprise • Data Inlets • Case studies Solutions • Applications • Conceptual approaches • Knowledge Extraction Technologies • Networked Knowledge • Knowledge Interaction The Big Data • Linked Open Data Cloud • Scalability Challenge • Query, Analysis © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 4
  • 5. Definition: „Web of Data“ The Open Semantic Enterprise Enterprise Data meets Web Data © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 5
  • 6. The „Web of Data“: Foundations  There is a wealth of information on the Web.  It is aimed mostly towards consumption by humans as end-users:  Recognize the meaning behind content and draw conclusions,  Infer new knowledge using context and  Understand background information. by © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 6
  • 7. The „Web of Data“: Foundations  Billions of diverse documents online, but it is not easily possible to automatically:  Retrieve relevant documents.  Extract information.  Combine information in a meaningful way.  Idea:  Also publish machine processable data on the web.  Formulate questions in terms understandable for a machine.  Do this in a standardized way so machines can interoperate.  The Web becomes a Web of Data  This provides a common framework to share knowledge on the Web across application boundaries. by © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 7
  • 8. The „Web of Data“: Evolution by © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 8
  • 9. The Evolution of the Web Attribution: © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 9
  • 10. The „Web of Data“: Foundations  Uniform Resource Identifier (URI)  Compact sequence of characters that identifies an abstract or physical resource.  Examples ldap://[2001:db8::7]/c=GB?objectClass?one mailto:John.Doe@example.com news:comp.infosystems.www.servers.unix tel:+1-816-555-1212 telnet://192.0.2.16:80/ urn:oasis:names:specification:docbook:dtd:xml:4.1.2 http://dbpedia.org/resource/Karlsruhe by © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 10
  • 11. The „Web of Data“: Foundations  Vocabularies  Collections of defined relationships and classes of resources.  Classes group together similar resources.  Terms from well-known vocabularies should be reused wherever possible.  New terms should be define only if you can not find required terms in existing vocabularies.  e.g. FOAF, DC, SIOC, SKOS by © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 11
  • 12. The „Web of Data“: Foundations  A set of well-known vocabularies has evolved in the Semantic Web community. Some of them are:  Friend-of-a-Friend (FOAF): Vocabulary for describing people.  Dublin Core (DC): Defines general metadata attributes.  Semantically-Interlinked Online Communities (SIOC): Vocabulary for representing online communities.  Simple Knowledge Organization System (SKOS): Vocabulary for representing taxonomies and loosely structured knowledge. by © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 12
  • 13. The „Web of Data“: Linked Data Principles  Set of best practices for publishing data on the Web.  Data from different knowledge domains, self-described, linked and accessible.  Follows 4 simple principles… 1. Use URIs as names for things. 2. Use HTTP URIs so that users can look up those names. 3. When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL). 4. Include links to other URIs, so that users can discover more things. by © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 13
  • 14. The „Web of Data“: Linked Data Rating Data is available on the Web. Data is available as machine-readable structured data. Non-proprietary formats are used. Individual data identified with open standards. Data is linked to other data provider. © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 14
  • 15. Vision: The Open Semantic Enterprise The Open Semantic Enterprise Enterprise Data meets Web Data © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 15
  • 16. Motivation  Enterprise data and media assets are often locked away in content silos (usually proprietary platforms and systems)  This results in redundancy of content and metadata, efforts and costs © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 16
  • 17. Motivation  Given heterogeneous, incomplete datasets with different formats and data models  Required unified data representation with connected datasets, with context information from the domain and with additional information from the Web © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 17
