CPECSC 481 KnowledgeBased Systems Dr Franz J Kurfess

Usage of the Slides ◆ these slides are intended for the students of my CPE/CSC 481 “Knowledge-Based Systems” class at Cal Poly SLO ◆ if you want to use them outside of my class, please let me know (fkurfess@calpoly. edu) ◆I usually put together a subset for each quarter as a “Custom Show” ◆ ◆ To to view these, go to “Slide Show => Custom Shows”, select the respective quarter, and click on “Show” print them, I suggest to use the “Handout” option 4, 6, or 9 per page works fine ◆ Black & White should be fine; there are few diagrams where color is important ◆ © 2002 -2011 Franz J. Kurfess Knowledge Representation

Course Overview u Introduction u Pattern u CLIPS u u Overview Concepts, Notation, Usage u Knowledge Representation u Semantic Nets, Frames, Logic u Reasoning u Predicate Logic, Inference Methods, Resolution u Reasoning u and Inference with Uncertainty Variables, Functions, Expressions, Constraints u Expert u Matching System Design ES Life Cycle u Expert System Implementation u Salience, Rete Algorithm u Expert System Examples u Conclusions and Outlook Probability, Bayesian Decision Making © 2002 -2011 Franz J. Kurfess Knowledge Representation

Overview Knowledge Representation u Knowledge u Motivation Methods u Objectives u Chapter u u u Introduction Review of relevant concepts Overview new topics Terminology u Knowledge and its Meaning u Epistemology u Types of Knowledge Pyramid u Representation © 2002 -2011 Franz J. Kurfess u u Production Rules Semantic Nets Schemata and Frames Logic u Semantic u u u Web and KR Ontologies OWL RDF u Important Concepts and Terms u Chapter Summary Knowledge Representation

Logistics u Term Project u Lab and Homework Assignments u Quizzes or Term Paper?

Motivation u KBS are useless without the ability to represent knowledge u different knowledge representation schemes may be appropriate u depending on tasks and circumstances u knowledge representation schemes and reasoning methods must be coordinated © 2002 -2011 Franz J. Kurfess Knowledge Representation

Objectives u know the basic principles and concepts for knowledge representation u u knowledge - information - data meaning u be familiar with the most frequently used knowledge representation methods u logic, rules, semantic nets, schemata v differences between methods, advantages, disadvantages, performance, typical scenarios u understand the relationship between knowledge representation and reasoning u u syntax, semantics derivation, entailment u apply knowledge representation methods u usage the methods © 2002 -2011 Franz J. of. Kurfess for simple problems Knowledge Representation

Knowledge and its Meaning u Epistemology u Types of Knowledge u Knowledge Pyramid ©

Epistemology u the science of knowledge EPISTEMOLOGY ( Gr. episteme, "knowledge"; logos, "theory"), branch of philosophy concerned with theory of knowledge. The main problems with which epistemology is concerned are the definition of knowledge and related concepts, the sources and criteria of knowledge, the kinds of knowledge possible and the degree to which each is certain, and the exact relation between the one who knows and the object known. [Infopedia 1996] © 2002 -2011 Franz J. Kurfess Knowledge Representation

Knowledge Definitions knowlaedge 'n. S-lij n [ME knowlege, fr. knowlechen to acknowledge, irreg. fr. knowen ] (14 c) 1 obs : cognizance 2 a (1) : the fact or condition of knowing something with familiarity gained through experience or association (2) : acquaintance with or understanding of a science, art, or technique b (1) : the fact or condition of being aware of something (2) : the range of one's information or understanding <answered to the best of my 4> c : the circumstance or condition of apprehending truth or fact through reasoning : cognition d : the fact or condition of having information or of being learned <a man of unusual 4> 3 archaic : sexual intercourse 4 a : the sum of what is known : the body of truth, information, and principles acquired by mankind b archaic : a branch of learning syn knowledge, learning, erudition, scholarship mean what is or can be known by an individual or by mankind. knowledge applies to facts or ideas acquired by study, investigation, observation, or experience <rich in the knowledge of human nature>. learning applies to knowledge acquired esp. through formal, often advanced, schooling <a book that demonstrates vast learning >. erudition strongly implies the acquiring of profound, recondite, or bookish learning <an erudition unusual even in a scholar>. scholarship implies the possession of learning characteristic of the advanced scholar in a specialized field of study or investigation <a work of first-rate literary scholarship >. © 2002 -2011 Franz J. Kurfess [Merriam-Webster, 1994] Knowledge Representation

