JUST ENOUGH ONTOLOGY TUTORIAL by Paola Di Maio

JUST ENOUGH ONTOLOGY TUTORIAL by Paola Di Maio WIMS, 25 -27 MAY 2011, NORWAY

OVERALL OUTLINE a. Introductions, Tutorial overview 15 minutes b. Problem space, common challenges 5 c. A few words about innovation, knowledge, open innovation d. Why ontology, and overview of main concepts e. Ontology engineering (development) f. Just Enough Ontology

Scope of this tutorial WHAT IT IS: AN AGILE , PRAGMATIC, SOCIOTECHNICAL APPROACH TO ONTOLOGY DEVELOPMENT /A REFLECTIVE, EVOLUTIONARY LEARNING FRAMEWORK WHAT IT IS NOT: NOT INTENDED TO BE A COMPLETE APPROACH, IT DOES NOT INTEND TO ADDRESS EXAUSTIVELY NOR SATISFY IN DETAIL EVERY ONTOLOGY ENGINEERING REQUIREMENT

INTRODUCTIONS! Say a few words about yourself, where you come from, what brings you here, your interest, issues and expectations for this tutorial, if any Then we can take a look at the planned agenda for today, and make adjustments if necessary

KNOWLEDGE CHALLENGES INFORMATION OVERLOAD VERY FAST KNOWLEDGE EXCHANGES VERY FAST DEVELOPMENT CYCLES CANT KEEP UP WITH PROGRESS ALL AROUND CONVERGENCE OF MANY DISCIPLINES DIFFICULT TO STAY ON TOP OF EVERYTHING TOO MUCH KNOWLEDGE TO GRASP/REASON WITH VERY RAPID CHANGES, SHORT ITERATIONS MAKE PROJECT PLANNING DIFFCULT

WE DESIGN INFORMATION SYSTEMS TO COPE However, an information system needs a conceptual frame, so. . /

WE DEVISE ONTOLOGIES TO SUPPORT SYSTEM ARCHITECTURES However, there are many ways to go about developing an ontology, which one to choose? Do we hire an ontologist? What level of resources should be devoted to the ontology development? How long will it take? How do we evaluate the ontology? etc. .

all partly right but on the whole all wrong

A FEW WORDS ABOUT KNOWLEDGE, INNOVATION, OPEN INNOVATION

INNOVATION TO GENERATE NOVELTY (INCORPORATE THE NEW) INCREMENTAL/ EMERGENT SUBSTANTIAL RADICAL/ REVOLUTIONARY

THE ROLE OF KNOWLEDGE IN INNOVATION "Innovation. . . is generally understood as the successful introduction of a new thing or method. . . Innovation is the embodiment, combination, or synthesis of knowledge in original, relevant, valued new products, processes, or services. " Luecke and Katz (2003)

OPEN INNOVATION THE ABILITY OF AN ORGANISATION TO DEVELOP, ACCESS, INTEGRATE, DEPLOY KNOWLEDGE Open Innovation, A New Paradigm for Understanding Industrial Innovation, Henry Chesbrough http: //www. openinnovation. net/Book/New. Paradigm/Chapt ers/01. pdf

IBM Global CEO Study 2006

PEOPLE AND THE OPEN WORLD PEOPLE: BEHAVIOURAL, SOCIO-TECHNICAL DIMENSIONS OPEN WORLD (VS CLOSED WORLD) DIFFERENT ASSUMPTIONS CWA holds that any statement that is not known to be true is false. OWA no single agent or observer has complete knowledge, any statement that is not known to be true, is not necessarily false http: //en. wikipedia. org/wiki/Open_world_assumption

ONTOLOGY

WHAT IS AN ONTOLOGY ANYWAY (. . . oh no not again. . . ) Ontologies are conceptual and semantic representations widely used, in different forms, to capture and express such models. . . SEE HANDOUT FOR A LIST OF DEFINITIONS AND ADD YOURS. . .

ontology/definition The etymological source of the term “ontology” — ont — comes fromthe Greek verb “einai” (to be)from which the Latin word followed — “ontologia” (ont +ogia), translated as “the study of existence” [15]. Ontology is writtenwith an uppercase initial when referring to the top-level “discipline” and with a lowercasein all other occurrences [14]. or the study of what we know exists> MORE IN HANDOUTS

WHY ONTOLOGIES Information centric systems are designed to leverage knowledge expressed via natural language symbols and meanings (semiotics and semantics) need to be captured and represented adequately for these systems to function.