  • 18. Motivation  Solution „Integration“ on several layers (e.g. content, metadata, user interfaces/ portals, services, applications)  Results Positive effects on the efforts and costs for the creation, preservation, interaction, enhancement, personalisation. © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 18
  • 19. Foundations of Smart Enterprises  Seven pillars for Smart Enterprises (cf. „Open Semantic Enterprise“, Michael K. Bergman) 1. Graph-based data model (RDF) 2. (Open) Linked Data technologies 3. Adaptive ontologies 4. Ontology-driven applications 5. Web-oriented architecture (from linked documents to linked data) 6. Layered approach 7. Open World Mindset  … moreover, …. people (!) See www.mkbergman.com/859/seven-pillars-of-the-open-semantic-enterprise © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 19
  • 20. The Open Semantic Enterprise: Layered Approach See www.mkbergman.com/859/seven-pillars-of-the-open-semantic-enterprise © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 20
  • 21. The „Open Semantic Enterprise“: Evolution or Revolution?  Does this mean open data or open source ? NO, but …  They are suitable for these purposes with many open source tools available.  They can equivalently be applied to internal, closed, proprietary data and structures.  The techniques can themselves be used as a basis for bringing external information into the enterprise.  Is there a requirement replacing current systems and assets? NO, …  The practices can be applied equally to public or proprietary information.  They can be tested and deployed incrementally at low risk and cost.  Learn-as-you-go approach and active and agile adaptation.  Accomplished with minimal disruption  Change Management  Embracing the open semantic enterprise is fundamentally a people process.  Leadership and vision is necessary to begin the process. See www.mkbergman.com/859/seven-pillars-of-the-open-semantic-enterprise © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 21
  • 22. Implementation of the Vision in Enterprises Institutional “Content Silos” Trusted Content Providers Media- and document archives Partner organisations Web content (Wikis, Blogs) Syndication, RSS-Feeds Newsgroups, eMails Agencies Web Content Suche mir Angabe Suche mit Kategorien Suche über der Materialart Stichw orten brow sen «extend» Texteingabe «extend» Suche über v om System Suche über v ordefinierte geografischen Begriffe Raum Suche über Zeit Suche {abstract} Trefferliste mit Keyframes anzeigen Suche über Anw endungsgebiet Trefferliste ohne Trefferliste mit Keyframes «include» Kurzbeschreibungen anzeigen ansehen Neueste Beiträge Home User anzeigen lassen Suche v erfeinern «extend» «extend» Suche einengen «extend» Am meisten «extend» gesehene Beiträge anzeigen «extend» Suche erw eitern «extend» Beiträge aus Details zu anderen «extend» Einzelbeitrag Kategorien ansehen ansehen Beiträge derselben Kategorie ansehen «extend» Metadaten zu Beitrag ansehen Videosummaries Andere verwandte Einzelne in Low -res (Dauer, Format,...) Beiträge anzeigen Closed/Private Ausschnitte ansehen lassen {abstract} Closed/Private ansehen Push Serv ice New sletter Interessensgebiete bestellen festlegen «include» Open/Public Open/Public Suche mir Angabe Suche mit Kategorien Suche über der Materialart Stichw orten brow sen «extend» Texteingabe «extend» Suche über v om System Suche über v ordefinierte geografischen Begriffe Raum Suche über Zeit Suche {abstract} Trefferliste mit Keyframes anzeigen Suche über Anw endungsgebiet Trefferliste ohne Trefferliste mit Keyframes «include» Kurzbeschreibungen anzeigen ansehen Neueste Beiträge Home User anzeigen lassen Suche v erfeinern «extend» «extend» Suche einengen «extend» Am meisten «extend» gesehene Beiträge anzeigen «extend» Suche erw eitern «extend» Beiträge aus Details zu anderen «extend» Einzelbeitrag Kategorien ansehen ansehen Beiträge derselben Suche mir Angabe Suche mit Kategorien Suche über Kategorie ansehen der Materialart Stichw orten brow sen «extend» Texteingabe «extend» «extend» Suche über v om System Suche über v ordefinierte Metadaten zu geografischen Begriffe Beitrag ansehen Videosummaries Andere verwandte Raum Einzelne in Low -res Suche über Zeit (Dauer, Format,...) Ausschnitte Beiträge anzeigen Suche {abstract} ansehen lassen {abstract} Trefferliste mit ansehen Keyframes Push Serv ice anzeigen Suche über Anw endungsgebiet Trefferliste ohne New sletter Keyframes Trefferliste mit Interessensgebiete bestellen «include» anzeigen «include» Kurzbeschreibungen festlegen ansehen Neueste Beiträge