Knowledge representation methods Knowledge bases, case bases, databases

1. Knowledge representation methodsKnowledge bases, case bases, databases

2. Outline • Data, information, knowledge • Knowledge representation methods • characteristics, advantages, disadvantages • Knowledge bases, case bases, databases • databases • case bases • knowledge bases (e.g., ontologies)

3. Data, information, knowledge • data is raw facts • information is processed, data that has a meaning • knowledge is what you apply to make decisions and solve problems

4. Computational Knowledge Knowledge • knowledge is what you apply to make decisions and solve problems • types of knowledge • declarative (classification) • procedural (association rules) • structural knowledge (relationships) • human vs. computational knowledge

5. Decision Making and Problem Solving

6. gathering information of alternate strategies the best strategy implement monitor

7. When is knowledge absolutely necessary ?? Can you perform it without using knowledge?

8. information knowledge knowledge information information Can you perform it without using knowledge?

10. How do you represent knowledge? How do you represent computational knowledge?

11. Knowledge representation formalisms • trees, e.g. parsing, decision trees • graphs • conceptual graphs • logic • neural nets • concepts, objects, facts • taxonomies, ontologies • rules • frames • cases, MOPs • similarity measures • semantic nets • Bayesian nets • intelligent agents

12. Knowledge representation formalisms can be used with different inferencing methodologies or algorithms to perform intelligent (AI) tasks

13. Metrics for representation formalisms • representational adequacy • inferential adequacy • inferential efficiency • clear syntax and semantics • naturalness

14. Logic • truth preserving inference • ability to recognize negation, ordering, disjunction and quantification • precise and formal language to represent declarative knowledge • represents semantics

15. Logic (Cawsey, 1998) “a logic is a formal system which may be described in terms of its syntax (what the allowable expressions are), its semantics (what they mean)and its proof theory(how can we draw new conclusions given some statements in the logic)”

16. Characteristics of Logic • everything is well defined, not inspired by human reasoning • Rules of inference • modus ponens, resolution rule • logic-based methods are complete so they can prove hypotheses without doubt • while propositional logic is simpler it is more limited and would require much more hand engineering to do the same

17. Concepts, Objects and Facts • An object is a basic entity that can be instantiated. • A fact is a statement that can be either true or false (Durkin, 1994). • A concept tells something about the object. • A concept can be: • An abstraction, such as a class of objects • An object associated with a valued attribute • It may be simpler to represent an abstraction as an attribute

18. (Production) Rules • A logic sequence of an antecedent (premise, condition) and a consequence (conclusion, action), which represent facts. • The antecedent attempts to verify if the fact is true or false, when the fact composing the antecedent is true, the conclusion is triggered. • The antecedent can be composed of several facts connected through operators such as and, or, and not. • Conclusions usually change or assign values to attributes of an object, call methods or trigger other rules.

19. Frames • representation formalism commonly used in expert systems • represents declarative, structural and procedural knowledge • first introduced by M. Minski in 1975, “A frame is a data-structure for representing a stereotyped situation, like being in a certain kind of living room, or going to a child's birthday party. Attached to each frame are several kinds of information. Some of this information is about how to use the frame. Some is about what one can expect to happen next. Some is about what to do if these expectations are not confirmed”.

20. Characteristics of frames • support inheritance (subclasses and instances) • support methods • when needed • after changed • before changed • easy to implement in different programming paradigms, logic-based or not

21. Cases, similarity functions • A description of an experience can be used as a knowledge representation formalism • Cases can be represented in formlike representations (e.g., job applications) • Cases can also be represented through networks or nodes in a graph • A case typically describes a pair wrt the task: problem-solution; text-interpretation; task-lesson • Similarity functions represent how to assess similarity between two cases in a give problem

22. Knowledge containers in case-based reasoning systems

23. Semantic Networks • commonly used in logic-based expert systems • directed graphs where (Quillian, 1968): • nodes represent objects and concepts • arcs represent relationships between objects and attributes • used to represent static elements of a representation such as the class, the instances and its features

24. Conceptual graphs • variety of semantic networks • represent meaning (Sowa, 1984) • (Cyre, 1997), “A conceptual graph is a finite, connected, bipartitioned graph consisting of a set of labeled concept nodes, a set of labeled conceptual relation nodes, and a set of (directed) arcs linking concept and relation nodes”.

