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
Prof. Dr. Sören Auer