co-funded by the european union semantic cms community designing semantic cms – part i copyright...
TRANSCRIPT
Co-funded by the European Union
Semantic CMS Community
Designing Semantic CMS – Part I
Copyright IKS Consortium1
LecturerOrganization
Date of presentation
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Introduction of Content Management
Foundations of Semantic Web Technologies
Storing and Accessing Semantic Data
Knowledge Interaction and Presentation
Knowledge Representation and Reasoning
Semantic Lifting
Designing Interactive Ubiquitous IS
Requirements Engineering for Semantic CMS
Designing Semantic CMS
Semantifying your CMS
Part I: Foundations
Part II: Semantic Content Management
Part III: Methodologies
(2) (1)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
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What is this Lecture about?
We have seen ... ... how requirements for
semantic content management are defined in a systematic way.
... a list of industry needs.
What is missing? An efficient way to design an
architecture for a semantic CMS that meets the defined requirements
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Designing Interactive Ubiquitous IS
Requirements Engineering for Semantic CMS
Designing Semantic CMS
Semantifying your CMS
Part III: Methodologies
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(8)
(9)
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How to design a semantic CMS?
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Conceptual Reference
Architecture
Technical Architectural
Style
Part 1IKS Reference
Architecture
Part 2REST Architecture
What does the architecture of a
semantic CMS look like?
How can a semantic CMS be realized?
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Towards Semantic Content Management
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extract knowledgefrom content
SemanticContent Management
Content Content Knowledge
ContentManagement
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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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Extend traditional CMS
architecture with required
semantic capabilities
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What are semantic CMS?
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A Semantic CMS is a CMS with the capability of
interacting withsemantic metadata,
extractingsemantic metadata,
managingsemantic metadata,
and storingsemantic metadata
about content.
Knowledge Representation andReasoning Layer
Persistence Layer
Semantic Lifting Layer
Presentation and Interaction Layer
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Traditional CMS Architecturefor Content
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User Interface
Content Management
Content Data Model
Content Repository
Content
Adm
inistration
Content Access
Persistence Layer
Business Logic Layer
Presentation Layer
Data Representation Layer
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Reference Architecture for Semantic CMS
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Semantic User Interaction
Reasoning
Knowledge Models
Knowledge Repository
Know
ledgeA
dministration
Knowledge Access
KnowledgeExtraction Pipelines
Knowledge Representation and Reasoning Layer
Persistence Layer
Semantic Lifting Layer
Presentation & Interaction 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
interact with the car manufacturer object and
see, e.g. the location of its headquarter.
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Semantic User Interaction
Reasoning
Knowledge Models
Knowledge Repository
Know
ledgeA
dministration
Knowledge Access
KnowledgeExtraction Pipelines
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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.
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Semantic User Interaction
Reasoning
Knowledge Models
Knowledge Repository
Know
ledgeA
dministration
Knowledge Access
KnowledgeExtraction Pipelines
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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
multistage process [FL04] by applying
different IE/IR algorithms
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Semantic User Interaction
Reasoning
Knowledge Models
Knowledge Repository
Know
ledgeA
dministration
Knowledge Access
KnowledgeExtraction Pipelines
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Pipeline Processing - Example
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ContentExtraction
Pre-Processing
EntityExtraction
RelationExtraction
John Miller has brought a Jaguar car this year.
Person Car Manufacturer
Time
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.
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Semantic User Interaction
Reasoning
Knowledge Models
Knowledge Repository
Know
ledgeA
dministration
Knowledge Access
KnowledgeExtraction Pipelines
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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.
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Semantic User Interaction
Reasoning
Knowledge Models
Knowledge Repository
Know
ledgeA
dministration
Knowledge Access
KnowledgeExtraction Pipelines
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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
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Semantic User Interaction
Reasoning
Knowledge Models
Knowledge Repository
Know
ledgeA
dministration
Knowledge Access
KnowledgeExtraction Pipelines
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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.
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Semantic User Interaction
Reasoning
Knowledge Models
Knowledge Repository
Know
ledgeA
dministration
Knowledge Access
KnowledgeExtraction Pipelines
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Integration
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User Interface
ContentManagement
Content Data Model
Content Repository
Content
Adm
inistration
Content Access
Semantic User Interface
Semantic User Interaction
Reasoning
Knowledge Models
Knowledge Repository
Know
ledgeA
dministration
Knowledge Access
KnowledgeExtraction Pipelines
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Implementation of the Reference Architecture
Reference implementation withinthe IKS project IKS: An open source community to
bring semantic technologies to CMS
platforms New incubating project at the
Apache Software Foundationhttp://incubator.apache.org/stanbol
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Implementation of the Reference Architecture
One year student projectInformation-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
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User Interface
ContentManagement
Content Data Model
Content Repository
Co
nte
nt
Ad
min
istratio
n
Content Access
Semantic User Interaction
Reasoning
Knowledge Models
Knowledge Repository
Kn
ow
led
ge
Ad
min
istratio
n
Knowledge Access
KnowledgeExtraction Pipelines
Semantic User Interface
Instantiation Content Management
ID|SE Platform
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Analysis &
Design
Implementation &
Test
Requirements
Engineering
Problem Statement
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?
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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
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http://idse.cs.upb.de:8082/opencms/opencms/idse
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ID|SE-Platform – Architecture
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Document-Content-Storage
ID|SE-Service-Platform
IE/IR-Service-Orchestrators
Meta-Data-
Search
Content-Management
IE/IR-ServicesEvaluation-
Services
Meta-Data-Storage
Meta-Data-Model
<<OpenCMS>>
Content-Management-
System
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Mapping with Reference Architecture
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ID|SE-Platform1. Send Request to the ID|SE Platform
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<<OpenCMS>>Content Management
SystemID|SE-Service Platform
IEIR-ServiceOrchestrators
Webservice
DefaultMetaDataCreator
<<OpenCMS-Module>>GUI
IDefaultMetaDataCreator
DefaultMetaDataCreatorWebservice
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ID|SE-Platform2. Providing Documents
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<<OpenCMS>>Content Management
System
ID|SE-Service Platform
IEIR-ServiceOrchestrators
DefaultMetaDataCreator
<<component>>DocumentProvider
Content-Management
DocumentContent-Storage
OpenCMSDocumentProviderProxy
IProvideDocuments
Webservice
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ID|SE-Platform3. Generation of Meta-Data
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IE/IR-ServicesEvaluationServices
MetaDataStorage
MetaDataModel
IE/IR-ServiceOrchestrators
DefaultMetaDataCreator
Content-Extrac-
tion
Pre-pro-
cessors
Classi-fier
ClustererNamed-Entity-
Recognizer
Information-Aggregator
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ID|SE-Platform4. Providing/Presenting Meta-Data
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<<OpenCMS>>Content Management
System
Webservice
<<OpenCMS-Module>>ArtifactSearchGUI
Meta-Data-Search
MetaDataModel
MetaDataStorage
IEIR-Services
MetaDataSearchEngine
MetaDataSearchEngineWebservice
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ID|SE Features
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Clustering of artefacts
Classification of artefacts
Named entity
recognition
Facetted Search
Efficient way in browsing through
content
“Which artefacts are about ‘XYZ’ ”
No redundancy in software specification
documents
Duplicate Check
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How can we
evaluate our
semantic
features?
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Evaluation Criteria
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Recall
Precision
F-Measure
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Evaluation of Semantic Features
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F-Measure Precision Recall0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
80%
88%
74%
Entity Recognition
F-Measure Precision Recall0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
77%
84%
72%
Classification
F-Measure Precision Recall0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
58%
64%
56%
Clustering
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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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