1. The Semantic Web Week 13 Module Website: http://scom.hud.ac.uk/scomtlm/chs2533 Lecture: Knowledge Acquisition / Engineering Practical: Getting to know Protégé-2000

2. New Rough, Draft Schedule for this term n Week 13 – 15 Lecture: Knowledge Engineering, Domain Modelling; Practical: Building Ontologies with Protégé/Owl n Week 16 -18 Knowledge Engineering ‘Project’ – a REAL domain modelling example n Week 19 - 21 lecture: Intelligent internet agents – u basics. types of agent - multi agents, mobile agents, information agents u reasoning+ planning u adaptation+ learning n Week 22 --> 24 Semantic web services: automated reasoning with web pages; Semantic mark-up for web services: service description languages eg DAML-S and OWL-S ; Automated service composition and service discovery;

3. New View of SW Module XML,RDF,OWL etc – technological foundation of the Semantic Web Ontology definition, Description Logics, First Order logic – logical foundations of the Semantic Web Knowledge Engineering – formalising and modelling knowledge (in particular for use on the Web) Agents, Intelligent Web Services – Applications of the Semantic Web TERM 1 TERM 2

4. Knowledge Acquisition n KA is a huge area of computer science that underlies and drives the activities of the semantic web. n As a rough definition KA = extracting and identifying all the knowledge needed for a particular application area, then formulating it into a knowledge base, and validating the formulation. n Using a knowledge base to create a precise ‘model’ of an application and using it to make predictions of the application is sometimes called `Domain Modelling’. An ontology is a kind of domain model.

5. Some Terminology n Domain is the application area n Domain model is an abstracted, formal model (theory) of the application area n Acquisition is the process of producing a domain model of the application area n Modelling is the process of using the domain model to predict behaviour in the domain

6. Knowledge Acquisition An Ontology (like a knowledge base) forms a simple domain model and requires a KA process to build it …. Reality Conceptualisation C subset of X u Y D&Y => Z …… Ontology (a kind of Domain Model) X Y Interpretation Knowledge Acquisition

7. Knowledge Engineering n KE = KA + manipulation, refinement, maintenance, re-use etc of a knowledge base (ontology). n The area of KE comes from 30+ years experience of trying to create Expert Systems n 'knowledge transfer', = extracting rule knowledge from experts and directly encoding it within an expert system 'shell‘ KE matured when it was realised that knowledge transfer was a bad idea! Application expertise Knowledge transfer Procedural expert knowledge Expert System Engine

8. Knowledge Engineering n Now KBS emphasises the building of a deep causal model prior to an operatational system. This domain model has to embody not just the procedural expert knowledge but the environment in which this knowledge was utilised. n Several 'modelling frameworks' have been developed to support the process of knowledge acquisition and validation, and are underpinned by an overall method of development with supporting tools. CommonKads is the most famous: it advocates the use of a series of models during domain capture, each dealing with different aspects of the domain. n Protégé is a long established tools environment for helping in Knowledge Acquisition we will see in the practical. Protégé-2000 helps a user build ontologies.

9. Rationale/advantages behind KBS n Early expert systems (1970’s) introduced the ideas of: u Separating Domain Knowledge from the way that knowledge was applied operationally (ie declaring domain knowledge) u Making Domain Knowledge explicit and open to analysis n Later on (1990’s) developers began to realise the great value in: u Making DM’s re-usable u Using DM’s to develop a common vocabulary and understanding of a domain (application or more usefully more general knowledge)

10. Knowledge Validation Something written in a formalism does not make it right….. “ If your PhD thesis is vacuous, turn it into predicate calculus, it makes the (forgot line..) seem nothing short of miraculous”

11. Knowledge Engineering: Validation Validation of a model is the process that promotes its quality in terms of internal and external criteria by the identification and removal of errors in the model. Internal criteria includes properties such as syntactic correctness and logical consistency; in general these properties can be proved formally and are not problematic. External criteria includes properties such as accuracy, correctness and completeness. Given that the sources of the model will not often be a mathematical object, these properties can never be proved correct (in the same sense that a requirements specification can never be proved correct).

12. Protégé - Protégé is an extensible tools environment to help with knowledge acquisition. - It has been specifically adapted in recent years to help acquire/formulate ontologies

13. Protégé - To download - http://protege.stanford.edu/download.html http://protege.stanford.edu/download.html - TUTORIAL: - http://protege.stanford.edu/doc/tutorial/get_started/