ONTOLOGY AS THE ULTIMATE KNOWLEDGE SPECIFICATION ontology = detailed, arguably complete, but always comprehensive knowledge schema many definitions, views , see separate handout HOWEVER DEVELOPING AN ONTOLOGY IS NON TRIVIAL

THE PROBLEMA Many methodologies Skills are scarce very complex project people with different views/priorities cognitive requirements are high organisation/coordination is difficult no single resource to helps us devise an ontology (more. . . )

ONTOLOGY ENGINEERING IS A SPECIALISED SKILL AN ONTOLOGY COULD BE THE ANSWER TO MANY INFORMATION SYSTEMS CHALLENGES, BUT PARADOXICALLY, ONTOLOGY DEVELOPMENT (AKA ONTOLOGY ENGINEERING) TENDS TO BECOME A LONG TERM PROJECT IN ITSELF, IE, REQUIRES DEDICATED ALLOCATION OF RESOURCES, lots of fluff in there as well THEREFORE IN THIS TUTORIAL WE SUGGEST A 'JUST ENOUGH ONTOLOGY' APPROACH TO TRY TO GET TO OUR SOLUTION WITHOUT GETTING LOST

AN ONTOLOGY CONSISTS OF. . . An ontology may take a variety of forms, but it will necessarily include a vocabulary of terms and some specification of their meaning, such as definitions and an indication of how concepts are interrelated, which collectively impose a structure on the domain and constrain the possible interpretations of terms Ushold et al

PROLIFERATION Many ontology engineering (OE) methods, artifacts, tools and techniques do not make ontology has not become any easier. The choice of an appropriate methodology for any project may require a systematic evaluation of existing approaches, and this can become extremely resource intensive and time consuming. A default option is to follow no methodology at all (the 'who needs a methodology? ' attitude) often preferred by developers who go straight into coding, and may confuse 'on the fly' schema creation with fully fledged ontology development.

JUST ENOUGH APPROACHES. . . 'SOFT' AND 'AGILE' APPROACH DEVELOPED BY ED YOURDON IN THE SEVENTIES WHEN DATABASES BECAME THE KEY TO INFORMATION SYSTEMS, STRUCTURING DATA BECAME THE NEXT BIG THING, BUT TOO MANY METHODOLOGIES, AND TOO RIGID APPROACHES, MADE THE TASK OFTEN MORE BURDENSOME THAN THE CHALLENGES THEY WERE TRYING TO SOLVE

some useful PRINCIPLES AGILITY ADAPTIVITY EVOLUTIONARY APPROACH -----------JUST IN TIME JUST ENOUGH

IS AN ONTOLOGY A VOCABULARY? http: //infogrid. org/wiki/Reference/Pidcock. Article A controlled vocabulary is a list of terms that have been enumerated explicitly A thesaurus is a networked collection of controlled vocabulary terms. People use the word ontology to mean different things, e. g. glossaries & data dictionaries, thesauri & taxonomies, schemas & data models, and formal ontologies & inference. A formal ontology is a controlled vocabulary expressed in an ontology representation language, . . . a grammar for using vocabulary terms to express something meaningful within a specified domain of interest. The grammar contains formal constraints (e. g. , specifies what it means to be a well-formed statement, assertion, query, etc. ) on how terms in the ontology’s controlled vocabulary can be used together.

applied minds

The notion of boundary WHAT CONSTRAINS A SYSTEM (lexical, conceptual, etc)

MANY TYPES OF ONTOLOGIES Upper ontology - toplevel categories and classes Domain ontology Aspect ontology — aspect of a domain Formal ontology — when the ontology is fully formalized Informal ontology — any structured collection not constrained by a formalization Applied ontology — wherethe ontology is developed in support of software artifacts Task ontology — when it isdevised in support of a specific task or function, as opposed to a whole domain

MORE TYPES OF ONTOLOGIES 1. Content ontologies for reusing knowledge 2. Communication ontologies for sharing knowledge 3. Indexing ontologies for case retrieval 4. Meta-ontologies (knowledge representation knowledge) Van Heijst et al. identifies orthogonal dimensions to distinguish different types ofontologies: (1) terminological ontologies, information technologies, and knowledge modeling ontologies; and (2) representation, generic, domain,

