the open semantic enterprise enterprise data meets web data

The Open Semantic Enterprise - Enterprise Data meets Web Data

2nd B2B Software Days (TechGate Vienna, 11.04.2013)

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

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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

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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.


http://www.salzburgresearch.at/

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Guide through the presentation

• Linked Data Principles

• Foundations (RDF, RDFS, OWL, SPARQL, …)

• Vocabularies (DC, SKOS, SIOC, FOAF, …) The Web of Data

• Open World Mindset

• Data Outlets

• Data Inlets

Open Semantic Enterprise

• Case studies

• Applications

• Conceptual approaches Solutions

• Knowledge Extraction

• Networked Knowledge

• Knowledge Interaction Technologies

• Linked Open Data Cloud

• Scalability

• Query, Analysis

The Big Data Challenge


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Definition: „Web of Data“


The Open Semantic Enterprise Enterprise Data meets Web Data

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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

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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

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The „Web of Data“: Evolution


by

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The Evolution of the Web

Attribution:


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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:[email protected]

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

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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

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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

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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

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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.

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Vision: The Open Semantic Enterprise



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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


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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


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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.


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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

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













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The Open Semantic Enterprise: Layered Approach
















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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.
















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Implementation of the Vision in Enterprises

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Institutional “Content Silos” Media- and document archives

Web content (Wikis, Blogs)

Newsgroups, eMails

Trusted Content Providers Partner organisations

Syndication, RSS-Feeds

Agencies

Web Content

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Communities, Social Networks Customers, subscribers, employees, prosumers

Closed/Private

Open/Public

Knowledge Space Linked Data, Open Data,

Taxonomies

Open/Public

Closed/Private

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11.04.2013 22 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner)

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Toolset to implement an Open Semantic Enterprise



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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


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Architecture of the Linked Data Platform


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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)


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Solutions



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Applications and Use Cases


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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)


http://www.w3.org/2001/sw/sweo/public/UseCases/

http://www.w3.org/2001/sw/sweo/public/UseCases/Volkswagen/

http://www.w3.org/2001/sw/sweo/public/UseCases/Audi/

http://www.w3.org/2001/sw/sweo/public/UseCases/Renault/

http://www.w3.org/2001/sw/sweo/public/UseCases/Aksio/

http://www.w3.org/2001/sw/sweo/public/UseCases/BT/

http://www.w3.org/2001/sw/sweo/public/UseCases/Vodafone-es/

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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


http://www.newmedialabn.at/

http://labs.newmedialab.at/

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Scenario: „Wings for the Red Bull Content Pool“

Search and display of semantically enhanced video fragments

Data and Information Sources

Information from various enterprise

data sources

Additionally Web of Data

Technologies and concepts

Resource Description Framework (RDF)

Ontology for Media Resources

Media Fragments URI

SPARQL 1.1 Query Language

HTML 5


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


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Content Enhancement with Apache Stanbol


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Structured metadata in the LMF

Semantic search and navigation


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HTML5-Player for video fragments (temporal, spacial)

Time code synchronized visualisation of concepts („catamaran“)


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Annotation with concepts from the „Web of Data“ (DBpedia)

Interactive extension of the „knowledge base“


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Linked Data is Big Data

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

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

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

Domain Number of

datasets Triples % (Out-)Links %

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

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





http://www.ckan.net/dataset/search?q=tags:media+AND+groups:lodcloud+AND+-tags:lodcloud.unconnected+AND+-tags:lodcloud.needsfixing

http://www.ckan.net/dataset/search?q=tags:geographic+AND+groups:lodcloud+AND+-tags:lodcloud.unconnected+AND+-tags:lodcloud.needsfixing

http://www.ckan.net/dataset/search?q=tags:government+AND+groups:lodcloud+AND+-tags:lodcloud.unconnected+AND+-tags:lodcloud.needsfixing

http://www.ckan.net/dataset/search?q=tags:publications+AND+groups:lodcloud+AND+-tags:lodcloud.unconnected+AND+-tags:lodcloud.needsfixing

http://www.ckan.net/dataset/search?q=tags:crossdomain+AND+groups:lodcloud+AND+-tags:lodcloud.unconnected+AND+-tags:lodcloud.needsfixing

http://www.ckan.net/dataset/search?q=tags:lifesciences+AND+groups:lodcloud+AND+-tags:lodcloud.unconnected+AND+-tags:lodcloud.needsfixing

http://www.ckan.net/dataset/search?q=tags:usergeneratedcontent+AND+groups:lodcloud+AND+-tags:lodcloud.unconnected+AND+-tags:lodcloud.needsfixing

http://www.ckan.net/dataset/search?q=groups:lodcloud+AND+-tags:lodcloud.unconnected+AND+-tags:lodcloud.needsfixing

©


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.


