+ All Categories
Home > Documents > Introduction to Ontologies Part I · • “Big Data”, Statistics • Leads to knowledge •...

Introduction to Ontologies Part I · • “Big Data”, Statistics • Leads to knowledge •...

Date post: 31-Jul-2020
Category:
Upload: others
View: 0 times
Download: 0 times
Share this document with a friend
18
EMMC The European Materials Modelling Council Introduction to Ontologies Part I Alexandra Simperler On-line 29.4.2019 https://emmc.info/
Transcript
Page 1: Introduction to Ontologies Part I · • “Big Data”, Statistics • Leads to knowledge • Learns new things • Allows computer programs to automatically improve through experience

EMMC The European Materials Modelling Council

Introduction to Ontologies

Part I

Alexandra Simperler

On-line 29.4.2019

https://emmc.info/

Page 2: Introduction to Ontologies Part I · • “Big Data”, Statistics • Leads to knowledge • Learns new things • Allows computer programs to automatically improve through experience

EMMC The EMMO round table

Emanuele Ghedini(University of Bologna)

Gerhard Goldbeck(Goldbeck Consulting)

Adham Hashibon(Fraunhofer Institut)

Georg J. Schmitz(Access)

Jesper Friis(SINTEF)

Page 3: Introduction to Ontologies Part I · • “Big Data”, Statistics • Leads to knowledge • Learns new things • Allows computer programs to automatically improve through experience

EMMC Outline

• Taxonomy vs Ontology

• The value of ontologies

• Semantic Technologies

• Representation of Ontologies

Page 4: Introduction to Ontologies Part I · • “Big Data”, Statistics • Leads to knowledge • Learns new things • Allows computer programs to automatically improve through experience

EMMC

What’s the difference between an

ontology and a taxonomy?

TAXONOMY ONTOLOGY

• Like a tree with branches

• Parent – Child relation,

is_a

• Generally limited to a

specific subject area

• Hierarchy of (simple)

concepts

• Like a spiderweb

• Manifold of relations,

adds non is_a relations

• Not limited to a specific

subject area

• Complex relations with

complex concepts

Page 5: Introduction to Ontologies Part I · • “Big Data”, Statistics • Leads to knowledge • Learns new things • Allows computer programs to automatically improve through experience

EMMC The Value of Semantic Technologies

• Natural perspective of human communication

• Greater expressivity than a database

• Improved logical structure

• Knowledge layer is separated from data layer

• Flexibility, reusability, interoperabilityinteroperabilityinteroperabilityinteroperability

• Hierarchies, relationships and annotation

• Search patterns can be stored, share, reused

• Reasoning – answers to what-if, if-then questions

• Accessible to Artificial Intelligence

Page 6: Introduction to Ontologies Part I · • “Big Data”, Statistics • Leads to knowledge • Learns new things • Allows computer programs to automatically improve through experience

EMMC The Value of Ontology in the Materials Field

Materials Ontology will contribute to:

• High throughput experiments

• High throughput characterization

• Cost reduction

• Reliable results

• Standard operation procedures (SOPs)

• Design of materials with improved

characteristics

• Classification of techniques and

acceleration of results

• Uniform query interface

All these fields create

Ontologies to limit

complexity and

organize information.

The Ontology can then

be applied to problem

solving.

Artificial Intelligence

Semantic Web

Systems Engineering

Biomedical Informatics

Library Science

Enterprise Bookmarking

Information Architecture

Page 7: Introduction to Ontologies Part I · • “Big Data”, Statistics • Leads to knowledge • Learns new things • Allows computer programs to automatically improve through experience

EMMC Examples/Use of Ontologies

• Database integration

– Connected data!

– Discover new trends

– New materials candidates

• Easier Database queries

– Ontology organises data by domain knowledge: contrast to

database which is organised by IT need.

– Querying can be done by scientist using scripts!

• Integration of analytical processes and equipment

• Integration/collaboration/PLM in complex engineering

projects (e.g. ISO 15926 for Oil/Gas industry)

• Avoid misunderstanding about concepts Airbus 380

7

Takahashi, et al (2018). Redesigning the Materials and Catalysts Database Construction Process Using

Ontologies. J Chem Inf Mod 58, 1742.

Schott presentation,

EMMC Workshop,

Vienna 2019

Page 8: Introduction to Ontologies Part I · • “Big Data”, Statistics • Leads to knowledge • Learns new things • Allows computer programs to automatically improve through experience

EMMC

Semantic Spectrum of Knowledge

Organization Systems

Adapted from:Leo Obrst “The Ontology Spectrum”. Book section in of Roberto Poli, Michael Healy, Achilles Kameas “Theory and Applications of Ontology: Computer Applications”. Springer Netherlands, 17 Sep 2010.