Types of Knowledge ua priori knowledge u u ua comes before knowledge perceived through senses considered to be universally true posteriori knowledge u u knowledge verifiable through the senses may not always be reliable u procedural u knowing how to do something u declarative u knowledge knowing that something is true or false u tacit u knowledge not easily expressed by language © 2002 -2011 Franz J. Kurfess Knowledge Representation

Knowledge Pyramid Meta. Knowledge Information Data Noise © 2002 -2011 Franz J. Kurfess Knowledge

Knowledge Representation Methods u Production Rules u Semantic Nets u Schemata and Frames u

Production Rules u frequently used to formulate the knowledge in expert systems u a formal variation is Backus-Naur form (BNF) u metalanguage for the definition of language syntax u a grammar is a complete, unambiguous set of production rules for a specific language u a parse tree is a graphic representation of a sentence in that language u provides only a syntactic description of the language v not all sentences make sense © 2002 -2011 Franz J. Kurfess Knowledge Representation

Example 1 Parse Tree u Example sentence: man loves woman forever <sentence> <subject> <verb> <noun> man © 2002 -2011 Franz J. Kurfess <object> <modifier> <noun> loves woman forever Knowledge Representation

Suitability of Production Rules u expressiveness u can relevant aspects of the domain knowledge be stated through rules? u computational u are u easy the computations required by the program feasible? to understand? u can u easy efficiency humans interpret the rules to generate? u how difficult is it for humans to construct rules that reflect the domain knowledge © 2002 -2011 Franz J. Kurfess Knowledge Representation

Case Studies Production Rules u sample u domains e. g. theorem proving, determination of prime numbers, distinction of objects (e. g. types of fruit, trees vs. telephone poles, churches vs. houses, animal species) u suitability u basic production rules v u no salience, certainty factors, arithmetic rules in ES/KBS v v u of production rules salience, certainty factors, arithmetic e. g. CLIPS, Jess enhanced rules v procedural constructs v v objects v v e. g. loops e. g. COOL, Java objects fuzzy logic v e. g. Fuzzy. CLIPS, Fuzzy. J © 2002 -2011 Franz J. Kurfess Knowledge Representation

Trees and Telephone Poles u u distinguish between stick diagrams of trees and telephone poles expressiveness u u computational efficiency u u are the computations required by the program feasible? easy to understand? u u is it possible to specify a set of rules that captures the distinction? the rules can be phrased in such a way that humans can understand them with moderate effort easy to generate? u may be difficult; the problem is to identify criteria that are common for trees, but not shared with telephone poles © 2002 -2011 Franz J. Kurfess Knowledge Representation

Identification and Generation of Prime Numbers u identification: for a given number, determine if it is prime u generation: compute the sequence of prime numbers u expressiveness u it is possible to specify identification as well as generation in rules u computational u reasonable if arithmetic is available, very poor if not u easy u to understand? the rules can be formulated in an understandable way u easy u efficiency to generate? may require a good math background © 2002 -2011 Franz J. Kurfess Knowledge Representation

Advantages of Production Rules u simple and easy to understand u straightforward implementation in computers possible u formal foundations for some variants © 2002 -2011 Franz J. Kurfess Knowledge Representation

Problems with Production Rules u simplementations are very inefficient u some types of knowledge are not easily expressed in such rules u large sets of rules become difficult to understand maintain © 2002 -2011 Franz J. Kurfess Knowledge Representation

Semantic Nets u graphical representation for propositional information u originally developed by M. R. Quillian as a model for human memory u labeled, directed graph u nodes represent objects, concepts, or situations u u labels indicate the name nodes can be instances (individual objects) or classes (generic nodes) u links u u represent relationships the relationships contain the structural information of the knowledge to be represented the label indicates the type of the relationship © 2002 -2011 Franz J. Kurfess Knowledge Representation

Semantix Net Example Abraracourcix Astérix is f -b o s s -o o f s s o is-b of retak es -ca AKO -from a is - [http: //www. asterix. tm. fr] Dog a Ordralfabetix Obélix is-a is- s e Human liv h wit f -o buys nd i barks-at © 2002 -2011 Franz J. Kurfess ie th -to -fr s-wi lls a s Gaul is-a se is fight Panoramix isa a is- Cétautomatix Idéfix Knowledge Representation

