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
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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.
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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“
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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.
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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.
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The „Web of Data“: Evolution
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The Evolution of the Web
Attribution:
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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:[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
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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
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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.
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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.
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The „Web of Data“: Linked Data Rating
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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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The Open Semantic Enterprise Enterprise Data meets Web Data
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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 (!)
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See 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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See www.mkbergman.com/859/seven-pillars-of-the-open-semantic-enterprise
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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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See www.mkbergman.com/859/seven-pillars-of-the-open-semantic-enterprise
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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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Toolset to implement an Open Semantic Enterprise
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The Open Semantic Enterprise Enterprise Data meets Web Data
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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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The Open Semantic Enterprise Enterprise Data meets Web Data
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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)
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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
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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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Scenario: „Wings for the Red Bull Content Pool“
Source material: videos and text transcripts (terminology „concepts“ are manually marked in the screenshot below)
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Scenario: „Wings for the Red Bull Content Pool“
Content Enhancement with Apache Stanbol
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Scenario: „Wings for the Red Bull Content Pool“
Structured metadata in the LMF
Semantic search and navigation
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Scenario: „Wings for the Red Bull Content Pool“
HTML5-Player for video fragments (temporal, spacial)
Time code synchronized visualisation of concepts („catamaran“)
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Scenario: „Wings for the Red Bull Content Pool“
Annotation with concepts from the „Web of Data“ (DBpedia)
Interactive extension of the „knowledge base“
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The Big Data Challenge
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The Open Semantic Enterprise Enterprise Data meets Web Data
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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 is Big Data
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
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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
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
Copyright © 2012, Oracle and/or its affiliates. All rights reserved. 42
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
Copyright © 2012, Oracle and/or its affiliates. All rights reserved. 43
Merging Customer Application Tables
Table 1 Table 2
Copyright © 2012, Oracle and/or its affiliates. All rights reserved. 44
Red Application has existing data model
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
Copyright © 2012, Oracle and/or its affiliates. All rights reserved. 46
Merging RDF models
Step 1: Merge RDF
Same nodes (URIs) join automatically
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)
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)
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!
Copyright © 2012, Oracle and/or its affiliates. All rights reserved. 50
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
Copyright © 2012, Oracle and/or its affiliates. All rights reserved. 51
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
Copyright © 2012, Oracle and/or its affiliates. All rights reserved. 52
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
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
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
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Conclusions and Questions
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The Open Semantic Enterprise Enterprise Data meets Web Data
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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
The Big Data Challenge
11.04.2013 "The Open Semantic Enterprise - Enterprise Data meets Web Data" (G. Güntner) 56
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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
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DI Georg Güntner
Head of Salzburg NewMediaLab – The Next Generation
Salzburg Research Forschungsgesellschaft m.b.H.
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Tel. +43 662 2288-401 | Fax +43 662 2288-222
The Open Semantic Enterprise Enterprise Data meets Web Data
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DI Herbert Beilschmidt
Principal Sales Consultant
Oracle Austria GmbH
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