“Semantic Technologies for Smart Services”

KIT – The Research University in the Helmholtz Association www.kit.edu

Institute of Applied Informatics and Formal Description Methods (AIFB), Karlsruhe Service Research Institute (KSRI),FZI Research Center for Information Technology

Semantic Technologies for Smart ServicesRudi Studer & Maria Maleshkova

Cognitive Systems Institute Speaker Series, 15 December 2016

Institute of Applied Informatics and Formal Description Methods (AIFB)

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“Semantic Karlsruhe”

Industrie 4.0

Medicine & eHealth

Digital Shift

Big Data & Data Analytics

SEMANTIC TECHNOLOGIES

SemanticData Management

Complex Event Processing

Data / Text Mining

SmartServices

Basic Research

Applied Research

Transfer

Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series


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WEB SCIENCE AND KNOWLEDGE MANAGEMENT

Institute of Applied Informatics and Formal Description Methods


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Karlsruhe Service Research Institute – an „industry-on-campus“ model with focus on interdisciplinary research

Prof. Dr. Christof WeinhardtInformation & Market Engineering

Prof. Dr. Gerhard SatzgerDigital Service Innovation

Prof. Dr. Stefan Nickel

Discrete Optimization& Logistics

Prof. Dr. Wolf Fichtner

Energy Economics

Prof. Dr. Alexander MädcheInformation Systems & Service Design

Prof. Dr. Rudi Studer

Knowledge Management

Prof. Dr. York Sure-Vetter

Prof. Dr. Kai Furmans

Value Stream Services


Dr. Markus Bauer


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Service Research investigates complex service systems where economic value is created jointly by multiple independent parties, acting together efficiently through the systematic use of information and communication technologies…

…from different perspectives and in different domains

... and others

HealthcareServices

Crowd andParticipation

Services(e)-Mobility

Smart Services,

Industry 4.0 and IoT

Research Focus: Intelligent Services for Real-world Networks



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• Motivation• Why Smart Services via Semantic Technologies?• Use Case 1 - Building Agile Systems • Use Case 2 – Smart Services for Predictive

Maintenance• Summary and Conclusions



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Market Influence

Technology Development

Today’s Driving ForcesShorter innovation cycles Need for continuous adaptationNear real-time analysesInvolvement of the customer not only with the finished product/service but during the complete development cycle

Ubiquitous accessSocial and community WebHeterogeneous big dataDistributed component-based solutions



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Internet of Things (IoT) Challenges

We expect one hundred billion IoT devices to be deployed within the next ten years

BUT the IoT is currently facing a lot of problemsProduct silos that do not interoperate with each otherMany approaches and incompatible platformsNo network effect

Heterogeneity in terms ofData Devices and interfaces

Data volumes and number of sources explode


We expect one hundred billion IoT devices to be deployed within the next ten years

BUT the IoT is currently facing a lot of problemsProduct silos that do not interoperate with each otherMany approaches and incompatible platformsNo network effect

Heterogeneity in terms ofData Devices and interfaces

Data volumes and number of sources explode

see: http://www.w3.org/2015/05/wot-framework.pdf


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The Web as the Solution

Source: http://www.w3.org/2015/05/wot-framework.pdf



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Semantic Technologies for Smart Services

Data Integration – combining data from multiple sources enables new applications and insights

More and more data available on the Web is published conforming to Semantic Web standards

Linking Open Data (LOD) initiativeSemantic Web technologies are beneficial for data exchange, integration and search

Decentralised Architectures – no central controller or repositoryOvercoming device heterogeneity – common model for devices (functional and non-functional properties) Overcoming interface heterogeneity – standard Web Technologies + Linked Data

Adaptation – adjusting services, products, things according to context and current needs

Intelligent Programmable InterfacesEmbedding intelligence into the service interface (e.g. rules)



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Semantic Technologies

Semantic Web technologies, standardised by the W3C, aremature:

RDF recommendation in 1999, update in 2004RDFa (RDF in HTML) note in 2008RDFS recommendation in 2004SPARQL recommendation in 2008OWL recommendation in 2004, update in 2009

Linked Data is a subset of the Semantic Web stack



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Use Cases

1. Building Agile SystemsFast integration of data and programmable interfaces based on semantic technologies

2. Smart Services for Predictive MaintenanceSemantics for integrating sensor data, background knowledge and decision rulesRecognizing maintenance needs before they occur


Semanticdescription

Semanticdescription

Semanticdescription


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BUILDING AGILE SYSTEMS Semantics for integrating data and programmable interfaces



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Today‘s Web is about Dynamic Data

Data is often dynamically created as a result of some calculation carried out over input data (e.g., weather information)Data can change frequently (e.g., moving objects)APIs are used to trigger functionalities in the Web and the real world and provide access to dynamic and static data sources

An important role plays Representational State Transfer (REST)

Architectural style for client–server interactionCompatible with Web architecture

http://programmableweb.com

8816 APIs

Over 16,400 APIs and 7,800 mashups



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Linked Data Principles

1. Use URIs to name things; not only documents, but also people, locations, concepts, etc.

http://dbpedia.org/resource/Johannes_Gutenberg2. To enable agents (human users and machine agents alike) to look up those

names, use HTTP URIshttp://dbpedia.org/page/Printing_press

3. When someone looks up a URI we provide useful information; with 'useful' in the strict sense we usually mean structured data in RDF

http://dbpedia.org/page/Printing_pressdct:subject dbc:Johannes_Gutenberg.