TAXONOMY OF THERMAL COMPONENT

TAXONOMY OF PHYSICAL MECHANISM

CONCEPTUAL SCHEMA OF SYSTEM

ONTOLOGY DEVELOPMENT LIFECYCLE

MANY METHODS DEF 5 TOVE DODDLE CLEPE/AFM Cyc method Mike Uschold and Martin King’s method Michael Grüninger and Mark Fox’s method KACTUS METHONTOLOGY SENSUS On-To-Knowledge Onto Clean DILIGENT, HCOME, OTK methodology, Ontology Development 101 CO 4, KASquare, DOGMA (AKEM) SEKT, On. To Knowledge(OTK) Onto. Clean BORO

METHODOLOGICAL DIFFERENCES THE PHASES TEND TO BE THE SAME SOME DETAIL CHANGES THE MAIN DIFFERENCE IS THE SCHEDULING OF DEVELOPMENT ACTIVITIES SOMETIMES DIFFERENT EMPHASES

ONTOLOGY PROJECTS CAN BE HIGH RISK OF FAILURE BECAUSE OF a. VERY HIGH COMPLEXITY • MANY UNCERTAINTIES • OD/E SKILLS ARE SCARCE RISK OF OVERENGINEER/GET LOST OR UNDERENGINEER/OVERSIMPLIFY WE ARE LOOKING FOR THE MIDDLE WAY

JUST ENOUGH OD/E RATIONALE CAPTURES COMMON DENOMINATOR TO MOST METHODOLOGIES MAKES ACTIVITIES AGILE/ITERATIVE SIMPLIFIES THE PROCESS BY PROVIDING A SCHEMA KEEPS ALL OPTIONS OPEN DESIGNED TO CAPTURE EMERGENCE

PRINCIPLES: STRUCTURING AND ABSTRACTION In data modeling, abstraction is what allows to identify and group information assets based on generic common characteristics that exist independently from their time/space representation. knowledge modeling Object Oriented Design (OOD), Unified Modeling Language (UML), Integrated Development Framework (IDEF)

SINGLE ABSTRACTION Structured methods rely on the notion of 'single abstraction' mechanism, which consists of extracting a top level view of different aspects of the system, forming the basis for functional decomposition', the technique that drills into top level functions, and breaks them down into smaller functions, while preserving the representation of other functional aspects of the systems such as inputs, outputs, controls and other mechanisms.

DIAGRAMMATIC REPRESENTATION Diagrammatic methods such as UML, for example, can be used as a form of ontological notation (S. Cranefield), although it is sometimes argued that they may not have the 'expressivity' required to represent all of the essential ontological formalism, such as axioms. Topic Maps are an ISO information standard that enables multiple, concurrent views of sets of information objects which can be used to represent ontological knowledge, Concept maps and mind maps are also used to aid conceptual visualizations, although they are not a standardized.

PROBLEMS WITH STRUCTURED METHODS HEAVY, NON AGILE PERCEIVED AS SUITABLE ONLY FOR WATERFALL DEVELOPMENT STYLE

DESIGN PATTERNS http: //en. wikipedia. org/wiki/Design_pattern_(computer_science) WAY OF REPRESENTING A STRUCTURE FOR REUSE C ALEXSANDER, ET AL Algorithm how to exploit application characteristic on a computing platform. Computational addressing concerns related to key computation identification. Execution patterns that address concerns related to supporting application execution, Implementation strategy patterns addressing concerns related to implementing source code Structural design patterns addressing concerns related to high-level structures of applications being developed.

ONTOLOGY PATTERNS (Blomqvist) Application Patterns - Purpose, scope, usage and context of the implemented ontology or ontologies, including interfaces and relations to other systems. Architecture Patterns - A description of how to combine or arrange implemented Design Patterns in order to fulfill the overall goal of the ontology. Design/Semantic Patterns - A small collection of Semantic Patterns that together create a common and generic construct for ontology development. Syntactic Patterns - Language specific ways to arrange representation symbols, to create a certain concept, relation or axiom. (end sidebar)

JOE MAIN CONTRIBUTIONS 1) a strong emphasis on stakeholder analysis and management 2) implementation independence

STAKEHOLDERS A diverse stakeholder basis necessary to a balanced mix of views and sustainability of ontologies, especially their use and long term maintenance. OE requires depth and breadth of understanding, knowledge and skills in a variety of fields. It is becoming increasingly important to broaden the stakeholder base and to make this process accessible to as many participants as possible, but not at the expense of validity and ‘ontological rigour’, although even validity and rigor depend on where certain boundaries are set. takeholders bring onto the development table a much needed socio-technical perspective, of which people and environments are important elements.

