1. Designing
Semantic
CMS – Part I
Semantic CMS Community
Lecturer
Organization
Date of presentation
Co-funded by the
1 Copyright IKS Consortium
European Union

2. Page:
Part I: Foundations
(1) Introduction of Content Foundations of Semantic
(2)
Management Web Technologies
Part II: Semantic Content Part III: Methodologies
Management
Knowledge Interaction Requirements Engineering
(3) (7)
and Presentation for Semantic CMS
(4) Knowledge Representation
and Reasoning
(8)
Designing
Semantic CMS
Semantifying
(5) Semantic Lifting (9) your CMS
Storing and Accessing Designing Interactive
(6) Semantic Data
(10) Ubiquitous IS
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3. Page: 3
What is this Lecture about?
 We have seen ... Part III: Methodologies
 ... how requirements for
semantic content management Requirements Engineering
(7)
are defined in a systematic way. for Semantic CMS
 ... a list of industry needs. Designing
(8) Semantic CMS
 What is missing?
Semantifying
 An efficient way to design an (9) your CMS
architecture for a semantic CMS
that meets the defined (10)
Designing Interactive
requirements Ubiquitous IS
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How to design a semantic
CMS?
What does the
Conceptual Part 1
architecture of a
Reference IKS Reference
semantic CMS look
Architecture Architecture
like?
Technical
How can a semantic Part 2
Architectural
CMS be realized? REST Architecture
Style
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Towards Semantic Content
Management
Content Semantic
Management Content Management
extract knowledge
from content
Content Content Knowledge
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How to build a Semantic CMS?
 Requirements from industry
 Easy integration with existing CMS
 Reuse features of existing CMS
 Use RESTful interfaces
 Semantic features as optional components
 Functional requirements
 Automatic extraction of entities from text
 Automatic extraction of relations between entities
 Automatic categorization of content
 Automatic linking of content
 ...
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What are semantic CMS?
A Semantic CMS is a CMS with the capability of
interacting with Presentation and Interaction Layer
semantic metadata,
extracting Semantic Lifting Layer
semantic metadata,
managing Knowledge Representation and
semantic metadata, Reasoning Layer
and storing Persistence Layer
semantic metadata
about content.
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Traditional CMS Architecture
for Content
User Interface Presentation Layer
Content Access
Business Logic Layer
Content Management
Administration
Content
Data Representation
Content Data Model Layer
Content Repository Persistence Layer
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Reference Architecture for
Semantic CMS
Semantic User Interaction Presentation &
Interaction Layer
Knowledge Access
Knowledge
Extraction Pipelines Semantic Lifting Layer
Administration
Knowledge
Reasoning Knowledge
Representation and
Knowledge Models Reasoning Layer
Knowledge Repository Persistence Layer
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Semantic User Interaction
 Dealing with knowledge in semantic CMS raises the
need an additional user interface level that allows the
interaction with content,
 Example:
 “A user writes an article and the SCMS recognizes the
brand of a car in that article. An SCMS includes a
reference to an object representing that car manufacturer
– not only the brand name. The user can Semantic User Interaction
Knowledge Access
interact with the car manufacturer object and Knowledge
Extraction Pipelines
Administration
see, e.g. the location of its headquarter.
Knowledge
Reasoning
Knowledge Models
Knowledge Repository
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Knowledge Access
 Access to inferred and extracted knowledge is
encapsulated through a Knowledge Access layer
 It provides the access to knowledge for Semantic User
Interaction.
Semantic User Interaction
Knowledge Access
Knowledge
Extraction Pipelines
Administration
Knowledge
Reasoning
Knowledge Models
Knowledge Repository
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Knowledge Extraction
Pipelines
 The main challenge for semantic CMS is the ability to
extract knowledge in terms of semantic metadata from
the stored content.
 A separate layer for Knowledge Extraction Pipelines
encapsulates algorithms for semantic metadata
extraction.
 Typically, knowledge extraction is a
Semantic User Interaction
multistage process [FL04] by applying Knowledge Access
Knowledge
Extraction Pipelines
different IE/IR algorithms
Administration
Knowledge
Reasoning
Knowledge Models
Knowledge Repository
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Pipeline Processing - Example
Content Pre- Entity Relation
Extraction Processing Extraction Extraction
John Miller has brought a Jaguar car this year.
Person Car Time
Manufacturer
Relation
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Reasoning
 After lifting content to a semantic level this extracted
information may be used as inputs for reasoning
techniques in the Reasoning layer
 Logical reasoning is a well-known artificial intelligence
technique that uses semantic relations to retrieve
knowledge about the content that was not explicitly
known before.
Semantic User Interaction
Knowledge Access
Knowledge
Extraction Pipelines
Administration
Knowledge
Reasoning
Knowledge Models
Knowledge Repository
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Knowledge Models
 Knowledge (representation) Models that define the
semantic metadata are used to express knowledge
 Ontologies can be used to define semantic metadata
that specifies so-called concepts and their semantic
relations.