Copyright © 2012, Oracle and/or its affiliates. All rights reserved. 41

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)

Oracle Spatial and Graph option

Graph Features


RDF for Enterprise Integration

Index

Content Mgmt BI Server Data Warehouse

Machine Generated Data

RDF metadata layer

(integrated graph metadata)

Transaction Systems

Big Data Appliance

Subscription Services

Human Sourced

Information Social Media

Event Server

Data Servers

Data Sources / Types

Access & Presentation Layer


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!


Big Data Sources Big Data Sources Applications Applications

End-user and Developer Environments

End-user and Developer Environments

Streaming

Services

Streaming

Services

Data Services Data Services

Statistics Statistics Text

Analytics

Text

Analytics

Graph

Analytics

Graph

Analytics Spatial Spatial

Data Mining Data Mining Natural Lang.

Processing

Natural Lang.

Processing

Structured

Data

Structured

Data

Unstructured

Data

Unstructured

Data

App Services App Services

Developers Developers

Data Integration Data Integration

Business Users Business Users

Business Intelligence Business Intelligence

Data Scientists Data Scientists

Discovery Discovery

Event

Processin

g

Event

Processin

g Data Management Data Management

NoSQL NoSQL Relational Relational Hadoop Hadoop

Web-log

Sessionizatio

n and

Enrichment

Web-log

Sessionizatio

n and

Enrichment

Sentiment

Analysis

Sentiment

Analysis Social Media

Social Media

Statistics Statistics Mining Mining

Supporting Breadth of Enterprise Data

JDeveloper JDeveloper Dashboards Dashboards

Reference

Architecture

Reference

Architecture

Sound and

Video

Sound and

Video Images Images

Compression Compression Security & Encryption Security & Encryption

Vertical

Applications

Vertical

Applications

Horizontal

Applications

Horizontal

Applications

ODBC ODBC JDBC JDBC

Semantic Metadata Layer


Use Case: Aligning Unstructured Content

Oracle Big Data Appliance (Entity extract and annotate as RDF)

Oracle RDF

Semantic Graph

InfiniBand

Acquire Organize Analyze & Visualize Stream

InfiniBand

Bulk Load

RDF triples Unstructured Documents

Oracle Advanced

Analytics

RDF Models


RDF Store

Social Graph

Entity and Property

Extraction

Entity and Property

Extraction Enterprise Vocabulary

Import

Enterprise Vocabulary

Import

e.g. “job roles”,

“customer accounts” e.g. “product catalog”,

“Directory”

User Entered Tags

Import

User Entered Tags

Import

Structured Data

Content Repositories

Transactional

Applications

People Communities

Semantic

GRAPH

(Metadata)

Extenders /

Connectors

Physical Data

Info

e.g. ”web content”, “wiki

topics”, “expertise”

Enterprise Collaboration – Social Collaboration Graph


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

• Foundations (RDF, RDFS, OWL, SPARQL, …)

• Vocabularies (DC, SKOS, SIOC, FOAF, …) The Web of Data

• Open World Mindset

• Data Outlets

• Data Inlets

Open Semantic Enterprise

• Case studies

• Applications

• Conceptual approaches Solutions

• Knowledge Extraction

• Networked Knowledge

• Knowledge Interaction Technologies

• Linked Open Data Cloud

• Scalability

• 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


http://www.iks-project.eu/



http://www.iks-project.eu/Demos



http://www.newmedialab.at/

http://labs.newmedialab.at/

http://stanbol.apache.org/




http://marmotta.incubator.apache.org/

http://lucene.apache.org/solr/



http://www.newmewdialab.at/LinkedMediaPrinciples

http://www.newmewdialab.at/LMF

http://code.google.com/p/LMF

http://viejs.org/




http://www.poolparty.biz/

http://www.newmedialab.at/LDPath

http://code.google.com/p/ldpath/
















©

DI Georg Güntner

Head of Salzburg NewMediaLab – The Next Generation

Salzburg Research Forschungsgesellschaft m.b.H.

Jakob-Haringer-Straße 5/3 | Salzburg, Austria

Tel. +43 662 2288-401 | Fax +43 662 2288-222

[email protected]


This work is licensed under a

Creative Commons

Attribution-ShareAlike 3.0

Unported License.

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

[email protected]

http://creativecommons.org/licenses/by-sa/3.0/









