K NOWLEDGE R EPRESENTATION

1. LECTURE NOTESKNOWLEDGE REPRESENTATIONKhurshid AhmadProfessor of Artificial IntelligenceDepartment of ComputingUniversity of Surrey

2. KNOWLEDGE REPRESENTATION ‘The idea of explicit representations of knowledge, manipulated by general purpose inference algorithms, dates back to the philosopher Leibniz, who envisioned a calculus of propositions that exceed in its scope and power the differential calculus he has developed’ (Brachman, Levesque and Reiter 1991:1)

3. KNOWLEDGE REPRESENTATION 'A representation is a set of conventions about how to describe a class of things. A description makes use of the conventions of a representation to describe some particular thing.' (Winston 1992:16). ‘Good representations make important objects and relations explicit, expose natural constraints, and bring objects and relations together’ (ibid: 45) The representation principle Once a problem is described using an appropriate representation, the problem is almost solved.

4. KNOWLEDGE REPRESENTATION The Farmer, The Fox, The Goose and The Grain The farmer must get a fox, a goose and a sack of grain across a river, however his boat is small and he can only carry one thing at a time. His problem is that if he leaves the fox with the goose the goose will be eaten, and if he leaves the goose with the grain, the grain will be eaten. . . .

5. KNOWLEDGE REPRESENTATION • The Farmer, The Fox, The Goose and The Grain • A good representation makes it easier for us to solve the problem: • Draw possible safe combinations in a diagram. • Arrange appropriate combinations in order. • Link appropriate arrangements to represent boat trips. • Problem is solved!

6. Grain Fox Farmer Goose Farmer Goose Grain Fox Farmer Fox Goose Grain Fox Grain Farmer Goose Farmer Fox Grain Goose Fox Farmer Goose Grain Farmer Fox Goose Grain Goose Farmer Fox Grain Farmer Goose Fox Grain Farmer Goose Fox Grain

7. KNOWLEDGE REPRESENTATION A number of knowledge representation schemes (or formalisms) have been used to represent the knowledge of humans in a systematic manner. This knowledge is represented in a KNOWLEDGE BASE such that it can be retrieved for solving problems. Amongst the well-established knowledge representation schemes are: • Production Rules • Semantic Networks • Frames • Conceptual Dependency Grammar • Conceptual Graphs • Predicate and Modal Logic • Conceptual or Terminological Logics • XML / RDF

8. KNOWLEDGE REPRESENTATION These schemes can be classified under three headings: • Procedural Schemes • Production Rules • Propositional Schemes • Semantic Nets; Conceptual Dependency Grammar, • Conceptual Graphs; Logics; Frames • Analogical Schemes • Matrices

9. KNOWLEDGE REPRESENTATION A Brief History of Knowledge Representation 1960's: Taxonomy, inheritance and knowledge 'networks‘ 1970's: Structuring the semantic network & the rise of logic 1980's: 'Semantic networks' with semantics & logic for change 1990's: Meta-knowledge representation, belief representation

10. KNOWLEDGE REPRESENTATION A Brief History of Knowledge Representation (1) 1960's: Taxonomy, inheritance and knowledge 'networks‘ Semantic Nets, Frames, Predicate Logic 1970's: Structuring the semantic network & the rise of logic Structured Semantic Networks Logic for Problem Solving: Program = Logic + Control Fuzzy Logic and Uncertainty Representation

11. KNOWLEDGE REPRESENTATION A Brief History of Knowledge Representation (2) 1980's: 'Semantic networks' with semantics & logic for change The 'epistemologically explicit' KL-ONE language; Temporal Logic, Deviant Logic, Non-monotonic Logics  1990's: Meta-knowledge representation, belief representation Theoretically well-grounded networks Representing Belief Default Logics, Temporal reasoning Mixed representation systems

12. KNOWLEDGE REPRESENTATION: 1960’S NETWORKS & MEANING • Ross Quillian (1966 and 1968) was among the early AI workers to develop a computational model which represented 'concepts' as hierarchical networks. • This model was amended with some additional psychological assumptions to characterise the structure of [human] semantic memory.

13. KNOWLEDGE REPRESENTATION: 1960’S NETWORKS & MEANING • Collins and Quillian (1969) proposed that: • Concepts can be represented as hierarchies of inter- connected concept nodes (e.g. animal, bird, canary) • Any concept has a number of associated attributes at a given level ( e.g. animal --> has skin; eats etc.) • Some concept nodes are superordinates of other nodes (e.g. animal >bird) and some are subordinates • (canary< bird) Continued . . .

14. KNOWLEDGE REPRESENTATION: 1960’S NETWORKS & MEANING (2) • . . . Continued • For reasons of cognitive economy, subordinates inherit all the attributes of their superordinate concepts • • Some instances of a concept are excepted from the attributes that help [humans] to define the superordinates (e.g. ostrich is excepted from flying) • Various [psychological] processes search these hierarchies for information about the concepts represented

15. canary can sing, is yellow bird can fly, has wings, has feathers is-a ostrich runs fast, cannot fly, is tall animal can breathe, can eat, has skin is-a is-a salmon lays eggs; swims upstream, is pink, is edible fish can swim, has fins, has gills is-a is-a KNOWLEDGE REPRESENTATION : 1960’S NETWORKS & MEANING • A Hierarchical Network

16. canary can sing, is yellow is-a bird can fly, has wings, has feathers ostrich runs fast, cannot fly, is tall animal can breathe, can eat, has skin is-a is-a salmon lays eggs; swims upstream, is pink, is edible fish can swim, has fins, has gills is-a is-a KNOWLEDGE REPRESENTATION: 1960’S NETWORKS & MEANING • From the above taxonomic organisation of knowledge about a number of different animals, one can conclude, by ‘inheriting properties down thetaxonomy’, that canaries, ostriches and salmon all have skin and can breathe. • But we as humans can also make exceptions to inherited properties in that we can represent an unflighted bird in a (sub-) hierarchy of birds by simply noting the exception, 'can't fly'.

