Knowledge Modeling and Management

1. Knowledge Modeling and Management M1 2011-2012

2. Objectives for this session History & theory of Knowledge Management (KM) KM models Ontology and its design methodology. OWL.

3. What is Knowledge Modeling? Knowledge modeling is a process of creating a standard and computer interpretable specifications of a given domain knowledge. Computer interpretable: it is expressed in some knowledge representation language that enables the knowledge to be interpreted by software and to be stored.

4. Seek knowledge from the cradle to the grave Expertise, and skills acquired by a person through experience or education; The theoretical or practical understanding of a subject: Knowing that. Knowing how.

5. What is Knowledge? It originates from the minds of knowers. In organizations it often becomes embedded in documents, repositories, organizational processes, practices and norms. Davenport, T.H. & Prusak, L (1998). Working Knowledge. Knowledge is information in action.O’Dell C. & Grayson Jr., C.J.(1998). If only we knew what we know.

6. Two types of knowledge Explicit knowledge Formal or codified Documents: reports, policy manuals, white papers, standard procedures Databases Books, magazines, journals (library) Implicit (Tacit) knowledge Informal and uncodified Values, perspectives & culture Knowledge in heads Memories of staff, suppliers and vendors Know-how & learning embedded within the minds people. Documented information that can facilitate action. Knowledge supports decisions and actions. Sources: Polanyi, M. (1967). The tacit dimension. Leonard, D. & Sensiper, S. (1998). The Role of Tacit Knowledge in Group Innovation. California Management Review.

7. Layers of knowledge Explicit Implicit (Tacit) Individual • Personal documents • on my C:\ • Formalized process. • Corporate polices and • procedures. • In people’s heads. • Undocumented ways of working. • Cultural, conventions • known and followed • but not formalized. Organizational Source: Luan, J & Serban, A. (2002, June). Knowledge management concepts, models and applications. Paper presented at Annual AIR Forum, Toronto.

8. One Perspective of KM “Knowledge Management involves turning a company’s information into actionable knowledge via a technology platform.” Susan DiMattia and Norman Oder in Library Journal, September 15, 1997.

9. Understanding KM Wisdom Knowledge Information Data

10. From Facts to Wisdom

11. Knowledge Management Models Documentalist? Technologist? Learner & Communicator?

12. Documentalists In the first part of the twentieth century, documentalists had grand visions of collecting, codifying and organizing the knowledge in form of structural linguistics and semiotics.”.

13. Caution It would be a mistake, though, to define KM as solely the domain of documents and documentalists.

14. KM as a Technological Solution KM is: Big business. A competitive advantage. Intellectual capital. An intranet solution. An asset dimension. A technological infrastructure.

15. Content nets KM applications As knowledge repositories for tacit knowledge that has been made explicit For best practices databases or expert “yellow pages” Online learning and knowledge sharing Knowledge sharing “boards”

16. The Challenges of Collaboration & Knowledge Sharing “Focusing exclusively on the technical issues of knowledge sharing is a vey good way to a very expensive failure.” “A focus on the people issues dramatically increases the potential for success.” David Coleman, IBM Manager, San Francisco in Knowledge Management, a Real Business Guide, London:IBM, nd.

17. Peoplenets & Processnets For group learning applications To connect individuals with each other for mentoring and knowledge sharing For decision support & decision making To sense, share, and respond to the “signals” coming from the environment To capture ideas and turn them into action. The eventual goal is to share knowledge among members of the organization.

18. Caution It would be a mistake, though, to define KM as solely the KM’s technology infrastructure.

19. “Processing data can be performed by machine, but only the human mind can process knowledge”!!!! Jesse Shera in Machlup and Mansfield’s The Study of Information: Interdisciplinary Messages. NY: Wiley, 1983. Is it a mistake?

20. How to represent the knowledge Links and structures Notation Ontology.

21. What is an Ontology? • A written, formal description of a set of concepts and relationships in a domain of interest. (Peter Karp (2000) Bioinformatics). • It is a formal explicit description of concepts in a domain, properties of each concept describing various features and attributes of the concept (slots (sometimes called roles or properties)), and restrictions on slots (facets (sometimes called role restrictions)). • An ontology together with a set of individual instances of classes constitutes a knowledge base. • In reality, there is a fine line where the ontology ends and the knowledge base begins.

22. Ontology is a system of concepts Knowledge Base Ontology An explicit specification of a hidden conceptualization of the target domain

23. The fundamental role of an ontology An ontology is not directly used for problem solving. It gives a specification of knowledge/models in a system. The role of an ontology to a knowledge base is to give definitions of concepts used in the knowledge representation and constraints among concepts to make the knowledge base consistent and transparent. Knowledge Amplifier systems.

24. Why? To share common understanding of the structure of information among people or software agents. Enabling reuse of domain knowledge. Making explicit domain assumptions underlying an implementation. Separating the domain knowledge from the operational knowledge is another common use of ontologies. BI: analyzing domain knowledge.

25. Knowledge Eng. VS. Ontological Eng. KE is the research on: Domain-specific heuristics for a stand-alone problem solver (AI). OE is the research on: General/reusable/sharable/long-lastingconcepts for building a KB/model for helping people solve problems.

26. Ontology types Light-weight Ontology One like Yahoo ontology Vocabulary rather than concepts Annotation-oriented ontology Used for Information search Heavy-weight Ontology (, Koru, Wolfram Alpha. Concepts rather than vocabulary for understanding the target world for building Knowledge-Based systems.