Semantix Net Cheats u colors u should properly be encoded as separate nodes with relationships to the respective objects u font u u types implies different types of relationships again would require additional nodes and relationships u class u u relationships not all dogs live with Gauls AKO (a-kind-of) relationship is special (inheritance) u instances u arrows from individual humans to the class Human omitted v assumes that AKO allows inheritance u directionality u the direction of the arrows matters, not that of the text © 2002 -2011 Franz J. Kurfess Knowledge Representation

Relationships u without relationships, knowledge is an unrelated collection of facts u reasoning v about these facts is not very interesting inductive reasoning is possible u relationships express structure in the collection of facts u this v v allows the generation of meaningful new knowledge generation of new facts generation of new relationships © 2002 -2011 Franz J. Kurfess Knowledge Representation

Types of Relationships u relationships can be arbitrarily defined by the knowledge engineer u allows great flexibility u for reasoning, the inference mechanism must know how relationships can be used to generate new knowledge v inference methods may have to be specified for every relationship u frequently used relationships u IS-A v relates an instance (individual node) to a class (generic node) u AKO v (a-kind-of) relates one class (subclass) to another class (superclass) © 2002 -2011 Franz J. Kurfess Knowledge Representation

Objects and Attributes u attributes provide more detailed information on nodes in a semantic network u often v expressed as properties combination of attribute and value u attributes v can be expressed as relationships e. g. has-attribute © 2002 -2011 Franz J. Kurfess Knowledge Representation

Implementation Questions u simple and efficient representation schemes for semantic nets u tables that list all objects and their properties u tables or linked lists for relationships u conversion u predicate v v into different representation methods logic nodes correspond variables or constants links correspond to predicates u propositional v logic nodes and links have to be translated into propositional variables and properly combined with logical connectives © 2002 -2011 Franz J. Kurfess Knowledge Representation

OAV-Triples u object-attribute-value u can triplets be used to characterize the knowledge in a semantic net u quickly leads to huge tables Object Attribute Value Astérix profession warrior Obélix size extra large Idéfix size petite Panoramix wisdom infinite © 2002 -2011 Franz J. Kurfess Knowledge Representation

Problems Semantic Nets u expressiveness u u u no internal structure of nodes relationships between multiple nodes no easy way to represent heuristic information extensions are possible, but cumbersome best suited for binary relationships u efficiency u u may result in large sets of nodes and links search may lead to combinatorial explosion v especially for queries with negative results u usability u u lack of standards for link types naming of nodes v classes, instances © 2002 -2011 Franz J. Kurfess Knowledge Representation

Schemata u suitable for the representation of more complex knowledge u causal relationships between a percept or action and its outcome u “deeper” knowledge than semantic networks u nodes can have an internal structure u for humans often tacit knowledge u related to the notion of records in computer science © 2002 -2011 Franz J. Kurfess Knowledge Representation

Concept Schema u abstraction of objects that captures general/typical properties u has the most important properties that one usually associates with an object of that type u may be dependent on task, context, background and capabilities of the user, … u similar to stereotypes u makes reasoning simpler by concentrating on the essential aspects u may still require relationship-specific inference methods © 2002 -2011 Franz J. Kurfess Knowledge Representation

Schema Examples u the most frequently used instances of schemata are u frames [Minsky 1975] u scripts [Schank 1977] u frames consist of a group of slots and fillers to define a stereotypical objects u scripts are time-ordered sequences of frames © 2002 -2011 Franz J. Kurfess Knowledge Representation

Frame u represents u provides default values for most slots u frames u related knowledge about a subject are organized hierarchically allows the use of inheritance u knowledge is usually organized according to cause and effect relationships u slots can contain all kinds of items u u rules, facts, images, video, comments, debugging info, questions, hypotheses, other frames slots can also have procedural attachments u procedures that are invoked in specific situations involving a particular slot u on creation, modification, removal of the slot value © 2002 -2011 Franz J. Kurfess Knowledge Representation

Simple Frame Example Slot Name Filler name Astérix height small weight low profession warrior armor helmet intelligence very high marital status presumed single © 2002 -2011 Franz J. Kurfess Knowledge Representation

Overview of Frame Structure u two basic elements: slots and facets (fillers, values, etc. ); u typically have parent and offspring slots u used to establish a property inheritance hierarchy (e. g. , specialization-of) u descriptive u contain declarative information or data (static knowledge) u procedural u slots attachments contain functions which can direct the reasoning process (dynamic knowledge) (e. g. , "activate a certain rule if a value exceeds a given level") u data-driven, event-driven ( bottom-up reasoning) u expectation-drive or top-down reasoning u pointers to related frames/scripts - can be used to transfer control to a more appropriate frame © 2002 -2011 Franz J. Kurfess [Rogers 1999] Knowledge Representation

