Prof. Dr. Wolfgang Ziegler. Hochschule Karlsruhe, Kommunikation und Medienmanagement Institute for Information and Content Management I4ICM

Transcription

1 Prof. Dr. Wolfgang Ziegler Hochschule Karlsruhe, Kommunikation und Medienmanagement Institute for Information and Content Management I4ICM

2 o Theoretical Physics (Ph.D.), Würzburg Univ., Germany o 1997 Tech. Doc Services (CMS Consultant, Developer XML/XSLT) o 2001 Central Documentation Services (LIEBHERR) o 2003 Professor of Information & Content Management (Karlsruhe Univ. Appl. Sciences) (Meta-)Data, Information & Process Modelling for CMS, XML Processing & Publishing, CMS Evaluation & Introduction, Content Automation & Delivery o Independent CMS & CDP consultant I4ICM Institute for Information and Content Management (I4ICM) (Methodologies: REx, PI-Class, PI-Mod, Content-Delivery, CoReAn)

3 Various Aspects of Intelligent Systems

4 Intelligence helps! o to systemize o to automize o to understand? Prof. Dr. Ziegler

5 CMS principle: Controlled reuse of content modules (topics) in multiple documents or media by the use of metadata CMS offer solutions for o o o o variant management version management translation management cross media publishing management CDP principle: Dynamic content delivery for electronic (web) media supported by metadata driven search functionalities Prof. Dr. Ziegler

6 Systems offering web based access to modular, aggregated content or other information for various user groups by related retrieval mechanisms. Basic functionalities Access or import content from relevant data sources and corresponding systems Manage and update content within the content lifecycle Retrieval functionalities including user interfaces for content searching by users. Web-based display of content on a modular or document based level Web services handling requests from other applications and events. Prof. Dr. Ziegler

7 Mobile devices and corresponding apps used in private spheres, and that are consequently being demanded also in business use Search driven information access from retrieval applications which includes facetted and filtered searching for consumer goods Virtual reality and augmented reality enriching visual environments by additional information Machine translation and its growing acceptance in private and business contexts Artificial intelligence technologies which allow the automated extraction of decision-relevant information and knowledge from various data sources Big data technologies for analyzing mass data generated for example from customer behavior in sales processes or from the tracking of machine states IoT (Internet of Things) and Industry 4.0 initiatives which aim to control and react to the real-time behavior of products. Prof. Dr. Ziegler

8 Requirement for Standardized Exchange Format Off Site Portal/Mobile CMS, CDP CMS CMS Supplier Delivery xms xms xms Additional Information & Sources Intelligent Product I4.0/IoT-ready On Site CDP Machine state (errors, messages, operating conditions) User Information Service Information Machine State

9 Intelligent Content Methodologies, technologies, systems which allow to automize (or at least systemize) content processes. Processes cover content aggregation, content delivery, (retrieval, search, request) and quality assurance Content Intelligence Methodologies, technologies which allow to track and check content and content related processes. Processes cover content creation and metadata use, content reuse in CMS and content use in CDP (overlap with artificial intelligence) Prof. Dr. Ziegler

10 Native Intelligence Semantic content and semantic metadata for process automization Augmented Intelligence Additional relations between content objects described e.g. by ontologies Artificial Intelligence Automated extraction of metadata and knowledge by statistical methods Prof. Dr. Ziegler

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12 Purpose of native intelligence is to automize processes within CMS or to access information stored in CDP Consists of o Semantic information modeling Standards: DITA, DocBook, S1000D, PI-Mod Custom Structures: CMS-dependant Technologies: XML, (DTP) o Semantic metadata Standards: Dublin Core, S1000D, ispec2200, RDS-PP, eclass, iirds Technologies: RDF, Turtle, Notation 3, JSON, Methodologies: PI-Classification, Prof. Dr. Ziegler

13 Physical Objects (Product Components) Content Objects (Modular Topics) Content Content Operation Information Classes Dismount Content Repair

14 Prof. Dr. Ziegler The basic dimensions of (semantic) meta data for modular Product-Class content topics Information-Class Component Information Type Content Product Information Product Extended Metadata (Variant Features/Properties & Functional) Prof. Dr. Ziegler