25. Neural Networks • inputs and outputs are represented numerically • a matrix of weights learns the input/output behavior • weights in the matrix are information • the learned matrix (for facts in the same category as the inputs) represents knowledge

26. Knowledge bases, case bases, databases • Databases • Case bases • Knowledge bases: • rule-based ES • ontologies

27. Databases • Characteristics: characters, fields, records, files • Types: relational, hierarchical, network • Advantages: • Fast, reduces redundancy, easy to update and maintain • Disadvantages: • brittle, not amenable to inference, do not contain knowledge • Potential applications and uses • data warehouses (lose ease of update & maintain)

28. Knowledge bases • Rule-based • Case-based • Ontologies

29. Knowledge base • Definition from Durkin (1994): Part of an expert system that contains the domain knowledge. • AI definition: Part of a knowledge-based system that contains knowledge to be used in reasoning. • General definition: Repositories of any knowledge representation formalism that have the ability to perform AI tasks

30. Case bases • Types of case bases • textual, numeric features, discrimination networks • case bases vs. databases • where do they meet? • is the case base the only knowledge base in a CBR system?

31. Ontologies

32. What are ontologies (in AI)? • general view • a formalism that represents shared conceptualizations and their interrelations in a domain (or subdomain) using a common vocabulary • “Ontologies are explicit specifications of conceptualizations.” most cited definition from Gruber (1993) • Conceptualizations represent interpreted concepts

33. What are ontologies (in AI)? • specific view • an ontology is an explicit description of: • concepts (or classes) in a domain • properties of each concept describing various features and attributes • and restrictions on the attributes (facets)

34. shared, explicit, and conceptual • consensual knowledge • not private to one individual, accepted by a group • types and constraints are explicitly defined • conceptual (abstract) model of a domain through its relevant concepts

35. Types of Ontologies • Domain • Additional specializations are possible • applications, tasks • Linguistic • Account for grammar and meanings in a natural language e.g., WordNet for American English

36. Types of Information • concepts, atomic types • cardinality of constraints • is-a hierarchy among concepts • relationships between concepts • taxonomies of relations • reified statements • axioms • semantic entailments

37. Uses of domain ontologies • interoperability among information systems • semantic web: link, coordinate software agents • sharing knowledge bases among KBS • intelligent retrieval, search

38. Uses of domain ontologies • Further reading: • Weber, R. & Kaplan, R. (2003). Knowledge-based knowledge management. In Innovations in Knowledge Engineering, Editors: Ravi Jain, Ajith Abraham, Colette Faucher and Berend Jan van der Zwaag. International Series on Advanced Intelligence, Volume 4. July 2003. Advanced Knowledge International Pty Ltd. • http://www.pages.drexel.edu/~rw37/weberkaplan.pdf

39. Ontology Editors(development environments) • ONTOLINGUA http://ontolingua.nici.kun.nl • WEBONTO* http://kmi.open.ac.uk/projects/webonto/ • PROTEGEWIN http://smi-web.stanford.edu/projects/prot-nt/ • ONTOSAURUS* http://www.isi.edu/isd/ontosaurus.html • ODE • KADS22

40. Further reading on ontology editors Duineveld, A.J., Stoter, R., Weiden, M.R., Kenepa, B. and Benjamins, V.R. (2000). WonderTools? A comparative study of ontological engineering tools. International Journal of Human-Computer Studies 52(6): 1111-1133.

41. looking at some ontologies • kmi.open.ac.uk/projects/webontoOpen University • http://www.isi.edu/isd/ontosaurus.htmlUSC/Information Sciences Institute