Co-funded by the European Union Semantic CMS Community Reference Architecture for Semantic CMS Copyright IKS Consortium 1 Lecturer Organization Date of presentation

Page: Copyright IKS Consortium 2

Page: Towards Semantic Content Management Copyright IKS Consortium 3 extract knowledge from content Semantic Content Management Content Knowledge Content Management

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 ... 4 Extend traditional CMS architecture with required semantic capabilities Copyright IKS Consortium

Page: What are semantic CMS? Copyright IKS Consortium 5 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

Page: Traditional CMS Architecture for Content Copyright IKS Consortium 6 User Interface Content Management Content Data Model Content Repository Content Administration Content Access Persistence Layer Business Logic Layer Presentation Layer Data Representation Layer

Page: Reference Architecture for Semantic CMS Copyright IKS Consortium 7 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

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 8 Semantic User Interaction Reasoning Knowledge Models Knowledge Repository Knowledge Administration Knowledge Access Knowledge Extraction Pipelines

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 9 Semantic User Interaction Reasoning Knowledge Models Knowledge Repository Knowledge Administration Knowledge Access Knowledge Extraction Pipelines

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 10 Semantic User Interaction Reasoning Knowledge Models Knowledge Repository Knowledge Administration Knowledge Access Knowledge Extraction Pipelines

Page: Pipeline Processing - Example Copyright IKS Consortium 11 Content Extraction Pre- Processing Entity Extraction Relation Extraction John Miller has brought a Jaguar car this year. Person Car Manufacturer Time Relation

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 12 Semantic User Interaction Reasoning Knowledge Models Knowledge Repository Knowledge Administration Knowledge Access Knowledge Extraction Pipelines

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 13 Semantic User Interaction Reasoning Knowledge Models Knowledge Repository Knowledge Administration Knowledge Access Knowledge Extraction Pipelines

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 14 Semantic User Interaction Reasoning Knowledge Models Knowledge Repository Knowledge Administration Knowledge Access Knowledge Extraction Pipelines

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 15 Semantic User Interaction Reasoning Knowledge Models Knowledge Repository Knowledge Administration Knowledge Access Knowledge Extraction Pipelines

Page: Integration Copyright IKS Consortium 16 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

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 17

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 18

Page: Breathing life to the Reference Architecture Copyright IKS Consortium 19 Instantiation Content Management ID|SE Platform

Page: Analysis & Design Implementation & Test Requirements Engineering Problem Statement 20 ? Copyright IKS Consortium

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 21 Copyright IKS Consortium

Page: ID|SE Demo Copyright IKS Consortium 22

Page: ID|SE-Platform – Architecture Copyright IKS Consortium 23 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

Page: Mapping with Reference Architecture Copyright IKS Consortium 24

Page: ID|SE-Platform 1. Send Request to the ID|SE Platform Copyright IKS Consortium 25 > Content Management System ID|SE-Service Platform IEIR-ServiceOrchestrators Webservice DefaultMetaDataCreator > GUI IDefaultMetaDataCreator DefaultMetaDataCreator Webservice

Page: ID|SE-Platform 2. Providing Documents Copyright IKS Consortium 26 > Content Management System ID|SE-Service Platform IEIR-ServiceOrchestrators DefaultMetaData Creator > DocumentProvider Content-Management DocumentContent- Storage OpenCMSDocument ProviderProxy IProvideDocuments Webservice

Page: ID|SE-Platform 3. Generation of Meta-Data Copyright IKS Consortium 27 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

Page: ID|SE-Platform 4. Providing/Presenting Meta-Data Copyright IKS Consortium 28 > Content Management System Webservice > ArtifactSearchGUI Meta-Data-Search MetaDataModel MetaDataStorage IEIR-Services MetaDataSearchEngine Webservice

Page: ID|SE Features Copyright IKS Consortium 29 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

Page: Copyright IKS Consortium 30 How can we evaluate our semantic features?

Page: Evaluation Criteria Copyright IKS Consortium 31 Recall Precision F-Measure

Page: Evaluation of Semantic Features Copyright IKS Consortium 32

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 33