Usage of Frames u filling slots in frames u can inherit the value directly u can get a default value u these two are relatively inexpensive u can derive information through the attached procedures (or methods) that also take advantage of current context (slotspecific heuristics) u filling in slots also confirms that frame or script is appropriate for this particular situation © 2002 -2011 Franz J. Kurfess [Rogers 1999] Knowledge Representation

Restaurant Frame Example u generic template for restaurants u different types u default values u script for a typical sequence of activities at a restaurant © 2002 -2011 Franz J. Kurfess [Rogers 1999] Knowledge Representation

Generic RESTAURANT Frame Generic Restaurant Frame Specialization-of: Business-Establishment Types: range: (Cafeteria, Fast-Food, Seat-Yourself, Wait-To-Be-Seated) default: Seat-Yourself if-needed: IF plastic-orange-counter THEN Fast-Food, IF stack-of-trays THEN Cafeteria, IF wait-for-waitress-sign or reservations-made THEN Wait-To-Be-Seated, OTHERWISE Seat-Yourself. Location: range: an ADDRESS if-needed: (Look at the MENU) Name: if-needed: (Look at the MENU) Food-Style: range: (Burgers, Chinese, American, Seafood, French) default: American if-added: (Update Alternatives of Restaurant) Times-of-Operation: range: a Time-of-Day default: open evenings except Mondays Payment-Form: range: (Cash, Credit. Card, Check, Washing-Dishes-Script) Event-Sequence: default: Eat-at-Restaurant Script Alternatives: range: all restaurants with same Foodstyle if-needed: (Find all Restaurants with the same Foodstyle) © 2002 -2011 Franz J. Kurfess [Rogers Knowledge 1999] Representation

Restaurant Script EAT-AT-RESTAURANT Script Props: (Restaurant, Money, Food, Menu, Tables, Chairs) Roles: (Hungry-Persons, Wait-Persons, Chef-Persons) Point-of-View: Hungry-Persons Time-of-Occurrence: (Times-of-Operation of Restaurant) Place-of-Occurrence: (Location of Restaurant) Event-Sequence: first: Enter-Restaurant Script then: if (Wait-To-Be-Seated-Sign or Reservations) then Get-Maitre-d's-Attention Script then: Please-Be-Seated Script then: Order-Food-Script then: Eat-Food-Script unless (Long-Wait) when Exit-Restaurant-Angry Script then: if (Food-Quality was better than Palatable) then Compliments-To-The-Chef Script then: Pay-For-It-Script finally: Leave-Restaurant Script © 2002 -2011 Franz J. Kurfess [Rogers 1999] Knowledge Representation

Frame Advantages u fairly intuitive for many applications u similar to human knowledge organization u suitable for causal knowledge u easier to understand than logic or rules u very flexible © 2002 -2011 Franz J. Kurfess Knowledge Representation

Frame Problems u it is tempting to use frames as definitions of concepts u not appropriate because there may be valid instances of a concept that do not fit the stereotype u exceptions can be used to overcome this v can get very messy u inheritance u not all properties of a class stereotype should be propagated to subclasses u alteration of slots can have unintended consequences in subclasses © 2002 -2011 Franz J. Kurfess Knowledge Representation

Logic u here: emphasis on knowledge representation purposes u logic and reasoning is discussed

Representation, Reasoning and Logic u two parts to knowledge representation language: u syntax v describes the possible configurations that can constitute sentences u semantics v v determines the facts in the world to which the sentences refer tells us what the agent believes © 2002 -2011 Franz J. Kurfess [Rogers 1999] Knowledge Representation

Reasoning u process of constructing new configurations (sentences) from old ones u proper reasoning ensures that the new configurations represent facts that actually follow from the facts that the old configurations represent u this relationship is called entailment and can be expressed as KB |= alpha v knowledge base KB entails the sentence alpha © 2002 -2011 Franz J. Kurfess [Rogers 1999] Knowledge Representation

Inference Methods u an inference procedure can do one of two things: given a knowledge base KB, it can derive new sentences α that are (supposedly) entailed by KB KB |- α ==> KB |= α u given a knowledge base KB and another sentence alpha, it can report whether or not alpha is entailed by KB KB ∧ α ==> KB |= α u u an inference procedure that generates only entailed sentences is called sound or truth-preserving u the record of operation of a sound inference procedure is called a proof u an inference procedure is complete if it can find a proof for any sentence that is entailed © 2002 -2011 Franz J. Kurfess [Rogers 1999] Knowledge Representation