Semantic User Interaction
Knowledge Access
Knowledge
Extraction Pipelines
Administration
Knowledge
Reasoning
Knowledge Models
Knowledge Repository
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Knowledge Repository
 Knowledge is stored in a Knowledge Repository that
defines the fundamental data structure for knowledge
 State-of-the-art knowledge repositories implement a
triple store where a triple is formed by a subject, a
predicate, and an object
 A triple can be used to express any relation between a
subject and an object Semantic User Interaction
Knowledge Access
Knowledge
Extraction Pipelines
Administration
Knowledge
Reasoning
Knowledge Models
Knowledge Repository
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Knowledge Administration
 Knowledge Administration includes the management of:
 Semantic User Interaction templates,
 Knowledge Extraction Pipeline management
 Reasoning management to the administration of
Knowledge Models and Repositories.
Semantic User Interaction
Knowledge Access
Knowledge
Extraction Pipelines
Administration
Knowledge
Reasoning
Knowledge Models
Knowledge Repository
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Integration
Semantic User Interface
User Interface Semantic User Interaction
Content Access Knowledge Access
Knowledge
Extraction Pipelines
Content
Administration
Administration
Knowledge
Management
Content
Reasoning
Content Data Model Knowledge Models
Content Repository Knowledge Repository
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Implementation of the
Reference Architecture
 Reference implementation within
the IKS project
 IKS: An open source community to
bring semantic technologies to CMS
platforms
 New incubating project at the
Apache Software Foundation
http://incubator.apache.org/stanbol
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Implementation of the
Reference Architecture
 One year student project
Information-Driven Software Engineering
 Extract knowledge from unstructured
software specification documents
 Case study: 10.000 pages specification of German Health
Card system
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Breathing life to the
Reference Architecture
Semantic User Interface
User Interface Semantic User Interaction
Content Access Knowledge Access
Knowledge
Extraction Pipelines
Content
Administration
Administration
Knowledge
Management
Content
Reasoning
Content Data Model Knowledge Models
Content Repository Knowledge Repository
Content Management
ID|SE Platform
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Problem Statement
Requirements
Analysis &
Engineering
? Design
Implementation &
Test
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Problem Statement
 Documents and Artifacts created in the software
development process contain implicit information:
 Type of the document (e.g. requirements specification)
 Named Entities (e.g. actor „User“)
 Relations between the different document are not obvious
 Thematically similar
 Duplicates
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ID|SE Demo
http://idse.cs.upb.de:8082/opencms/opencms/idse
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ID|SE-Platform – Architecture
<<OpenCMS>>
ID|SE-Service-Platform
IE/IR-Service-Orchestrators
Content- Meta-
Management- Data-
System Search
Evaluation-
Services IE/IR-Services
Content-
Management Meta-Data-Model
Document-Content- Meta-Data-Storage
Storage
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Mapping with Reference
Architecture
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ID|SE-Platform
1. Send Request to the ID|SE Platform
<<OpenCMS>>
Webservice
Content Management
System
ID|SE-Service Platform
<<OpenCMS-Module>>
GUI IEIR-ServiceOrchestrators
DefaultMetaDataCreator
Webservice
IDefaultMetaDataCreator
DefaultMetaDataCreator
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ID|SE-Platform
2. Providing Documents
ID|SE-Service Platform
<<OpenCMS>>
Content Management
System
IEIR-ServiceOrchestrators
DefaultMetaData
Creator
<<component>>
DocumentProvider
Content-Management
IProvideDocuments
Webservice OpenCMSDocument
DocumentContent-
Storage
ProviderProxy Copyright IKS Consortium
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ID|SE-Platform
3. Generation of Meta-Data
IE/IR-ServiceOrchestrators
DefaultMetaDataCreator
Evaluation IE/IR-Services
Services
Content- Pre- Named-
Classi- Information-
Extrac- pro- Clusterer Entity-
fier Aggregator
tion cessors Recognizer
MetaDataModel
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31. ID|SE-Platform
Page: 31
4. Providing/Presenting Meta-Data
Webservice
<<OpenCMS>>
Content Management
Meta-Data-Search
System
<<OpenCMS-Module>>
ArtifactSearchGUI IEIR-Services MetaDataSearchEngine
Webservice
MetaDataSearchEngine
MetaDataModel
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MetaDataStorage

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ID|SE Features Clustering
of artefacts
“Which
artefacts are Classification
about „XYZ‟ ” of artefacts
Named
No redundancy in entity
software specification recognition
documents
Duplicate
Efficient way in Check
browsing through
content Facetted
Search
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Evaluation Criteria
Recall
Precision
F-Measure
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Evaluation of Semantic Features
Entity Recognition Classification Clustering
100% 100% 100%
90% 88% 90% 90%
84%
80%
80% 80% 77% 80%
74%
72%
70% 70% 70%
64%
60% 58%
60% 60% 56%
50% 50% 50%
40% 40% 40%
30% 30% 30%
20% 20% 20%
10% 10% 10%
0% 0% 0%
F-Measure Precision Recall F-Measure Precision Recall F-Measure Precision Recall
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Lessons Learned ...
 Now you should know ...
 ... the architectural requirements for a semantic CMS.
 ... the integration concept of two loosely coupled columns.
 ... the components of the reference architecture
 ... how the reference architecture model can used to build
a semantic CMS from scratch and how an extended
system can be extended
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