Home User anzeigen lassen Suche v erfeinern «extend» «extend» Suche einengen «extend» Am meisten «extend» gesehene Beiträge anzeigen «extend» Suche erw eitern «extend» Beiträge aus Details zu anderen «extend» Einzelbeitrag Kategorien ansehen ansehen Beiträge derselben Kategorie ansehen «extend» Metadaten zu Beitrag ansehen Videosummaries Andere verwandte Einzelne in Low -res (Dauer, Format,...) Ausschnitte Beiträge anzeigen ansehen lassen {abstract} ansehen Push Serv ice New sletter Interessensgebiete bestellen festlegen «include» Knowledge Space Communities, Social Networks Linked Data, Open Data, Customers, subscribers, employees, prosumers Taxonomies © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 22
  • 23. Toolset to implement an Open Semantic Enterprise The Open Semantic Enterprise Enterprise Data meets Web Data © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 23
  • 24. Toolset for Open Semantic Enterprises (1)  The „Toolset“ for Smart Enterprises comprises Open Source tools and frameworks, that can easily be integrated into existing applications without replacing them  Knowledge Extraction (Enhancement)  Natural language processing (NLP)  Entity linking und disambiguation  Content classification  Metadata extraction  Networked Knowledge (Linked Media Platform)  Implementing the Read-/Write-Webs based on the Linked Data Principles  Linked Data Platform (Apache Marmotta)  Data Federation  Caching  Versioning  Reasoning © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 24
  • 25. Architecture of the Linked Data Platform © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 25
  • 26. Toolset for Open Semantic Enterprises (2)  The „Toolset“ for Smart Enterprises comprises Open Source tools and frameworks, that can easily be integrated into existing applications without replacing them  Knowledge (Inter-)Activation  Decoupling of the CMS and the semantic interaction  Semantic content editing  Knowledge based navigation  Semantic search  Open Source: Apache License 2.0 (permissive) © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 26
  • 27. Solutions The Open Semantic Enterprise Enterprise Data meets Web Data © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 27
  • 28. Applications and Use Cases © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 28
  • 29. Case Studies: Semantic Technologies in the Enterprise  Various applications (not restricted to enterprise sector) are listed, e.g. in the directory of „Semantic Web Case Studies and Use Cases” at http://www.w3.org/2001/sw/sweo/public/UseCases/  Sectors:  automotive (2), broadcasting (2), energy (3), IT industry (5), oil & gas (3), publishing (4), telecommunications (4), utilities (1) (out of totally 46 entries as of Sep. 2012)  Some examples:  Contextual Search for Volkswagen and the Automotive Industry (Link)  How Ontologies and Rules Help to Advance Automobile Development (use case at AUDI) (Link)  Semantic Web Technologies in Automotive Repair and Diagnostic (use case at Renault) (Link)  Active Knowledge Management for Integrated Operations (use case at Statoil) (Link)  B2B Integration with Semantic Mediation (use case at BT Research) (Link)  WEASEL: Corporate Semantic Web (use case by Vodafone R&D) (Link) © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 29
  • 30. Case Studies: Salzburg NewMediaLab  Exploitation scenarios in “Salzburg NewMediaLab – The Next Generation” (SNML-TNG), a centre of excellent technologies in the COMET programme (www.newmedialabn.at, labs.newmedialab.at)  Some examples:  Concept based annotation in the ORF media  Semantic search and annotation of media fragments in the Red Bull Content Pool  Search and recommendation in a heterogeneous content pool at Salzburger Nachrichten  Enterprise search at Salzburg AG  Search and recommendation in a job portal at derStandard.at © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 30
  • 31. Scenario: „Wings for the Red Bull Content Pool“  Search and display of semantically enhanced video fragments Data and Information Sources Technologies and concepts  Information from various enterprise  Resource Description Framework (RDF) data sources  Ontology for Media Resources  Additionally Web of Data  Media Fragments URI  SPARQL 1.1 Query Language  HTML 5 © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 31
  • 32. Scenario: „Wings for the Red Bull Content Pool“  Source material: videos and text transcripts (terminology „concepts“ are manually marked in the screenshot below) © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 32