15 pi-fan.de Product-Class Base/ Telescopic Rod X3B, X3-H1, X5-B, X5-D, Information-Class Operation/ Height Adjustment User Manual, Service Manual, Prof. Dr. Ziegler

16 pi-fan.de Taxonomy of (intrinsic) Product Component Classes Analogous procedure of component-based decomposition and classification of software products: software components software classes/objects GUI components programming units EN Translation provided by RWS Group, Germany

17 pi-fan.de Taxonomy of (intrinsic) Information Classes EN Translation provided by RWS Group, Germany

18 pi-fan.de Taxonomy of (extrinsic) Product Classes EN Translation provided by RWS Group, Germany

19 Heating Rotor Display X3B T3B Multidimensional Information Space Content Topics Safety Functional Description Repair User Manual Service Manual Prof. Dr. Ziegler

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21 Intrinsic Taxonomies Heating Multidimensional Information Space Safety Intrinsic Taxonomies Functional Description Rotor Repair Display Extrinsic Hierarchies Content Topic Extrinsic Hierarchies X3B User Manual T3B Service Manual Variant properties Functional Metadata Hierarchies, Taxonomies, List,

22 Mutidimensional Information Space Content Topic Variant properties Functional Metadata (Collections) Tools Time Spare Parts Maint Intervals Error Codes

23 Multidimensional Information Space Content Topic Variant Properties/ Features Location Parts No Geometry Material Features Functional Metadata (Collections)

24 Product features included or in addition to (extrinsic) product classes / names Example PI-Fan: Type TP-DH2 (Combination type) o Table Fan (T) o Continuous Switch (P) o Display (D) o Heating (H) o 2-Level Heating (2) Goal: Facilitate planning of new variants/configurations and metadata handling in CMS Prof. Dr. Ziegler

25 Extrinsic Classification as Variant Property TAB, TB5, T445,TX5B, TPB,TAMP,. Einstellen der Gebläsestärke T7B,TFX,.. Die Stärke des Gebläses kann in fünf Stufen eingestellt werden. Die Stärke des Gebläses kann stufenlos eingestellt werden. Die Stärke des Gebläses kann in sieben Stufen eingestellt werden. Drehen Sie den Drehregler (Abb. 23) bis die gewünschte Stärke erreicht ist. Die Stärke des Gebläses kann ebenfalls an der Fernsteuerung eingestellt werden (s. Abschnitt 5.4 Fernsteuerung). Prof. Dr. Ziegler

26 Features as Variant Property Stufe=5 Stufe=Cont Einstellen der Gebläsestärke Stufe=7 Die Stärke des Gebläses kann in fünf Stufen eingestellt werden. Die Stärke des Gebläses kann stufenlos eingestellt werden. Die Stärke des Gebläses kann in sieben Stufen eingestellt werden. Drehen Sie den Drehregler (Abb. 23) bis die gewünschte Stärke erreicht ist. Die Stärke des Gebläses kann ebenfalls an der Fernsteuerung eingestellt werden (s. Abschnitt 5.4 Fernsteuerung). Prof. Dr. Ziegler

27 CMS CDP

28 Direct Search Structured Search Facets Navigation Cleaning the rotor Procedures Mounting the rotor All Components X-Series Docufy Topic Pilot [ Prof. Dr. Ziegler

29 Schema Content Delivery Server [ Prof. Dr. Ziegler

30 [ Schema Content Delivery Server Prof. Dr. Ziegler

31 Prof. Dr. Ziegler Empolis Content Express [

32 Prof. Dr. Ziegler Practice innovation IDS c-rex.net [

33 Taxonomies and hierarchies are two-dimensional descriptions of a dreedimensional world Prof. Dr. Ziegler

34 Intrinsic Taxonomies Heating Mutidimensional Information Space Safety Intrinsic Taxonomies Functional Description Rotor Repair Display Extrinsic Hierarchies Content Topic Extrinsic Hierarchies X3B T3B User Manual Service Manual Variant Features/ Properties Hierarchies, Taxonomies, List, Functional Metadata

35 Multi occurences of product components at different locations (in taxonomy) Relations between product components; Depencies of topics on combinations of components Dependencies of additional variant properties on product components Dependencies of information types on other taxonomic values Prof. Dr. Ziegler

