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

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Presentation on theme: "Co-funded by the European Union Semantic CMS Community Designing Semantic CMS – Part I Copyright IKS Consortium 1 Lecturer Organization Date of presentation."— Presentation transcript:

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

2 Page: 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) (10)

3 Page: 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 Copyright IKS Consortium 3 Designing Interactive Ubiquitous IS Requirements Engineering for Semantic CMS Designing Semantic CMS Semantifying your CMS Part III: Methodologies (7) (8) (9) (10)

4 Page: How to design a semantic CMS? Copyright IKS Consortium 4 Conceptual Reference Architecture Technical Architectural Style Technical Architectural Style Part 1 IKS Reference Architecture Part 1 IKS Reference Architecture Part 2 REST Architecture Part 2 REST Architecture What does the architecture of a semantic CMS look like? How can a semantic CMS be realized?

5 Page: Copyright IKS Consortium 5

6 Page: Towards Semantic Content Management Copyright IKS Consortium 6 extract knowledge from content Semantic Content Management Content Knowledge Content Management

7 Page: 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 ... 7 Extend traditional CMS architecture with required semantic capabilities Copyright IKS Consortium

8 Page: What are semantic CMS? Copyright IKS Consortium 8 A Semantic CMS is a CMS with the capability of interacting with semantic metadata, extracting semantic metadata, managing semantic metadata, and storing semantic metadata about content. Knowledge Representation and Reasoning Layer Persistence LayerSemantic Lifting LayerPresentation and Interaction Layer

9 Page: Traditional CMS Architecture for Content Copyright IKS Consortium 9 User Interface Content Management Content Data Model Content Repository Content Administration Content Access Persistence Layer Business Logic Layer Presentation Layer Data Representation Layer

10 Page: Reference Architecture for Semantic CMS Copyright IKS Consortium 10 Semantic User Interaction Reasoning Knowledge Models Knowledge Repository Knowledge Administration Knowledge Access Knowledge Extraction Pipelines Knowledge Representation and Reasoning Layer Persistence Layer Semantic Lifting Layer Presentation & Interaction Layer

11 Page: 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. Copyright IKS Consortium 11 Semantic User Interaction Reasoning Knowledge Models Knowledge Repository Knowledge Administration Knowledge Access Knowledge Extraction Pipelines

12 Page: 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. Copyright IKS Consortium 12 Semantic User Interaction Reasoning Knowledge Models Knowledge Repository Knowledge Administration Knowledge Access Knowledge Extraction Pipelines

13 Page: 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 Copyright IKS Consortium 13 Semantic User Interaction Reasoning Knowledge Models Knowledge Repository Knowledge Administration Knowledge Access Knowledge Extraction Pipelines

14 Page: Pipeline Processing - Example Copyright IKS Consortium 14 Content Extraction Pre- Processing Entity Extraction Relation Extraction John Miller has brought a Jaguar car this year. Person Car Manufacturer Time Relation

15 Page: 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. Copyright IKS Consortium 15 Semantic User Interaction Reasoning Knowledge Models Knowledge Repository Knowledge Administration Knowledge Access Knowledge Extraction Pipelines

16 Page: 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. Copyright IKS Consortium 16 Semantic User Interaction Reasoning Knowledge Models Knowledge Repository Knowledge Administration Knowledge Access Knowledge Extraction Pipelines

17 Page: 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 Copyright IKS Consortium 17 Semantic User Interaction Reasoning Knowledge Models Knowledge Repository Knowledge Administration Knowledge Access Knowledge Extraction Pipelines

18 Page: 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. Copyright IKS Consortium 18 Semantic User Interaction Reasoning Knowledge Models Knowledge Repository Knowledge Administration Knowledge Access Knowledge Extraction Pipelines

19 Page: Integration Copyright IKS Consortium 19 User Interface Content Management Content Data Model Content Repository Content Administration Content Access Semantic User Interface Semantic User Interaction Reasoning Knowledge Models Knowledge Repository Knowledge Administration Knowledge Access Knowledge Extraction Pipelines

20 Page: 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 Copyright IKS Consortium 20

21 Page: Implementation of the Reference Architecture  One year student project Information-Driven Software Engineering  Extract knowledge from unstructured software specification documents  Case study: pages specification of German Health Card system Copyright IKS Consortium 21

22 Page: Breathing life to the Reference Architecture Copyright IKS Consortium 22 Instantiation Content Management ID|SE Platform

23 Page: Analysis & Design Implementation & Test Requirements Engineering Problem Statement 23 ? Copyright IKS Consortium

24 Page: 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 24 Copyright IKS Consortium

25 Page: ID|SE Demo Copyright IKS Consortium 25

26 Page: ID|SE-Platform – Architecture Copyright IKS Consortium 26 Document-Content- Storage ID|SE-Service-Platform IE/IR-Service-Orchestrators Meta- Data- Search Content- Management IE/IR-Services Evaluation- Services Meta-Data-Storage Meta-Data-Model > Content- Management- System

27 Page: Mapping with Reference Architecture Copyright IKS Consortium 27

28 Page: ID|SE-Platform 1. Send Request to the ID|SE Platform Copyright IKS Consortium 28 > Content Management System ID|SE-Service Platform IEIR-ServiceOrchestrators Webservice DefaultMetaDataCreator > GUI IDefaultMetaDataCreator DefaultMetaDataCreator Webservice

29 Page: ID|SE-Platform 2. Providing Documents Copyright IKS Consortium 29 > Content Management System ID|SE-Service Platform IEIR-ServiceOrchestrators DefaultMetaData Creator > DocumentProvider Content-Management DocumentContent- Storage OpenCMSDocument ProviderProxy IProvideDocuments Webservice

30 Page: ID|SE-Platform 3. Generation of Meta-Data Copyright IKS Consortium 30 IE/IR-Services Evaluation Services MetaDataStorage MetaDataModel IE/IR-ServiceOrchestrators DefaultMetaDataCreator Content- Extrac- tion Pre- pro- cessors Classi- fier Clusterer Named- Entity- Recognizer Information- Aggregator

31 Page: ID|SE-Platform 4. Providing/Presenting Meta-Data Copyright IKS Consortium 31 > Content Management System Webservice > ArtifactSearchGUI Meta-Data-Search MetaDataModel MetaDataStorage IEIR-Services MetaDataSearchEngine Webservice

32 Page: ID|SE Features Copyright IKS Consortium 32 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

33 Page: Copyright IKS Consortium 33 How can we evaluate our semantic features?

34 Page: Evaluation Criteria Copyright IKS Consortium 34 Recall Precision F-Measure

35 Page: Evaluation of Semantic Features Copyright IKS Consortium 35

36 Page: 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 Copyright IKS Consortium 36


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