Practical Design of Business Enterprise Ontologies

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Tatiana Gavrilova Intelligent Computer Technologies Dept. Saint-Petersburg State Polytechnic University, Russia David Laird School of Information Science, University of Pittsburgh, USA

Outline 1. 2. 3. 4. 5.

Ontologies in KM. Trivial introduction. Ontologies as Cognitive structures IT-knowledge: practical ontology design Training of Analysts Summary & Discussion


1. Ontologies in KM. Trivial introduction. Top-managers and IT-analysts are continually challenged by the need to analyze massive volumes, velocities, and varieties of multilingual and multimedia data. Company staff and employees require support for knowledge sharing about information analysis, theories, methodologies and tools. Knowledge management (KM) is one of the powerful approaches to solve these problems. Analyst operates as a knowledge engineer by making the skeleton of the company data and knowledge visible and showing the domain’s conceptual structure. Visual representation of the general corporate business concepts facilitate company personnel understanding of both substantive and syntactic knowledge. At the present time, this structure is called an ontology.

Ontology Definition Ontology is a hierarchically structured vocabulary describing a domain that can be used as a skeletal foundation for a knowledge base [Gruber, Guarino, Gomez-Peres, Mizugochi, etc]


Categorization of Ontological Engineering

Types of Business Ontologies • • • • •

Company organizational structure Main concepts ontology (products, services, customers, skills, etc.), Historical ontology (genealogy of owners, customers, products, services, etc.), Partonomy of the company knowledge Taxonomy (methods, techniques, technologies, business-processes, skills, etc.)


The main problem 99% of research is focused on technology (languages, tools, standards) – it is a problem of HOW present ontology. We are focused on WHAT (what are concepts, relations, content, hidden structures, form, etc.) “How” is skill, “what” is art.

2. Ontology as a Cognitive structure Cognition is a process leading to Knowledge. Ontology is explicit cognitive structure that helps to present objectivity as agreement about subjectivity.


Knowledge Transformation

Design Principles using Gestalt theory laws (good form principles) Law of Pragnanz (M. Wertheimer)- organization of any structure in nature or cognition will be as good (regular, complete, balanced, or symmetrical) as the prevailing conditions allow (law of good shape).

Law of Proximity – objects or stimuli that are viewed being close together will tend to be perceived as a unit.

Law of Similarity – things that appear to have the same attributes are usually perceived as being a whole.

Law of Inclusiveness (W. Kohler)- there is a tending to perceive only the larger figure and not the smaller when it is embedded in a larger.

Law of Parsimony – the simplest example is the best or known as Ockham’s razor principle (14-th century): ”entities should not be multiplied unnecessarily''.


Good shape principle

Categorization mistakes


Law of similarity

Illustration by Maria Harrington

Towards beautiful ontology Harmony=conceptual balance +clarity

Conceptual balance Concepts of one level should be linked with the parent concept by one type of relationship such as is-a, or has part. The depth of the branches should be more or less the same (±2 nodes). The general outlay should be symmetrical. Cross-links should be avoided as much as possible.

Clarity Minimizing the number of concepts. The maximal number of branches and the number of levels should follow Miller number (7±2) The type of relationship should be clear and obvious if the name of the relationship is missed.


3. IT-knowledge: practical ontology design Four Step Process for Ontology Creation Goals, strategy and boundary identification Glossary development or meta-concept identification Laddering, including categorization and specification Refinement

Step 1 - Purpose and use of Ontology Allow IT Skills to be categorized and presented in a fashion that allows selection and grouping of skills during a organization-wide skills assessment


Step 2 - Glossary Development Personal Computer Maintenance Database Administration Cyber Security Super Computing Project Management Computer Architecture Human Factors in Systems Geographic Information Systems Information Storage and Retrieval Algorithm Design Operating Systems Knowledge Representation Ontologies Bridges Virus Detection

Peripherals Maintenance

Help Desk Support

Programming Encryption Telecommunications Graphics Software Development Lifecycle Human Computer Interactions Decision Support Systems

Application Development Commercial Software Training Mobile Computing Quality Assurance

Data Mining

Programming Languages

Software Engineering

Computer Engineering Document Processing

Visual Languages Information Processing Standards Expert Systems Routers Computer Server Support Enterprise System Customization

Legal and Ethical Issues Knowledge Management Network Switches Email Systems

Artificial Intelligence

Step 3 - Laddering: Building an Initial Mind Map Structure


First Level Categorization

General Ontology


Step 4: Refinement

Experiences Allows employee selection of skills from a standard and consistent skills hierarchy Simplifies skill selection for employees Ensures skills are entered consistently with a common understanding between employees Ensures more complete identification of skills by individual employees; searching only relevant categories and ignoring irrelevant categories Allows categorization of collected data to support continuity planning, through the following techniques; Training, Recruiting and Crossassignment Allows direct comparison and grouping of individual employee skills to identify shared training needs, allowing more efficient and proactive training Better identify risks of skill loss through employee loss or retirement Provide a structure for the establishment of a usable expert finder system Allows rapid location of desired skill by drilling down from broad to more narrow categories Supports other organizational issues Ensures a consistent understanding of skills and needs by all individuals the organization in establishing training and recruiting plans; creating job descriptions; establishing keyworks for document management systems, etc. Helps ensure that the organization is best structured to meet organizational knowledge and skill requirements.


4. Training of Analysts Structures Information Skills and and Processes Technology Motivation

47% Corporate culture



Management support



KM: Critical success factors


n=104 European companies Source: Fraunhofer IPK, Heisig, Heisig, Vorbeck 2001 [inf. from OntoWeb SIG4SIG4-WP2 - Ontoweb 5, Sanibel, Sanibel, October 2003] 2003].

Dualism of a Knowledge Analyst Communicative skills


Analytic skills



Analysts’ Psychological Portrait Sincerity Empathy Interest to other people

Friendly Leader

Optim ist


No defence


Field independent

Hum our

Accuracy Can listen to


Sociable pedant

ANALYST ontology


Gender differences Better spatial orientation Higher analyticity and field-independence Interest to the new decisions search (hunting) Worse speech perception

Better communicative skills Strong crosshemisphere links Hazard minimisation, fear of novelty Narrow range of cognitive likelihood Mix up «right-left» (50%)

Training for Analysts Part 1. Introduction to Data and Knowledge Engineering (12 hours, 8 tests) 1.1. Working with information: data and knowledge 1. 2. Knowledge representation models 1. 3. Working with fuzzy information 1. 4. Knowledge engineering structure 1. 5. Psychological aspect of data and knowledge acquisition 1. 6. Linguistic aspect of data and knowledge acquisition 1. 7. Methodological aspect of data and knowledge acquisition

Part 2. Practical Knowledge Engineering (16 hours, 10 tests) 2.1. Individual communicative data and knowledge acquisition methods 2.2. Group communicative acquisition methods 2.3. Textological methods 2.4. Data and knowledge structuring: object-structured analysis 2.5. Ontologies and visual modelling 2.6. Functional distribution in systems project team


Practical exercises/test/games

5. Summary & Discussion A 4-step ontology development process is proposed Ontology design needs qualified analysts which should be properly trained. Development and use of an ontology of IT Skills and Knowledge was illustrated to provide a concrete example of the proposed methodology In subsequent research, we plan to explore ways that ontology development and use can further improve visualization of business needs, and deliver additional value to the organization


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