36 Prof. Dr. Ziegler

37 Purpose of Augmented Intelligence is to model the complexity of real world products and information Overcome typical shortcomings of the taxonomic modelling of metadata Introduce model of objects, their properties and (conditional) relations between each other Prof. Dr. Ziegler

38 Intrinsic Taxonomies Heating Mutidimensional Information Space Safety Intrinsic Taxonomies Functional Description Rotor Repair Display Extrinsic Hierarchies Content Topic Extrinsic Hierarchies X3B T3B User Manual Service Manual Variant Features/ Properties Hierarchies, Taxonomies, List, Functional Metadata

39 CMS CDP

40 CMS CDP

41 CMS CDP

42 Source: Ontolis Prof. Dr. Ziegler

43 Source: Ontolis Prof. Dr. Ziegler

44 CMS CDP

45 Prof. Dr. Ziegler Source: Coreon

46 Source: Coreon

47 Prof. Dr. Ziegler Source: Coreon

48 CMS CDP

49 Prof. Dr. Ziegler Source: Intelligent views / K-infinity

50 CMS CDP

51 Prof. Dr. Ziegler

52 Purpose of AI is the extraction of knowledge from (large/big) data and content sources Assigning content automatically to a given ontology or taxonomy Scenarios (in TC and within context of CMS/CDP) o Migration of legacy data (e.g., between CMS) o Structured access to unstructured content o Quality Control (classification, duplicates) o Prof. Dr. Ziegler

53 Algorithm (Weighting) Training Classification Training data (pre-classified topics) Feature Extraction Model Test data/topics (unclassified) Prediction/ Classification Quelle: Jan Oevermann (2016) Intelligente Klassifizierung von technischen Inhalten Automatisierung und Anwendungspotenziale. In: Tagungsband zur tekom Jahrestagung 2016, tcworld : Stuttgart

54 Quelle: Jan Oevermann (2016): Reconstructing Semantic Structures in Technical Documentation with Vector Space Classification. In: Proceedings of the Posters and Demos Track of the 12 th International Conference on Semantic Systems. CEUR Workshop Proceedings Prof. Dr. Ziegler

55 Source: Sebastian Bader / Jan Oevermann (2017): Semantic Annotation of Heterogeneous Data Sources: Towards an Integrated Information Framework for Service Technicians. (to appear) Prof. Dr. Ziegler

56 Prof. Dr. Ziegler

57 Content Control o Controlled language checker CLC o Terminology checking o Similaritiy analysis (content duplicates) in CMS Reuse tracking in CMS: Report Exchange (REx) Metrics Use tracking in CDP: Content Relevance Analytics (CoReAn) Prof. Dr. Ziegler

58 Duplicates Variants J. Oevermann; fastclass.de Similarity

59 Business Intelligence (REx) Metrics: Reuse Rates (Abundancy) Redundancy Document Sharing factor Variant management Correlations; Distributions Artificial Intelligence CMS KPI Delivery & Feedback KPI CDP Web Analytics (CoReAn) Indirect feedback Metrics: visiting time, Visit frequency search behaviour search terms Direct feedback Rating Satisfaction Quality assurance: Similarity analysis Classification quality Improve: Product Information Terminology (Harvesting)

60 Prof. Dr. Ziegler

61 Content is managed in CMS by Native Intelligence, and in many other data sources. Artificial Intelligence and Content Intelligence (REx, ) can ensure content quality, variant management and reuse efficiency in CMS Augmented Intelligence will/might drive the interaction between product and information development Industry 4.0 / IoT concepts rely on precise identification of parts and corresponding information delivered by CDP: Native Intelligence of data is required to ensure retrieval precision Artificial Intelligence can ensure data/metadata quality Manual search & retrieval processes in CDP rely on most complete network of information Augmented intelligence reveales network and related content Artificial Intelligence can assign structured and unstructured content (documents and parts of them) automatically to the information network to reduce manual effort Prof. Dr. Ziegler

62 Depends on the use case and level of digitization The Digitization Cascade The Intelligence Cascade o Do Native Intelligence o Let Guide Augmented Intelligence o Let Do Artificial Intelligence Smart Examples LifeSciences, Navigation, Consumer,, Documentation/Information) Prof. Dr. Ziegler

63 Ziegler, W. (2017): Metadaten für intelligenten Content In: tekom Schriftenreihe Band 22 Prof. Dr. Ziegler