Introduction to Ontologies

Transcription

1 The European Materials Modelling Council Introduction to Ontologies From a list of keywords to Taxonomies, Ontologies, and Materials Informatics Brussels,

2 The EMMO round table Emanuele Ghedini Gerhard Goldbeck Georg J. Schmitz Adham Hashibon Jesper Friis and everyone here and the rest of Costas A. Charitidis

3 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, interoperability Hierarchies, relationships and annotation Search patterns can be stored, share, reused Reasoning answers to what-if, if-then questions Accessible to Artificial Intelligence

4 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 Artificial Intelligence Semantic Web Systems Engineering Biomedical Informatics Library Science Enterprise Bookmarking Information Architecture All these fields create Ontologies to limit complexity and organize information. The Ontology can then be applied to problem solving.

5 Keywords, Categories, Taxonomy and Ontology Cow Chicken Keywords:define a value Taxonomy:a hierarchy consisting of terms denoting types linked by subtype relations Birds: Duck Goose Chicken Mammal Cow Animals: Cow Horse Goat is_not is_food_of eats Category:a class or division of things regarded as having particular shared characteristics Plant Gras Ontology:a set of concepts and categories in a subject area or domain that shows their properties and the relations between them.

6 EMMC What s the difference between an ontology and a taxonomy? TAXONOMY Like a tree with branches Parent Child relation, Generally limited to a specific subject area Hierarchy of (simple) concepts ONTOLOGY Like a spiderweb Manifold of relations, adds non relations Not limited to a specific subject area Complex relations with complex concepts

7 It starts with a taxonomy EMMC Scientific classification Kingdom: Animalia Phylum: Chordata Class: Mammalia Order: Perissodactyl Most general Family: Equidae Genus: Equus Subgenus: Hippotigris and Dolichohippus Linnaean taxonomy Species Equus zebra Equus quagga Equus grevyi Specific Taxonomy: branch of science concerned with classification

8 Let s add some relations An ontology comprises taxonomies AND provides relationships between the concepts Continent Herbivore Grass eats Africa is_part_of Kenia lives_in Genus: Equus Subgenus: Hippotigrisand Dolichohippus Equus zebra Equus quagga Equus grevyi Species

9 An example in Characterisation Ontology Material belongs_to Analysis Process Light absorption has_output sample is_input_for is_input_for FTIR Interferogram has_output has_output is_input_for sample Raw data file Fourier Transform Provided by Costas A. Charitidis

10 and special Relationships A simplified Approach Provided by Costas A. Charitidis Materials Entity Example: MethodTEM of the ClassMicroscopy characterizes Carbon Nanotubes of the Class Nanomaterials. CNTs have Tensile Strength of 13GPa and require a protection layer (Prerequisite) to avoid deterioration. TEM has the limitationof <100 nm specimen thickness. Performance Entity Classes Materials Physical Properties Chemical Characterization Methods Method Classes of Techniques Requirements Entity Prerequisites Requirements Limitations

11 How does an Ontology work? An ontology can enable a (semantic) reasoner: software able to inferlogical consequencesfrom a set of asserted facts or axioms Herbivore eats Grass Equus zebra is a Hippotigris and Hippotigris is an Equus. An Equus eats Grass. Equus zebra eats Grass. Genus: Equus Subgenus: Hippotigrisand Dolichohippus Equus zebra Equus quagga Equus grevyi Species

12 How to build an Ontology? Methodology 1. Terminology definition. Establish a common Terminology (define concepts and vocabulary) for better communication and easier Implementation of Ontology 2. Classification of terms 3. Definition of classes 4. Identification of nodes, ensembles & assemblies.. (relationships between terms/classes/entities) 5. Well organized Datasets Challenges-Questions 1. What information to involve? 2. Which are our classes/entities/attributes? 3. Which other Ontologies must be involved? 4. How do objects connect with each other in the most efficient way? 5. How to build the Ontology? (OWL, other tools, programming languages) Provided by Costas A. Charitidis

13 How to build an Ontology? Knowledge Questions: Which wine characteristics should I consider when choosing a wine? Is Bordeaux a red or white wine? Does Cabernet Sauvignon go well with seafood? What is the best choice of wine for grilled meat? We need: information on various wine characteristics and wine types, vintage years, classifications of foods, recommended combinations of wine and food etc.

14 How to build an Ontology? Consider reusing existing ontologies Build a Taxonomy(terms and classes): wine, grape, winery, location, a wine s colour, body, flavour, types of food, Define the classesand the class hierarchy: white, red, rose; red = Merlot, Syrah, Montepulciano,

15 How to use an Ontology? Steak: savoury protein Requires as antidote a wine that is hearty, full, rich, has tannins E. Vysniauskas& L. Nemuraite; Information technology and control 35(3) 2006

16 How is an Ontology represented? An example upper level ontology entity schematic ontology domain ontology material object research equipment has_part technological product specimen object Characterization Method Spectroscopy Technique FTIR is_info_about has_info process condition (limitations) information entity content data file material identifier quality Light absorption Provided by Costas A. Charitidis

17 How is an Ontology represented? OWL (Web Ontology Language)

18 How is an Ontology represented Protégé is a free, open-source ontology editor Areasoneris a piece of software able to inferlogical consequencesfrom a set of asserted facts or axioms.

19 How is an Ontology represented? 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, Set Theory I. Horrocks, P.F. Patel-Schneider, and F. van Harmelen. J. of Web Semantics, 1(1):7-26, representation examples given serve a short demo, there are other way to represent an ontology.

20 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 are valid descriptions!

21 Speaking the same language Review of Materials Modelling (RoMM) VI 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 April 2018: The CEN (European Committee for Standardization) Workshop Agreement CWA Materials modelling terminology, classification and metadata The lingua franca of materials modelling

22 Metadata - Papers There were a lot of computational choices made when we tackled this how to preserve these? Each of these points has its own story Also how did we put the regression lines? The end-result often only a number!

23 Modelling-Data (MODA) MODEL User Case Solver Model Physics 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!

24 EMMO (European Materials Modelling Ontology) Attempted Outcome: bring to materials modelling same benefits that similar ontologies have brought to bio/chem-informatics; common ground for describing materials models and data Ongoing Effort: create an ontology for materials modelling; pave the road for semantic interoperability within the field of materials modelling Expected Benefits: avoid duplicating reference data, capture provenance, make information/complex relationships discoverable via reasoning, expose context, support data creation/publication/reuse, dimensionality reduction of large amounts of data

25 Where are we going? EMMC Processing Modelling World Continuum Mesoscopic Atomistic EMMO Electronic Interoperability and merge Characterization Manufacturing

26 Where are we going? and beyond! Interoperability on a large scale Modelling integrated in Business Decisions Digital Marketplaces

27 EMMC-CSA project has received funding from the European Union's Horizon 2020 research and innovation programme, under Grant Agreement No