1. Clément Troprès - Damien Coppéré1 Semantic Web Based on: -The semantic web -Ontologies Come of Age

2. Clément Troprès - Damien Coppéré2 Plan Introduction to semantic web Kwnoledge Representation Ontologies Agents

3. Clément Troprès - Damien Coppéré3 1. Introduction to semantic web Today, most of the web contents is designed for human to read The actual web looks insufficient The semantic web purpose is to structure the world wide web

4. Clément Troprès - Damien Coppéré4 1. Introduction to semantic web Principles: 1. Each object of the web has a metadata 2. Each metadata is readable by agents and humans 3. Each metadata represents accurately an object 4. Each metadata is available in a common space, readable by agents and humans. The selection of the metadata makes the object avalaible as a resource

5. Clément Troprès - Damien Coppéré5 1. Introduction to semantic web The semantic web architecture

6. Clément Troprès - Damien Coppéré6 2. Knowledge representation (1): Technology which permits computers to access to structured collections of information System must have sets of inference rules that computers can use to conduct automated reasoning It has to be linked into a single global system

7. Clément Troprès - Damien Coppéré7 2. Knowledge representation (2) : Traditional systems usually : - Limit the questions that can be asked - Become unmanageable New systems, in contrast, accept paradoxes - Unanswerable questions are a price that must be paid to achieve versatility.

8. Clément Troprès - Damien Coppéré8 2. Knowledge representation (3) : Two important technologies exist : - EXtensible Markup Language (XML) - Resource Description Framework (RDF) XML : - Everyone can create their own tags - It allows to add arbitrary structure to the document

9. Clément Troprès - Damien Coppéré9 2. Knowledge representation (4) : RDF : - Encode in sets of triplets - Each triple being rather like the subject, predicate and object of an elementary sentence identified by URIs - Natural way to describe the vast majority of the data processed by machines - Example : New York has a postal abbreviation which is NY NY Universal Resource Identifier - Ensure that concepts are tied to a unique definition that everyone can find on the Web

10. Clément Troprès - Damien Coppéré10 3. Ontologies - Introduction Current web : It has grown and continues to grow very quickly Problems to find information you are really looking for Designed for human perception Semantic web: Make the web understandable by computers agent

11. Clément Troprès - Damien Coppéré11 3. Ontologies - Introduction How make the web semantic? - Complete HTML tag (with XML) - Organize the keywords in each document - Indexing all the resources of the web (RDF) - Ontologies

12. Clément Troprès - Damien Coppéré12 3. Ontologies - Introduction We are here

13. Clément Troprès - Damien Coppéré13 3. Ontologies - Introduction Definition: - In 1993, Gruber propose his definition (which is now the most cited in AI) : « An ontology is an explicit specification of a conceptualization ». (Gruber T., 1993b) - In 1997, Borst modified slightly the definition in order to highlight major aspects of this paradigm: « An ontology is a formal specification of a shared conceptualization ». (Borst W. N., 1997)

14. Clément Troprès - Damien Coppéré14 3. Ontologies - Introduction Definition: In 1998, these two definitions were only one in the definition of Studer. « An ontology is a formal, explicit specification of a shared conceptualization ». (Studer R. et al., 1998) - « Conceptualization » refers to an abstraction of a phenomenon obtained by identifying the concepts appropriate to this phenomenon - « Shared » means that ontology captures consensual knowledge

15. Clément Troprès - Damien Coppéré15 3. Ontologies - Introduction « Formal » means that ontology is interpretable by a machine (machinereadable) « explicit specification » means that the concepts of ontology and the constraints related to their use are defined in a declaratory way Ontology has the following characteristics : 1) shared, 2) explicit, 3) formal

16. Clément Troprès - Damien Coppéré16 3.Ontologies – Possible representation? A controlled vocabulary (eg: Catalogs) A glossary (list of terms) Thesauri (synonym relationship…, but not an explicit hierarchy) Term hierarchies (without true subclass) Strict subclass hierarchies Frames (classes include property information) Value restriction (eg: a price is a number) Logical deduction A B A is a superclass of B

17. Clément Troprès - Damien Coppéré17 3. Ontologies – Simple Ontologies Some of the ways that simple ontologies may be used in practice: - A controlled vocabulary (beginning of interoperability) - Site organization and navigation support - Expectation setting - Umbrella structures from which to extend content - Browsing support - Search support - Sense disambiguation support

18. Clément Troprès - Damien Coppéré18 3. Ontologies – Structural Ontologies - Consistency checking - Completion - Interoperability support - Support validation and verification testing - Encode entire test suites - Configuration support - Support structured, comparative and customized search - Exploit generalization/specialization information

19. Clément Troprès - Damien Coppéré19 3. Ontologies – Implications and Needs An ontology-based application has two major concerns: The language The environment

20. Clément Troprès - Damien Coppéré20 3. Ontologies – Implications and Needs (1) The language: Simple ontologie: It’s not a real problem (language with subclass and instance relationships) Structural ontologie: the language must be able to express the entire domain unambiguously (KRSS, KIF, OKBC)

21. Clément Troprès - Damien Coppéré21 3. Ontologies – Implications and Needs (2) Environment: Ontology tools are needed to analyze, modify and maintain an ontology over time Many are avalaible commercially

22. Clément Troprès - Damien Coppéré22 3. Ontologies – Implications and Needs (3) Environment – Criterias needed : - Collaboration and distributed workforce support (share session) - Platform interconnectivity (able to read and write compatible formats) - Scale (In terms of size of ontologies, number of simultaneous users) - Versioning (Able to support many versions of ontology)

23. Clément Troprès - Damien Coppéré23 3. Ontologies – Implications and Needs (4) Environment – Major criteria of performance : - Security - Analysis (focus the user’s attention in areas which need modification) - Lifecyle issues (Support for ontology evolution issues) - Ease of use (training materials, tutorials…) - Diverse user support - Presentation style - Extensibility (Adapt along with the needs)

24. Clément Troprès - Damien Coppéré24 4. Agents Representing by programs : - Collect Web content from diverse sources - Process the information - Exchange the results with other programs All agents can work together

25. Clément Troprès - Damien Coppéré25 4. Agents (2) Important facets : - "Proofs" written in the Semantic Web's unifying language (Proof Markup Language PML) - Digital signatures used to verify that the attached information has been provided by a specific trusted source Example of agent : You answer your phone and the stereo sound which was working is turned down.

26. Clément Troprès - Damien Coppéré26 4. Agents (3) You want to buy a car … An intelligent Agent is going to find your new car - How ? It looks for all cars which corespond to your criterias - Which criteria ? Prices, delivery period, colour… - Where ? On web documents described by semantic standards (proofs, digital signature…) Travel Agency…

27. Clément Troprès - Damien Coppéré27 - L ets anyone express new concepts with minimal effort - Unifies a logical language The Semantic Web