KR Languages and Programming Languages u how is a knowledge representation language different from a programming language (e. g. Java, C++)? u programming languages can be used to express facts and states u what about "there is a pit in [2, 2] or [3, 1] (but we don't know for sure)" or "there is a wumpus in some square" u programming languages are not expressive enough for situations with incomplete information u we only know some possibilities which exist © 2002 -2011 Franz J. Kurfess [Rogers 1999] Knowledge Representation

KR Languages and Natural Language u how is a knowledge representation language different from natural language u e. g. English, Spanish, German, … u natural languages are expressive, but have evolved to meet the needs of communication, rather than representation u the meaning of a sentence depends on the sentence itself and on the context in which the sentence was spoken u e. g. “Look!” u sharing of knowledge is done without explicit representation of the knowledge itself u ambiguous (e. g. small dogs and cats) © 2002 -2011 Franz J. Kurfess [Rogers 1999] Knowledge Representation

Good Knowledge Representation Languages u combines u u expressive concise unambiguous independent of context v u what you say today will still be interpretable tomorrow efficient v v u the best of natural and formal languages: the knowledge can be represented in a format that is suitable for computers practical inference procedures exist for the chosen format effective v there is an inference procedure which can act on it to make new sentences © 2002 -2011 Franz J. Kurfess [Rogers 1999] Knowledge Representation

Example: Representation Methods [Guinness 1995]J. Kurfess © 2002 -2011 Franz Knowledge Representation

Ontologies u principles v definition of terms v v relationships between terms v u establishing a common vocabulary for a domain graphical representation v u taxonomy, thesaurus purpose v u lexicon, glossary UML, topic maps, examples v IEEE SUO, SUMO, Cyc, Word. Net © 2002 -2011 Franz J. Kurfess Knowledge Representation

Terminology u ontology u u u lexicon u u provides semantics for concepts words are used as descriptors for concepts provides semantics for all words in a language by defining words through descriptions of their meanings thesaurus u establishes relationships between words v u u synonyms, homonyms, antonyms, etc. often combined with a taxonomy u u hierarchical arrangement of concepts often used as a “backbone” for an ontology © 2002 -2011 Franz J. Kurfess Knowledge Representation

What is the Semantic Web? u Based on the World Wide Web u Characterized by resources, not text and images u Meant for software agents, not human viewers u Defined by structured documents that reference each other, forming potentially very large networks u Used to simulate knowledge in computer systems u Semantic Web documents can describe just about anything humans can communicate about © 2002 -2011 Franz J. Kurfess Knowledge Representation

Ontologies and the Semantic Web u Ontologies are large vocabularies u Defined within Semantic Web documents (OWL) u Define languages for other documents (RDF) u Resources can be instances of ontology classes u Upper Ontologies define basic, abstract concepts u Lower Ontologies define domain-specific concepts u Meta-ontologies define ontologies themselves © 2002 -2011 Franz J. Kurfess Knowledge Representation

Ontology Terms u precision ua term identifies exactly one concept u expressiveness u the representation language allows the formulation of very flexible statements u descriptors for concepts u ideally, there should be a one-to-one mapping between a term and the associated concept (and vice versa): high precision, and high expressiveness v v this is not the case for natural languages “parasitic interpretation” of terms often implies meaning that is not necessarily specified in the ontology © 2002 -2011 Franz J. Kurfess Knowledge Representation

IEEE Standard Upper Ontology u u project to develop a standard for ontology specification and registration based on contributions of three SUO candidate projects u u IFF Open. Cyc/Cyc. L SUMO Standard Upper Ontology Working Group (SUO WG), Cumulative Resolutions, 2003, http: //suo. ieee. org/SUO/resolutions. html © 2002 -2011 Franz J. Kurfess Knowledge Representation

Open. Cyc u derived ua from the development of Cyc very large-scale knowledge based system u Cycorp, The Syntax of Cyc. L, 2002, http: //www. cyc. com/cycdoc/ref/cycl-syntax. html © 2002 -2011 Franz J. Kurfess Knowledge Representation

SUMO u stands for “Suggested Upper Merged Ontology” u Niles, Ian, and Adam Pease, Towards a Standard Upper Ontology, 2001 u Standard Upper Ontology Working Group (SUO WG), Cumulative Resolutions, 2003, http: //suo. ieee. org/SUO/resolutions. html © 2002 -2011 Franz J. Kurfess Knowledge Representation

