TIB Transfer, 6. März 2018

Semantische

Datenvernetzung für

Digitalisierung & Industrie 4.0

© Fraunhofer

--- VERTRAULICH ---

Sören Auer 6

Sören Auer 7

Sören Auer 8

Sören Auer 9

Page 10

Machine Learning and Big Data

http://www.spacemachine.net/views/2016/3/datasets-over-algorithms

AI is not just the next hype after Big Data, Big Data is the reason why we have AI!

Page 11

Source: Gesellschaft für

Informatik

The Three “V” of Big Data - Variety often Neglected

Linked Data Principles

Addressing the neglected third V (Variety)

1. Use URIs to identify the “things” in your data

2. Use http:// URIs so people (and machines) can look them up on the web

3. When a URI is looked up, return a description of the thing in the W3C Resource Description Format (RDF)

4. Include links to related things

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

12

[1] Auer, Lehmann, Ngomo, Zaveri: Introduction to Linked Data and Its Lifecycle on the Web. Reasoning Web 2013

Page 13

1. Graph based RDF data model consisting of S-P-O statements (facts)

RDF & Linked Data in a Nutshell

TransferEvent dbpedia:Hannover

06.03.2018

TIB conf:organizes

conf:starts

conf:takesPlaceIn

2. Serialised as RDF Triples:

TIB conf:organizes TransferEvent .

TransferEvent conf:starts “2018-03-06”^^xsd:date .

TransferEvent conf:takesPlaceAt dbpedia:Hannover .

3. Publication under URL in Web, Intranet, Extranet

Subject Predicate Object

Page 14

Creating Knowledge Graphs with RDF

Linked Data

located in

label

industry

headquarters

full nameDHL

Post Tower

162.5 m

Bonn

Logistics Logistik

DHL International GmbH

height物流

label

Page 15

Page 16

Search Engine Optimization & Web-Commerce Schema.org used by >20% of Web sites Major search engines exploit semantic descriptions

Pharma, Lifesciences Mature, comprehensive vocabularies and ontologies Billions of disease, drug, clinical trial descriptions Digital Libraries Many established vocabularies (DublinCore, FRBR,

EDM) Millions of aggregated from thousands of memory

institutions in Europeana, German Digital Library

Emerging Knowledge Graphs & Data Spaces

Page 17

The Trinity of Semantic Integration

Knowledge Graphs

• Complex fabric of concepts

& relationships

• Focus on heterogenous,

multi-domain knowledge

representation

Data Spaces

• Community of

organizations agreeing on

standards for data access/

security/ semantics/

governance/ licenses

• Focus on data sharing &

exchange

Semantic Data Lakes

• Storage facility for

enterprise/research data

• Use Big Data (HDFS)

management

• Focus on scalable data

access

Use in a single organization Intra-organizational use

A flexible, generic platform for (Big) Data

Value Chain Deployment

BigDataEurope Integrator Platform

7-mars-18 www.big-data-europe.eu

Key actors

Integrator Platform Architecture

Stacks Open Source solutions (Free)

Dockerization

Facilitates integration and

deployment

Plug-and-play BD Platform

Key BDE additions

o Support layer: integrated UI

o Semantification layer

7-mars-18 www.big-data-europe.eu

Platform Architecture Support Layer

Init Daemon

GUIs

Monitor

App Layer

Traffic

Forecast Satellite Image Analysis

Platform Layer

Spark Flink Semantic Layer

Ontario SANSA Semagrow

Kafka

Real-time Stream Monitoring

...

...

Resource Management Layer (Swarm)

Hardware Layer

Premises Cloud (AWS, GCE, MS Azure, …)

Hadoop NOSQL Store Cassandra Elasticsearch ... RDF Store

Data Layer

© Fraunhofer

Industrial Data Space

Establishing Data Value Chains

Page 23

Industrial Data Space • initiative for secure, distributed (peer-to-peer) data sharing • Supported by mayor industrial (Telekom, SAP, Siemens, Huawei, PWC, Deloitte) and

research (Fraunhofer, TNO, Insight, VTT, L3S) players • Core technology:

Data Space Connector – Secure Web Server for Data

• Validated in first use cases incl. pharma logistics

• Based on pillars: 1. security 2. light-weight semantics 3. open architecture 4. roles/governance

• Initially strong focus on Industry 4.0, but now expansion on other domains

http://www.industrialdataspace.org

Example: Vocabulary-based Data-Integration

for Industry 4.0

Datenvernetzung für Industrie 4.0

Page 25

Semantic Models bridge between Shop &

Office Floor

Page 26

Semantic Administrative Shell &

Reference Architecture for Industry 4.0

(RAMI4.0)

Page 27

Industry 4.0 Example

Semantic Representation of Sensor Data

myd:m123245 rdf:type i40:SensorMeasurement .

myd:m123245 rdf:hasValue “27.9"^^i40:DegreeCelsius .

myd:m123245 i40:hasMeasureTime "2016-03-24T12:38:54:12Z"^^xsd:DateTime .

myd:m123245 i40:fromSensor myd:Sensor123 .

...

# ^ subject ^ predicate ^ object

Page 28

VoCol: Gemeinsame Erstellung von

Wissensgraph-Schemata („Vokabularen“) Hilfestellung für Fachexperten und Wissens-Ingenieure

Dr. Christoph Lange

■ Integration heterogener Datenquellen (Silos)

■ Basis: semantische Technologien (RDF, Linked Data)

■ Methode und integrierte Entwicklungsumgebung zur Vokabular-Entwicklung

■ Wissens-Ingenieure modellieren; Fachexperten prüfen

■ Ansatz unterstützt Daten als Wirtschaftsgut

■ http://vocol.iais.fraunhofer.de

Page 29

Digitalisierung bedeutet nicht (nur) die Realisierung innovativer

Anwendungsfälle, sondern die Basis zu schaffen, das neue unvorhergesehene

Anwendungsfälle in der Zukunft schnell realisiert werden können!

Hybrid AI – combination of smart data (knowledge graphs) and smart analytics

Distributed semantic technologies – knowledge representation using vocabularies,

ontologies

Knowlege Graphs, Semantic Data Lakes

Zusammenfassung, Technologien & Projekte

Answering Questions

using Web Data

https://de.linkedin.com/in/soerenauer

https://twitter.com/soerenauer

https://www.xing.com/profile/Soeren_Auer

http://www.researchgate.net/profile/Soeren_Auer

TIB & Leibniz University of Hannover

Soeren.Auer@tib.eu

Prof. Dr. Sören Auer