Semantics and metadata allow a resource

to be understood by both humans and

machines � promote interoperability.

List glossary, catalogue ID

Thesaurus synonyms, association relations

Taxonomy formal hierarchy, RDFS

Ontology Logics, OWL

Informal hierarchy table of contents, xml

semantic interoperability

syntactic interoperability

Machine can

process

information

due to

compatible

syntax.

Machine can

interpret

information

and reason.

Page 9: Introduction to Ontologies Part I · • “Big Data”, Statistics • Leads to knowledge • Learns new things • Allows computer programs to automatically improve through experience

EMMC A flower, by any name?

Cowslip or cuy lippe,

herb peter, paigle,

peggle, key flower, key

of heaven, fairy cups,

petty mulleins, crewel,

buckles, palsywort,

plumrocks, ….

All these words for one

and the same thing …

Page 10: Introduction to Ontologies Part I · • “Big Data”, Statistics • Leads to knowledge • Learns new things • Allows computer programs to automatically improve through experience

EMMC “Set” means …?

All these meanings for one

and the same word …

Page 11: Introduction to Ontologies Part I · • “Big Data”, Statistics • Leads to knowledge • Learns new things • Allows computer programs to automatically improve through experience

EMMC Speaking the same language

Definitions of concepts and a harmonised language

Categorizes the models in an interpretable way

Together the physics or chemistry equations and

materials relations are called governing equations and

they form one model

Review of Materials Review of Materials Review of Materials Review of Materials

Modelling (RoMM) VIModelling (RoMM) VIModelling (RoMM) VIModelling (RoMM) VI

April 2018: The CEN (European Committee for

Standardization) Workshop Agreement CWA

17284 “Materials modelling – terminology,

classification and metadata”

The “lingua franca” of materials modellingThe “lingua franca” of materials modellingThe “lingua franca” of materials modellingThe “lingua franca” of materials modelling

Page 12: Introduction to Ontologies Part I · • “Big Data”, Statistics • Leads to knowledge • Learns new things • Allows computer programs to automatically improve through experience

EMMC Modelling-Data (MODA)

MODEL

User Case Model Physics

Solver Post Processing

Finding a common language and

formal approach how to log a

simulation project

At some point we

want a machine

to understand it.

This is where

Ontologies enter!

Page 13: Introduction to Ontologies Part I · • “Big Data”, Statistics • Leads to knowledge • Learns new things • Allows computer programs to automatically improve through experience

EMMC What is A ‘Semantic Knowledge System’?

• Semantics is the linguistic and philosophical study of

meaning

• Logics is the systematic study of the form of valid

inference

– Predicate:

• “Elasticity is a materials property”

– Subject Predicate Object:

• “Material has a materials property”

– First order logic

• “There exist materials with elasticity”

13

Page 14: Introduction to Ontologies Part I · • “Big Data”, Statistics • Leads to knowledge • Learns new things • Allows computer programs to automatically improve through experience

EMMC ML vs AI

Machine Learning

• “Big Data”, Statistics

• Leads to knowledge

• Learns new things

• Allows computer programs

to automatically improve

through experience

Artificial Intelligence

• Logics + Data/Statistics/ML

• Leads to intelligence

• Makes decisions

• Mimics the human brain

Page 15: Introduction to Ontologies Part I · • “Big Data”, Statistics • Leads to knowledge • Learns new things • Allows computer programs to automatically improve through experience

EMMC How is an Ontology represented?

OWL (Web

Ontology

Language)

Page 16: Introduction to Ontologies Part I · • “Big Data”, Statistics • Leads to knowledge • Learns new things • Allows computer programs to automatically improve through experience

EMMC How is an Ontology represented

Protégé is a free, open-source ontology editor

A reasoner is a piece

of software able to

infer logical

consequences from

a set of asserted

facts or axioms.

Page 17: Introduction to Ontologies Part I · • “Big Data”, Statistics • Leads to knowledge • Learns new things • Allows computer programs to automatically improve through experience

EMMC How is an Ontology represented?

representation examples ; there are other way to represent an ontology

OWL DL (description logic): maximum expressiveness without losing

computational completeness, decidability of reasoning systems.

includes restrictions such as type separation (a class can not also be an

individual or property, a property can not also be an individual or class, …

I. Horrocks, P.F. Patel-Schneider, and F. van Harmelen. J. of Web Semantics, 1(1):7-26, 2003.

Page 18: Introduction to Ontologies Part I · • “Big Data”, Statistics • Leads to knowledge • Learns new things • Allows computer programs to automatically improve through experience

EMMC

EMMC-CSA project has received funding from the European Union's Horizon 2020

research and innovation programme, under Grant Agreement No. 723867.


Recommended