Word. Net u online lexical reference system u design is inspired by current psycholinguistic theories of human lexical memory u English nouns, verbs, adjectives and adverbs u organized into synonym sets, each representing one underlying lexical concept u related efforts for other languages © 2002 -2011 Franz J. Kurfess Knowledge Representation

Lojban u u artificial, logical, human language derived from a language called Loglan one-to-one correspondence between concepts and words u u u high expressiveness audio-visually isomorphic nature u u u high precision only one way to write a spoken sentence only one way to read a written sentence Logical Language Group, Official Baseline Statement, 2005 u http: //www. lojban. org/llg/baseline. html © 2002 -2011 Franz J. Kurfess Knowledge Representation

What is Lojban? u. A constructed/artificial language u Developed from Loglan u Dr. James Cooke Brown u Introduced between 1955 -1960 u Maintained by The Logical Language Group u also known as la lojbangirz u branched Lojban off from Loglan in 1987 © 2002 -2011 Franz J. Kurfess [Brandon Wirick, 2005] Knowledge Representation

Main Features of Lojban u Usable by Humans and Computers u Culturally Neutral u Based on Logic u Unambiguous but Flexible u Phonetic Spelling © 2002 -2011 Franz J. Kurfess u Easy to Learn u Large Vocabulary u No Exceptions u Fosters Clear Thought u Variety of Uses u Demonstrated with Prose and Poetry [Brandon Wirick, 2005] Knowledge Representation

Lojban at a Glance Example sentence in English: “Wild dogs bite. ” Translation into Lojban: “loi cicyge'u cu batci” cilce (cic) - x 1 is wild/untamed gerku (ger, ge'u) - x 1 is a dog/canine of species/breed x 2 batci (bat) - x 1 bites/pinches x 2 on/at specific locus x 3 with x 4 cilce gerku → (cic) (ge'u) → cicyge'u © 2002 -2011 Franz J. Kurfess [Brandon Wirick, 2005] Knowledge Representation

Lojban and the Semantic Web u Currently, most upper ontologies use English u Not really English, but arbitrary class names u Classes’ meanings cannot be directly inferred from their names, nor vice-versa u Translating English prose into Semantic Web documents can be difficult u Class choices depend on context within prose u English prose is highly idiomatic u Lojban does not have these problems © 2002 -2011 Franz J. Kurfess [Brandon Wirick, 2005] Knowledge Representation

English v. Lojban © 2002 -2011 Franz J. Kurfess [Brandon Wirick, 2005] Knowledge Representation

OWL to the Rescue u XML-based. RDF on steroids. u Designed for inferencing. u Closer to the domain. u Don’t need a Ph. D to understand it. u Information sharing. u u u RDF-compatible because it is RDF. Growing number of published OWL ontologies. URIs make it easy to merge equivalent nodes. u Different u u u levels OWL lite OWL DL (description logics) OWL full (predicate logic) © 2002 -2011 Franz J. Kurfess [Frank Vasquez, 2005] Knowledge Representation

Description Logic u Classes u Things, categories, concepts. u Inheritance hierarchies via subclasses. u Properties u Relationships, predicates, statements. u Can have subproperties. u Individuals u Instances of a class. u Real subjects and objects of a predicate. © 2002 -2011 Franz J. Kurfess [Frank Vasquez, 2005] Knowledge Representation

Visualizing the Data Model u Venn Diagrams and Semantic Networks. Images from University of

RDF Ontologies u u u u Dublin Core FOAF RDF v. Card RDF Calendar

Fixing Modeling Conflicts 1. map. AL = Match(MA, ML) © 2002 -2011 Franz J.

Important Concepts and Terms u u u u attribute common-sense knowledge concept data derivation entailment epistemology expert system (ES) expert system shell facet frame graph If-Then rules inference mechanism information u © 2002 -2011 Franz J. Kurfess u u u u u knowledge base knowledge-based system knowledge representation link logic meta-knowledge node noise object production rules reasoning relationship rule schema script semantic net slot Knowledge Representation

Summary Knowledge Representation u knowledge representation is very important for knowledge-based system u popular knowledge representation schemes are u rules, semantic nets, schemata (frames, scripts), logic u the selected knowledge representation scheme should have appropriate inference methods to allow reasoning u a balance must be found between u effective representation, efficiency, understandability © 2002 -2011 Franz J. Kurfess Knowledge Representation