27. Know that Ontologies’ Design principles

28. Building Ontologies In practical terms, developing an ontology includes: defining classes in the ontology, arranging the classes in a taxonomic (subclass–superclass) hierarchy, defining slots and describing allowed values for these slots, filling in the values for slots for instances.

30. Fundamental rules There is no one correct way to model a domain— there are always viable alternatives. The best solution depends on the application that you have in mind and the extensions that you anticipate.

31. Fundamental rules Ontology development is iterative. Concepts in the ontology are: objects and relationships in a domain of interest. Objects are most likely to be nouns, the relationships are verbs in sentences that describe your domain.

32. Step 1. Determine the domain and scope of the ontology What is the domain that the ontology will cover? For what we are going to use the ontology? For what types of questions the information in the ontology should provide answers? Who will use and maintain the ontology?

33. Example Consider an ontology which helps representing the best combination (wine/ food). Representation of food and wines is the domain of the ontology. We plan to use this ontology for the applications that suggest good combinations of wines and food. It is unlikely that the ontology will include concepts for managing inventory in a winery or employees in a restaurant. If it helps restaurant customers decide which wine to order, we need to include retail-pricing information.

34. Step 1 Example Competency question Which wine characteristics I consider when choosing a wine? Is Bordeaux a red or white wine? Does it go well with seafood? What is the best choice of wine for grilled meat? Which characteristics of a wine affect its appropriateness for a dish? Does a bouquet or body of a specific wine change with vintage year?

35. Step 2. Consider reusing existing ontologies Sure, if they exist!!!

36. Step 3. Enumerate important terms in the ontology What are the terms we would like to talk about? wine, grape, winery, location, fish and red meat; subtypes of wine such as white wine, and so. What properties do those terms have? a wine’s color, body, flavor and sugar content; What would we like to say about those terms? Constitute a list of terms without worrying about overlap between concepts, relations among the terms, or whether the concepts are classes or slots.

37. Step 4. Define the classes and the class hierarchy Methods Top-down: start with the most general concepts in the domain and then their domain and sub-classes. For example creating classes for the general concepts of Wine and Food. Then we specialize the Wine class by creating some of its subclasses: White wine, Red wine, Rosé wine.

38. Step 4. Define the classes and the class hierarchy • Bottom-up: starts with the most specific classes, the leaves of the hierarchy, with grouping of these classes into more general concepts. • Combination: it is a combination of the top-down and bottom-up approaches: We define the more salient concepts first and then generalize and specialize them appropriately.

39. Step 4. Define the classes and the class hierarchy Top-down Food and Wine -- followed by White, Blush and Red Bottom-up define specific wine class first and the work your way up Combination

40. Step 4. Define the classes and the class hierarchy From the list we select the terms that describe objects having independent existence rather than terms that describe these objects. These terms will be classes in the ontology. We organize the classes into a hierarchical taxonomy by asking if by being an instance of one class, the object will be by definition an instance of some other classes. A super class of B means that class B represents a concept that is a “kind of” A.”

41. Step 5. Define the properties of classes—slots Types of properties “intrinsic” properties such as the flavor of a wine; “extrinsic” properties such as a wine’s name, parts, if the object is structured; these can be both physical and abstract “parts” (e.g., the courses of a meal) relationships to other individuals; these are the relationships between individual members of the class and other items.

42. Step 5. Define the properties of classes—slots We have already selected classes from the list of terms we created in step3. Most of the remaining terms are likely to be properties of these classes. For each property in the list, we must determine which class it describes. Describe the internal structure of concepts. Examples a wine’s color, body, flavor sugar content location of a winery.

43. Completing slots In addition to the properties we have identified earlier, we need to add the following slots to the Wine class: name, area, maker, grape.

44. Inheritance All subclasses of a class inherit the slot of that class. For example, all the slots of the class Wine will be inherited to the subclasses Red Wine and White Wine. We will add an additional slot, tannin level (low, moderate, or high), to the Red Wine class. The tannin level slot will be inherited by all the classes representing red wines (such as Bordeaux and Beaujolais).

45. Step 6. Define the facets of the slots Slot cardinality defines how many values a slot can have. sometimes it may be useful to set the maximum cardinality to 0 (why?) Slot-value type what types of values can fill in the slot. allowed values other features of the values the slot can take.

46. Step 6. Define the facets of the slots common value types: String Number Boolean Enumerated Domain-range (instance- type) slots allow definition of relationships between individuals.

48. Step 6 - Domain and Ranges When defining a domain or a range for a slot, find the most general classes or class that can be respectively the domain or the range for the slots . If a list of classes defining a range or a domain of a slot includes a class and its subclass, remove the subclass. If a list of classes defining a range or a domain of a slot contains all subclasses of a class A, but not the class A itself, the range should contain only the class A and not the subclasses. If a list of classes defining a range or a domain of a slot contains all but a few subclasses of a class A, consider if the class A would make a more appropriate range definition.

49. Step 7 - Creating Instances Body: Light Color: Red Flavor: Delicate Tannin level: Low Grape: Gamay (instance of the Wine grape class) Maker: Chateau-Morgon (instance of the Winery class) Region: Beaujolais (instance of the Wine-Region class) Sugar: Dry