IMPLEMENTATION INDEPENDENCE A variety of stakeholders with different skill sets (not necessarily skilled in the implementation language of choice) and used as part of sometimes heterogeneous architectures, separation between ontology design and itimplementation. Implementation independence is a well established principle in systems science, and it constitutes one of the core features of systems architectures. In the early days of computing it was advocated by Childs

IMPLEMENTATION INDEPENDENCE 2 Codd and Dates relational model as 'data independence' Model Driven Architectures 'platform independence' In Ontology, Tom Gruber advocated ‘minimal encoding bias’

GRUBER'S 5 PRINCIPLES 1. Clarity 2. Coherence 3. Extendibility 4. Minimal encoding bias 5. Minimal ontological commitment

JEO, 13 STEPS (mostly iterative) 1 Identify stakeholders, outline stakeholder profile 2 Define the purpose of the ontology (emphasis on representation/indexing, problem solving/reasoning) 3 Outline requirements [iterative] 4 Identify and survey existing knowledge sources and existing ontologies, elicit existing knowledge Assess why the existing knowledge resources do not meet the intended user requirements, update the requirements with the output of activities above [iterative 5 Scoping ontology (defining the boundaries andlevel of granularity, according to goal and stakeholders requirements) Update the requirements [iterative] 6 Devise and implement quality assurance plan Add quality parameters to the requirements [iterative] 7 Define the field of competence to identify the knowledge boundaries (competence questions) Match the field of competence with the knowledge sources 8 Define the ontology artifacts: Vocabulary Identity concepts/entities/classes relations, axioms Refine and map vocabularies to artifact 9 Transfer concepts to ontology language representation: Select knowledge representation formalisms and annotation depending on stakeholder requirements, scope and goal 10 Deploy/systems integration (modular, incremental) 11 Testing Evaluation quality monitoring competence assessment 12 Publishing 13 Maintenance Reuse

1. IDENTIFY STAKEHOLDERS Potentially, anyone actively involved in the ontology development and intended use, and anyone whose interests may be affected by the development of such an ontology. Stakeholder management' to assess goals, motives and commitments, and to create and sustain the collaborative momentum that can fuel an ontology development project itself. For example: assessing their ‘readiness’ to participate in the project, identifying the barriers that prevent them from participating and making commitments to the ontology, determining the congnitive or organizational requirements, including existing and potential conflicts with existing conceptual models in use.

2. DEFINE GOAL/PURPOSE GIVEN THE MULTIPLICITY OF THINGS THAT ARE UNDERSTOOD UNDER 'ONTOLOGY' (SEE THE EXAMPLE BEFORE, IT IS IMPORTANT TO ESTABLISH WHAT IS THE INTENDED USAGE OF THE ONTOLOGY examples: support a process execution within a system Improve the efficiency of reasoning Consolidate and harmonize existing data/information Provide an abstract, more schematized view Create a consensual, unified view that can serve as synthesys of different views Provide a formal specification Support integration of data, applications, and systems to help minimize design and planning errors caused by lack of domain knowledge

3. OUTLINE REQUIREMENTS SHALL/SHOULD. . FOLLOW USUAL RA SUPPORT THE STATED GOALS/PURPOSE DESIRABLES: a. It must declare explicitly what high level knowledge (upper level ontology) it references, • Must declare explicitly what kind of reasoning/inference supports/it is based on. • It must be accessible to all the agents/agencies (this means shared, viewable, understandable) • It must be ‘acceptable’ to all the agents/agencies from the different perspectives, in terms of culture, language, conformance to policy and protocols • It must be ‘usable’ in terms of compatibility with local information systems used by agents/agencies

MOST ONTOLOGIES WILL BE USED FOR: Consistency checking ( properties and value restrictions) Auto Completion of information partially provided by users Interoperability support (shared conceptualization) Support validation and verification testing of data (and schemas) Configuration support — class terms may be defined so that they contain descriptions of what kinds of parts may be in a system.