  • 33. Scenario: „Wings for the Red Bull Content Pool“  Content Enhancement with Apache Stanbol © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 33
  • 34. Scenario: „Wings for the Red Bull Content Pool“  Structured metadata in the LMF  Semantic search and navigation © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 34
  • 35. Scenario: „Wings for the Red Bull Content Pool“  HTML5-Player for video fragments (temporal, spacial)  Time code synchronized visualisation of concepts („catamaran“) © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 35
  • 36. Scenario: „Wings for the Red Bull Content Pool“  Annotation with concepts from the „Web of Data“ (DBpedia)  Interactive extension of the „knowledge base“ © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 36
  • 37. The Big Data Challenge The Open Semantic Enterprise Enterprise Data meets Web Data © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 37
  • 38. Linked Data is Big Data Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/ ©
  • 39. Linked Data is Big Data  Linked Data volume by domain (as of Sep. 2011) Number of Domain Triples % (Out-)Links % datasets Media 25 1,841,852,061 5.82 % 50,440,705 10.01 % Geographic 31 6,145,532,484 19.43 % 35,812,328 7.11 % Government 49 13,315,009,400 42.09 % 19,343,519 3.84 % Publications 87 2,950,720,693 9.33 % 139,925,218 27.76 % Cross-domain 41 4,184,635,715 13.23 % 63,183,065 12.54 % Life sciences 41 3,036,336,004 9.60 % 191,844,090 38.06 % User-generated content 20 134,127,413 0.42 % 3,449,143 0.68 % 295 31,634,213,770 503,998,829 cf. http://lod-cloud.net/state/ ©
  • 40. Linked Data is Big Data  State-of-the-art big data platforms need to process massive quantities of data in batch and in parallel - filtering, transforming and sorting it before loading it into an enterprise data warehouse. In order to realize an Open Semantic Enterprise, a big data platform has to be optimized for acquiring, organizing, and loading unstructured data. Technological approaches such as NoSQL databases and connectors for Apache Hadoop complement big data solutions for the open world of a semantic enterprise. © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 40
  • 41. Oracle Spatial and Graph option Graph Features  Network Data Model graph – Manages logical / spatial networks in database – Persists link/node structure, connectivity and direction – Supports constraints at link and node level – Logically partitioning network graphs for scalability  RDF Semantic graph – Enterprise class RDF Graph Database – Scales to petabytes of triples – by exploiting Exadata, RAC, SQL*Loader , Parallelism, Label Security – W3C standards: RDFS, OWL2 RL, OWL2 EL, SPARQL 1.1, RDB2RDF, RDFa, SKOS – SQL, PL/SQL APIs and Java APIs (Jena/Sesame) 41 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
  • 42. RDF for Enterprise Integration Access & Presentation Layer Index RDF metadata layer (integrated graph metadata) Data Servers Event Server Big Data Appliance Content Mgmt BI Server Data Warehouse Data Sources / Types Human Sourced Machine Generated Data Social Media Information Subscription Services Transaction Systems 42 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
  • 43. Merging Customer Application Tables Table 1 Table 2 43 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
  • 44. Red Application has existing data model 44 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
  • 45. Blue Application has existing data model But, users need to integrate Red & Blue models 45 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
  • 46. Merging RDF models  Step 1: Merge RDF  Same nodes (URIs) join automatically 46 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
  • 47. Enriching your model with Relationships and Rules  Step 2: Add relationships and rules  (Relationships are also RDF) 47 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
  • 48. Flexible metadata model for new app requirements  Step 3: Define Green model  (Making use of Red & Blue models) 48 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
  • 49. Ease of data integration – no change to legacy apps!  What the Blue app sees: – No difference! 49 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