17. canary can sing, is yellow is-a bird can fly, has wings, has feathers ostrich runs fast, cannot fly, is tall animal can breathe, can eat, has skin is-a is-a salmon lays eggs; swims upstream, is pink, is edible fish can swim, has fins, has gills is-a is-a KNOWLEDGE REPRESENTATION: 1960’S NETWORKS & MEANING Collins and Quillian carried out a number of tests on human subjects and found that the subjects recognise propositions lower down the hierarchy (canary is a yellow bird) more readily than propositions higher up the hierarchy (canary has skin).

18. canary can sing, is yellow is-a bird can fly, has wings, has feathers ostrich runs fast, cannot fly, is tall animal can breathe, can eat, has skin is-a is-a salmon lays eggs; swims upstream, is pink, is edible fish can swim, has fins, has gills is-a is-a KNOWLEDGE REPRESENTATION: 1960’S NETWORKS & MEANING • A semantic network is a structure for representing knowledge as a pattern of interconnected nodes and arcs.Nodes in the net represent concepts of entities, attributes, events, values. Arcs in the network represent relationships that hold between the concepts

19. C111 C111’s attributes is-a C11 C11’s attributes C112 C112’s attributes C1 C1’s attributes is-a is-a C121 C121’s attributes C12 C12’s attributes is-a is-a KNOWLEDGE REPRESENTATION: 1960’S NETWORKS & MEANING • Concepts labeled C111 and C112 inherit all the attributes of C11 which, in turn, inherits all the attributes of C1; similarly C121 inherits attributes of C12 and C12 of C1. All arcs are labeled is-a, which relates superordinates (C1) to subordinates (C11, C12) to instances (C111, C112, C121).

20. KNOWLEDGE REPRESENTATION: 1960’S NETWORKS & MEANING Quillian’s semantic network: A graph theoretic data structure whose nodes represent word senses and whose arcs express binary semantic relationships between these word senses. Quillian gave an account, perhaps first used by a computer scientist, of the associate features of human memory that incorporated a spreading activation model of computation.

21. canary can sing, is yellow is-a bird can fly, has wings, has feathers ostrich runs fast, cannot fly, is tall animal can breathe, can eat, has skin is-a is-a salmon lays eggs; swims upstream, is pink, is edible fish can swim, has fins, has gills EAT EAT Problem! Although called semantic nets there are no clear semantics of the network representations The above network is identical to the previous example, but NOW is interpreted as “Salmon eat fish” and Fish eat animals”

22. Representation in a Semantic Net Would be represented in logic as: is_a(person, mammal), instance(Chris, person), team(Chris, Ferrari), team_colour(Chris, red), has_part(person, head), type(head, bald)

23. Game Is_a Spurs Fixture 5 3 - 1 Away_team Score Home_team Norwich Representation in a Semantic Net How represent predicates with more than two places (e.g. score (Norwich, Spurs, 3 – 1)? Create new node(s) to represent objects contained, or alluded to, in the original semantic net.

24. Gave Book Action Instance Agent Object John Event 1 Book_69 Patient Mary A More Complicated Example “John gave Mary the book”

25. Chris John Height Height 1.9 1.6 Relating Entities (1) Representing the height of two people:

26. Chris John Height Height H1 H2 Greater_than Value Value 1.9 1.6 Relating Entities (2) Comparing the height of two people: We need extra nodes for the concept as well as its value.

27. KNOWLEDGE REPRESENTATION: 1960’S NETWORKS & MEANING 1960’s: Networks and 'Meaning' Representation The biosystematic notions of taxonomies, where the concept of superordinates, like kingdoms, phylla and families plays a major role, and links like these have with subordinates instances based on colour, geography and so on, has had a substantial influence on the knowledge representation literature.

28. KNOWLEDGE REPRESENTATION: 1960’S NETWORKS & MEANING 1960’s: Networks and 'Meaning' Representation TAXONOMY OF LIFE The taxonomic organisation of species is hierarchical: Kingdom > Phylum (division in botany) > Class > Order > Family > Genus > Species Carolus Linneaus (c.18th century Swedish botanist) devised the system of binomial nomenclature used for naming species: each species has a two-part Latin name, formed by appending a specific epithet to the genus name. The latter is capitalised and both parts italicised.

29. KNOWLEDGE REPRESENTATION: 1960’S NETWORKS & MEANING Modern taxonomy recognises five kingdoms, into which the five million species of the world are organised:

30. KNOWLEDGE REPRESENTATION: 1960’S NETWORKS & MEANING Work in knowledge representation has been influenced by key notions in biosystematics. However, there are crucial differences between what a taxonomist does and a knowledge engineer does. The key difference is that of the intended audience in the two cases: for the taxonomist the audience is intelligent and human and for the knowledge engineer the primary 'audience' is a computer system, or more accurately the representation program.

31. KNOWLEDGE REPRESENTATION: 1960’S NETWORKS & MEANING Inheritance AI researchers have refined the notion of inheritance: It is called a specialised inferencing technique ‘for representing properties of classes, exceptions to inherited properties, multiple superclasses, and structured concepts with specific relations among the structural elements’ (Touretzky 1992:690).