4. KNOWLEDGE SOURCES DECIDE AND MAKE EXPLICIT WHERE DOES THE K COME FROM Stakeholders (who have a vested interest in the business, application, or component being specified). Subject matter experts (e. g. , domain experts, industry analysts, consultants). Reusable requirements and requirements specifications (see Requirements Reuse) Documentation (e. g. , business strategy documents, business vision statement, documentation of relevant legacy systems, workflow procedures, vendor documentation, marketing studies, change/enhancement requests from the users and technical service representatives, industry analyst reports, etc. ). Human Interface Prototypes. Legacy, competing, and similar applications. Application and enterprise databases

5. SCOPE THE ONTOLOGY: LEVELS OF REALITY

LATTICE OF TOP LEVEL CATEGORIES J SOWA http: //www. jfsowa. com/ontology/toplevel. htm

MEREOLOGY Theory of whole and parts

Component level: Out of which concrete artefacts (device components) does the system that is to be designed exist, and how are they interconnected (system structure or layout)? Process level: How is the behaviour of the system components realized in terms ofphysical mechanisms? Mathematical level: How is the physical behaviour formally specified in terms of equations, such that system analysis and simulation can be carried out by computer

6. QUALITY MODEL Quality Models contain patterns of qualitative and quantitative measurements of various aspects, and quality cannot be easily assessed with straight ‘testing’, ad quality often results from a combination of factors, of which ‘correctness’ and ‘accuracy’ are merely part of. A ‘ quality model’ is developed upfront, and its dimensions and metrics are used as target parameters throughout the development, evaluation and testing of an ontology. The best measure for quality is ‘fit for purposeness’, also know as the 'good enough' principle, however measuring standard parameters can help develop a quality strategy which is essential especially when working in large organizations. In a narrow sense, the quality of an ontology is measured across two dimensions: accuracy and its comprehensiveness, corresponding roughly to precision and recall in search technology, but a whole range of parameters can be used to assess the quality, as shown in the indicative table below.

7 FIELD OF COMPETENCE DOMAIN OF COMPETENCE = KNOWLEDGE FIELD SET OF QUESTIONS TO VERIFY WHETHER AN ONTOLOGY CAN ANSWERS THE QUESTIONS GET THE QUESTIONS FROM THE STAKEHOLDERS Different tests can be set up to verify the validity and quality of each part of the ontology, and each process within the ontology development, and carried out correspondingly at each step.

8. ARTEFACTS 1. vocabularies 2. concepts 3. relations 4. axioms/ruls

8/1 VOCABULARIES STUDENT definition: person who studies | class, subclass of_x | relation: part_of, parent_of | allowed value: male, female|

8/2 CONCEPTS Concepts are fundamental to our ability to think, express, represent and communicate, however, defining unambiguously and with certainty what constitutes a concept, is rather tricky, and pushes IT practitioners toward the realm of philosophy. But that’s a challenge of ontology engineering. Concepts can correspond to things, but also to ‘fuzzy clouds’ of ideas and notions identified by words and related to a certain thing or subject. And even when referring to tangible things, concepts can be abstract, volatile. Even simple ‘things’ are made of parts, relations, dependencies, temporal and spatial circumstances. When trying to put the finger exactly reality, all sorts of questions inevitably come up. The nearest technique that can be compared to conceptual modeling, is entity modeling, or class modelling. Concepts can be broadly divided into cognitive artifacts that support categorization and communication, [20] and are necessary to support human and artificial thinking and reasoning. The purpose of ontologies is to make them explicit and represent them so that they serve a variety of purposes, namely the intended goals. Conceptual categories and thoughts are closely related to language. A concept model can be used to complement and extend a functional data model.

8/2. 1 RELATIONS • Arity: Typically binary relationships are of most interest, but relationships can be of arbitrary arity, i. e. , we could have 3 or more concepts participating in a relationship. These constraints are characterized in one of the following ways: 1 -1, many-1, 1 -many, or many-many. A more generalized way of representing these cardinality constraints is using a pair of numbers that specify the minimum and maximum number of times an instance of a concept can participate in a relationship. This is a very useful technique for n-ary relationships and also captures partial participation of concepts in relationships. 1 -1 and many-1 relationships are functions which can be exploited in various ways. • Direct v/s Transitive Relationships: • Crisp vs. Fuzzy:

8/2. 2 RELATIONS causal temporal spatial relation semantic relation logical relation In a language context, relations are called ‘lexical relations’, and are known as ‘taxonomic relations’, such as synonymy, where two different terms point to different concepts, homonomy, where the same word points to two different things or concepts, antonymy, where two terms indicate two opposites etc.