4. Include links to other URIs allowing agents (machines and humans) to discover more things

<http://dbpedia.org/page/Printing_press> rdfs:seeAlso<http://dbpedia.org/page/Letterpress_printing> .

http://www.w3.org/DesignIssues/LinkedData


http://dbpedia.org/resource/Johannes_Gutenberg

http://dbpedia.org/page/Printing_press

http://dbpedia.org/page/Printing_press

http://www.w3.org/DesignIssues/LinkedData


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Linking Open Data Cloud

Linking Open Data cloud diagram 2014, by Max Schmachtenberg, Christian Bizer, Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/



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Motivation for Combining Semantics and Services

Increased value comes from combinations of services and APIs

But a lot of manual effort is required for this compositions (glue code)Structured service/API descriptions ease the composition process considerablySemantic descriptions allow for execution of several tasks automatically (e.g., data matching, discovery, ranking)

Manually drafted glue code


Semanticdescription

Semanticdescription

Semanticdescription


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Motivation for Combining Semantics and Services

Increased value comes from combinations of services and APIs

But a lot of manual effort is required for this compositions (glue code)Structured service/API descriptions ease the composition process considerablySemantic descriptions allow for execution of several tasks automatically (e.g., data matching, discovery, ranking)

Manually drafted glue code


Semanticdescription

Semanticdescription

Semanticdescription


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Creating Linked Services

Functionality attainable via the Web by combining:

RESTful services (respecting Web architecture)resource-orientedmanipulated with HTTP verbs

GET, PUT (, PATCH), POST, DELETENegotiate representations

Linked dataUniform use of URIsUse of RDF and SPARQL

= Linked Services



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Facilitate Data Integration

Linked ServiceCombines data (MashUp)

build on top

Applicationthat consumes one

Linked Service

Bad solution



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Linked ServiceCombines data (MashUp)

build on top

Applicationthat consumes one

Linked Service

Bad solution



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Linked Service

Application(integrates data and

functionalities from several Linked Services, e.g. via Linked

Data-Fu)

Good solution

Linked Service

Linked Service


http://linked-data-fu.github.io/


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SMART SERVICES FORPREDICTIVE MAINTENANCE

Semantics for integrating sensor data, background knowledge and decision rules



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Cognition Framework


Perception Reaction

Back

grou

nd

Know

ledg

e

Interpretation and Analysis


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The Cognition Framework for Predictive Maintenance

Input data in terms of- Sensor data- Personal observations- Alarms and errors

Background knowledge- Log files- Previous similar problems

and solutions- Guidelines- Manuals - Detail about the machines


Interpretation and Analysis- Data integration to enable

analysis- Similarity analysis with

previous problems - Heuristics encoded as rules

Reaction- Automated solution

recommendation vs.- Providing solution support


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Problem Breakdown

1. Smart Services for Problem Recognition Recognizing what the current problem is based on previous problemsCombination with heuristics

2. Smart Services for preparing Solution ContainersProviding summary of the problem, difficulty, time estimateLinks to relevant manuals, links to required partsRequired expertise, contacts of people with relevant qualifications


?

http://www.aifb.kit.edu/web/STEP/en


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Problem Breakdown

2. Smart Services for preparing Solution Containers (continued)Dealing with multilingual and multimodal sources

Identifying related articles across different languages and media types Possible use – the solution might be available in another language; images and videos can be used to identify the problem, support the solution

3. Smart Services for Interactive Problem Solving

Guiding the user towards the solutionRecommending the next possible step


http://xlime.eu/


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Problem Breakdown

4. Smart Services for Route Planning for the technician

Supporting the dispatcher in planning the routesSupporting the technician during the tripsSolution based on Use Case 1: Building Agile Systems

Creating Linked Services for the interfacesRules for defining the composition and interactionAutomated execution with Linked DataFu

Prototype system for data / service integration and execution


Proximity serviceStreet View

Maintenance route-planning


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Summary and Outlook

Market trends and technology developments pave the way for developing new products and services, which are more flexible and adapted to the customer needs

We need technology solutions to achieve more automation and adaptability –– putting the ‘Smartness’ into services

Providing means for agile system developmentProviding means for self-adaptivity

We can use Semantic Technologies for Smart Services to support:The rapid development of mashups and applicationsTo realize Industry 4.0 / IoT solutions



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Relevant PublicationsS. Stadtmüller, S. Speiser, A. Harth, R. StuderData-Fu: A Language and an Interpreter for Interaction with Read/Write Linked Data. Proceedings of the 22nd International Conference on World Wide Web, pp. 1225-1236, Rio de Janeiro, 2013.A. Harth, C. Knoblock, S. Stadtmüller, R. Studer, und P. Szekely. On-the-fly Integration of Static and Dynamic Sources. Proceedings of the ISWC Workshop on Consuming Linked Data. 2013: CEUR-WS.M. Maleshkova, P. Philipp, Y. Sure-Vetter, R. Studer. Smart Web Services (SmartWS) –The Future of Services on the Web. IPSI BgD Transactions on Advanced Research (TAR), 12 (1), pp. 15-26, January, 2016. T. Weller, M. Maleshkova, K. März, L. Maier-Hein. A RESTful Approach for Developing Medical Decision Support Systems. The Semantic Web: ESWC 2015 Satellite Events, pp. 376-384, Springer, 9341.T. Weller, M. Maleshkova. Cognitive Process - An Open-Source Tool to Capture Processes according to the Linked Data Principles. The Semantic Web: ESWC 2016 Satellite Events, Springer.L. Zhang, A. Rettinger, J. Zhang. A Knowledge Base Approach to Cross-Lingual Keyword Query Interpretation. The 15th International Semantic Web Conference (ISWC'16), Springer, Oktober, 2016


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