  • 50. Supporting Breadth of Enterprise Data End-user and Developer Environments Developers Data Scientists Business Users Data Integration JDeveloper Discovery Statistics Mining Business Intelligence Dashboards Semantic Metadata Layer Data Services Big Data Sources Text Graph Applications Statistics Spatial Analytics Analytics Streaming Structured Services App Services Data Natural Lang. Sound and Data Mining Images Processing Video Web-log Sessionizatio Event Unstructured n and Vertical Processin Data Management Data ODBC JDBC Enrichment Applications g NoSQL Hadoop Relational Social Media Sentiment Analysis Horizontal Applications Reference Architecture Compression Security & Encryption 50 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
  • 51. Use Case: Aligning Unstructured Content Oracle Big Data Appliance Oracle RDF Oracle Advanced (Entity extract and annotate as RDF) Semantic Graph Analytics InfiniBand InfiniBand Bulk Load RDF triples RDF Models Unstructured Documents Stream Acquire Organize Analyze & Visualize 51 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
  • 52. Enterprise Collaboration – Social Collaboration Graph I nfo Communities People RDF Store Semantic GRAPH (Metadata) Social Graph e.g. “product catalog”, e.g. “job roles”, e.g. ”web content”, “wiki “Directory” “customer accounts” topics”, “expertise” Extenders / Enterprise Vocabulary Entity and Property Connectors User Entered Tags Import Extraction Import Physical Data Structured Data Transactional Applications Content Repositories 52 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
  • 53. Oracle Capabilities  Scalability: Persistent storage scales to hundreds of billion triples – Leading competitors are in-memory DBs – Parallelism, compression, Exadata  Security: Label Security on triples  Native inferencing capability  Supports combined query of graph, relational, text, spatial data  Query: SQL, SPARQL or combined query  Platforms: SQL and NoSQL storage  Built-in analysis tools: Advanced Analytics  Growing ecosystem of 3rd party tools partner 53 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
  • 54. Application Areas for Semantic Graph  Intelligence, Law Enforcement – Threat analysis, asset tracking, integrated justice  Health Care and Bio-Informatics – Integrated patient records, bio-surveillance, genomics  Finance – Fraud detection, Compliance Management  Web and Social Network Solutions – Recommender, Social Network Analysis, Activity Analysis  Media, Games, Content Management – Media metadata, content re-purposing 54 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
  • 55. Conclusions and Questions The Open Semantic Enterprise Enterprise Data meets Web Data © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 55
  • 56. Summary • Linked Data Principles The Web of Data • Foundations (RDF, RDFS, OWL, SPARQL, …) • Vocabularies (DC, SKOS, SIOC, FOAF, …) Open Semantic • Open World Mindset • Data Outlets Enterprise • Data Inlets • Case studies Solutions • Applications • Conceptual approaches • Knowledge Extraction Technologies • Networked Knowledge • Knowledge Interaction The Big Data • Linked Open Data Cloud • Scalability Challenge • Query, Analysis © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 56
  • 57. References  IKS-Projekt (EU FP7 – Integrated Project)  Website: www.iks-project.eu  Demos: www.iks-project.eu/Demos  Salzburg NewMediaLab – The Next Generation (K-Projekt)  Website: www.newmedialab.at  Labs (Demo-Bereich): labs.newmedialab.at  Apache Stanbol  Project Repository: stanbol.apache.org  Demos: www.iks-project.eu/Demos  Apache Marmotta  Project Repository: marmotta.incubator.apache.org  Apache Lucine/Solr  Project Repository : lucene.apache.org/solr/  Linked Media Framework  Linked Media Principles: www.newmewdialab.at/LinkedMediaPrinciples  Google Code-Repository: www.newmewdialab.at/LMF, code.google.com/p/LMF  VIE  Project Repository: viejs.org  Demos: www.iks-project.eu/Demos  Weitere Technologien  PoolParty: www.poolparty.biz  LD-Path: www.newmedialab.at/LDPath, code.google.com/p/ldpath/  Weitere Information  Open Semantic Enterprise: www.mkbergman.com/859/seven-pillars-of-the-open-semantic-enterprise © 11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 57
  • 58. DI Herbert Beilschmidt Principal Sales Consultant Oracle Austria GmbH Wagramer Straße 19 | 1223 Wien, Austria Tel. +43 1 33777 0 | Fax +43 1 33777 33 herbert.beilschmidt@oracle.com This work is licensed under a Creative Commons DI Georg Güntner Attribution-ShareAlike 3.0 Head of Salzburg NewMediaLab – The Next Generation Unported License. Salzburg Research Forschungsgesellschaft m.b.H. Jakob-Haringer-Straße 5/3 | Salzburg, Austria The Open Semantic Enterprise Tel. +43 662 2288-401 | Fax +43 662 2288-222 Enterprise Data meets Web Data georg.guentner@salzburgresearch.at ©