8/2. 3 example of lexical relations from OBO (Stanford)

8/4 AXIOMS WHAT IS ALWAYS TRUE GENERALLY EXPRESSED AS RULES USED TO CHECK CONSISTENCY SUPPORT OF REASONING

8/4. 2 AXIOMS AS RULES Axioms translate into constraints, which in turn can be considered as the logical boundaries of an ontology. They can be transformed and mapped easily directly into rules. If an axiom maps to a rule, then consisting parts of an axiom map to the consisting parts of a rule. The mapping follows: • ontology axiom → rule • axiom statement → rule clause • statement concept → entity in a rule clause • statement relationship → relationship in a rule clause

9 FORMALISM/IMPLEMENTATION A FORMALISM CAN BE DEFINED AS A 'LANGUAGE/NOTATION' RULE 4 OF GRUBER (SEE GRUBER'S 5 RULES) DO NOT OVER COMMIT TOO EARLY a. DESCRIPTION LOGIC • XML/RDF/OWL • COMMON LOGIC • OTHERS (. . . )

9/1 DESCRIPTION LOGIC

9/2. 2 TBOX AND ABOX Knowledge representation system based on DLs consists of two components - TBox and ABox. The TBox describes terminology, i. e. , the ontology in the form of concepts and roles definitions, while the ABox contains assertions about individuals using the terms from the ontology. Concepts describe sets of individuals, roles describe relations between individuals

10. PUBLISHING MAKING DISCOVERABLE, AVAILABLE, ACCESSIBLE LEVEL OF PERMISSIONING (COPYRIGHT, INTELLECTUAL PROPERTY) SENSITIVITY FORMATTING INDEXING TAGGING LINKING!

10. 1 OWL: ontology web language W 3 C SPECIFICATION http: //www. w 3. org/TR/owl-guide/ LOOK AT THE SPEC OWL Web Ontology Language New Version Available: OWL 2 (Document Status Update, 12 November 2009)The OWL WG has produced a W 3 C Recommendation for a new version of OWL which adds features to this 2004 version, while remaining compatible. Please see OWL 2 Document Overview

THEN WATCH DEMO WEBCAST SEE THE WEBCAST http: //knoodl. com/ui/site/webcast/intro. jsp

11. INTEGRATION DEPLOY THE ONTOLOGY AS PART OF A SYSTEM INTERFACES (SYSTEMS, USERS ETC)

12. EVALUATION/TESTING Consistency, integrity, validation, redundancy testing, as well as usability testing can be adapted to test an ontology. In order of priority, an ontology should be tested for -accuracy – ability to support correct inference – in relation to the expected ‘range of competence’ -its ability to identify and represent correctly linguistic intensionality and extensionality, for example, where the first refers to the ‘aboutness’ of an expression, and the extension is the class of objects that an expression refers to. http: //en. wikipedia. org/wiki/Extension_(semantics) http: //en. wikipedia. org/wiki/Intension

13. MAINTENANCE REVISE PERIODICALLY UPDATE SET UP FEEDBACK LOOP CAPTURE ITERATIVE CHANGES START FROM 1, REPEAT AS MANY TIMES AS NECESSARY

Coverage/Scope Is the vocabulary capable of representing all of the concepts used in the chapter? Does the vocabulary have the terms necessary to represent the full range of issues? Does the vocabulary encompass the terminology used to describe the various procedures? Does the vocabulary use terms that are commonly used by SE? Specificity Is the vocabulary specific enough to accurately represent the many aspects of SE reality? Does the vocabulary capture information in sufficient detail? Structure Are the vocabulary hierarchies logical and complete? Are the meanings of terms clearly defined? Does the vocabulary contain redundant terms? Are there explicit rules for combining terms, or for combining terms and qualifiers? Does the vocabulary allow for multiple classification of terms, that is, can terms appear in more than one hierarchy? Useability Is the vocabulary mapped to other vocabularies used in the practice? Does the vocabulary meet the needs of a range of end users? Does the user interface facilitate optimal use of the vocabulary with minimal training?

RECOMMENDED TOOLS TO TRY http: //knoodl. com SEE THE WEBCAST http: //knoodl. com/ui/site/webcast/intro. jsp Referata PROTEGE

CONCLUSION/QUESTIONS this tutorial introduces an agile approach to ontology developement in relation questions?

JUST ENOUGH ONTOLOGY WWW. JUSTENOUGHONTOLOGY. ORG OPEN FOR COLLABORATIVE DEVELOPMENT ADD YOUR OWN ITERATION, RESOURCE, ILLUSTRATION, CASE STUDY ETC. . .

EXERCISE/DISCUSSION IF THERE IS TIME, FROM A GROUP, ASSIGN ROLES (STAKEHOLDERS) CARRY OUT STEPS 1/13 WHERE POSSIBLE DISCUSS