The open semantic enterprise enterprise data meets web data

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.


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


Definition: „Web of Data“

The Open Semantic Enterprise
Enterprise Data meets Web Data


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



 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


The „Web of Data“: Evolution

by


The Evolution of the Web

Attribution:



 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



 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



 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


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


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.


Vision: The Open Semantic Enterprise



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


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


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.


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


The Open Semantic Enterprise: Layered Approach



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.



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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Open/Public Open/Public

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Knowledge Space Communities, Social Networks
Linked Data, Open Data, Customers, subscribers, employees, prosumers
Taxonomies


Toolset to implement an Open Semantic Enterprise



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


Architecture of the Linked Data Platform


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)


Solutions



Applications and Use Cases


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)


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


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



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



 Content Enhancement with Apache Stanbol



 Structured metadata in the LMF
 Semantic search and navigation



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



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


The Big Data Challenge


©
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 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/

©


 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.


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


Merging Customer Application Tables

Table 1 Table 2


Red Application has existing data model


Blue Application has existing data model

But, users need to integrate Red & Blue models


Merging RDF models

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


Enriching your model with Relationships and
Rules

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


Flexible metadata model for new app
requirements
 Step 3: Define Green model
 (Making use of Red
& Blue models)


Ease of data integration – no change to
legacy apps!

 What the Blue app sees:
– No difference!


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


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


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


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


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


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


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
 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
 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


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

©

The open semantic enterprise enterprise data meets web data

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