INFO 629 Dr. R. Weber Copyright R. Weber Knowledge representation methods Knowledge bases, case...

INFO 629 Dr. R. Weber

Copyright R. Weber

Knowledge representation methods

Knowledge bases, case bases, databases


Copyright R. Weber

Outline• Data, information, knowledge• Knowledge representation methods

– characteristics, advantages, disadvantages

• Knowledge bases, case bases, databases– databases– case bases– knowledge bases (e.g., ontologies)


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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


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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

Computational Knowledge


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Decision Making and Problem Solving

gathering information

of alternate strategies

the best strategy

implement

monitor

When is knowledge absolutely

necessary ??Can you

perform it without using knowledge?

informationknowledgeknowledgeinformationinformationCan you perform it without

using knowledge?


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How do you represent knowledge?

How do you represent computational knowledge?


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Knowledge representation formalisms

• rules• frames• cases, MOPs • similarity measures• semantic nets• Bayesian nets• intelligent agents

• trees, e.g. parsing, decision trees• graphs• conceptual graphs• logic• neural nets• concepts, objects, facts• taxonomies, ontologies


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Knowledge representation formalisms can be used with

different inferencing methodologies or algorithms to

perform intelligent (AI) tasks


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Metrics for representation formalisms

• representational adequacy• inferential adequacy• inferential efficiency• clear syntax and semantics• naturalness


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Logic• truth preserving inference• ability to recognize negation, ordering,

disjunction and quantification• precise and formal language to represent

declarative knowledge• represents semantics


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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)”


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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


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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


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(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.


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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”.


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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


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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


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Knowledge containers in case-based reasoning systems


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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


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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”.


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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


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Knowledge bases, case bases, databases

• Databases• Case bases• Knowledge bases:

– rule-based ES– ontologies


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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)


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


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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


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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?


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Ontologies


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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


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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)


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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


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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


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Types of Informationconcepts, atomic typescardinality of constraintsis-a hierarchy among

conceptsrelationships between

concepts

taxonomies of relations

reified statementsaxiomssemantic

entailments


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Uses of domain ontologies•interoperability among information systems

•semantic web: link, coordinate software agents

•sharing knowledge bases among KBS•intelligent retrieval, search


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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


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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


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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.


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looking at some ontologies

• kmi.open.ac.uk/projects/webontoOpen University

• http://www.isi.edu/isd/ontosaurus.html USC/Information Sciences Institute

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