Artificial Intelligence Ontological engineering

1. Artificial IntelligenceOntological engineering Fall 2008 professor: Luigi Ceccaroni

2. Ontological engineering • What content to put into an agent’s knowledge base? • How to represent facts about the world? • An ontology organizes everything in the world into a hierarchy of categories. • Categories are the primary building blocks of any large-scale knowledge representation scheme. • Ontologies are an evolution of semantic networks and frames. 2

3. Reasoning systems for categories • Semantic networks are systems specially designed for organizing and reasoning with categories. • They provide: • graphical aid for visualizing a knowledge base • efficient algorithms for inferring properties of an object on the basis of its category membership. • They are an evolution of existential graphs (Charles Peirce, 1909) 3

4. Reasoning systems for categories • Another closely related family of systems are description logics, which provide: • a formal language for constructing and combining category definitions • efficient algorithms for deciding subset and superset relationships between categories. 4

5. Semantic networks • Logic, as representation language, has practical difficulties. • More intuitive and easy to use representation schemes are necessary. • According to cognitive psychology, knowledge representation and retrieval is based on its relations and this is formalized, for example, through semantic networks. • A semantic network is a graph in which: • nodes represent concepts; • (directed) arcs represent relations between concepts.

6. Semantic networks • They allow to represent the elements of a domain in a declarative way. • Specific reasoning mechanisms can be established, which allow to answer questions on the representation: • Are two concepts related? • What relates two concepts? • Which is the closest concept that relates two other concepts? • If a richer semantics is defined for relations, more complex questions can be answered about: • taxonomy among concepts (class, subclass, instance) • generalization / specialization

7. Limitations and problems • Many different formalisms exist, with different capabilities. • There is no standard reasoning model. • Difficulties exist associated to the mechanisms of property inheritance: • values of properties inherited by different parent nodes can be in conflict. • It is difficult (but not impossible) to express disjunctions (and therefore implications) and negations.

8. Frames • Frames are a version of semantic networks proposed by Marvin Minsky in 1975. • A frame is a representation of an object or concept, with descriptors (slots) and relations to other objects or concepts. • Relations and slots have a structure, too, which helps to describe their semantics. • A procedural part can be associated to frames.

9. Frames: example • frame Person super-classes: Class names: ages: dates-of-birth: sexes: readings: sexual-preferences: TV-preferences:

10. Frames: elements • A frame is usually made of a declarative part (slots) and a procedural one (demons o methods). • The demons allow to obtain information about a frame or to make operations about its characteristics and relations. • The description of the slots is structured, too. • Slot’s descriptors are called facets.

11. Frames: elements • Relations allow to connect frames. • Relations have a description that defines their semantics and functioning. • Slot inheritance is based on relations. • Relations are divided into two simple classes: • taxonomic relations: IS-A (subclass/class) and INSTANCE-OF (instance/class) • non-taxonomic relations

12. Slots • Slots describe the characteristics of frames. • Their facets (e.g., domain, range, cardinality, default value) allow to define their semantics. • They are where the demons are defined. • Demons can be of the following types: • if-needed (They activate when the slot is read.) • if-added (They activate when a value is added to the slot.) • if-removed (They activate when a value is removed from the slot.) • if- modified (They activate when a slot’s value is modified.)

13. Slots: description • Slot creation-time-stamp spanish-name value domain(list of frames where it can appear) range(type of values it admits: list, class…) max-cardinality min-cardinality(if ≥ 1, the slot is obligatory) default-value (to be used if value is empty) function-to-calculate-value demons (functions with conditional activation) inheritance conditions (slot + value) (yes/no) (default: tax. r. = yes; non-tax. r. = no) To access the value of a slot, the following syntax is used: <name frame>.<name slot> (value or list of values)

14. Slots: example • Slot age spanish-name: edad value: 28 domain: class Person range: integer [0..140] max-cardinality: 1 min-cardinality: 0 default-value: function-to-calculate-value:

15. Slots: demons • Demons are inheritable (can be invoked from within descendants) or non inheritable (can be solely invoked from within the frame where they are defined) actions or functions. • Example of if-modified demon: • If Deunan.age has value of 28 and is modified to 32, the demon activates and change the value of the slot Deunan.desire-to-get-married from 1 to 5. • Demons can be invoked with or without parameters. • Predefined Boolean functions: • <slot-1>?(<frame>): returns true if <frame> has the slot <slot-1>

16. Relations • Their semantics is defined through a set of properties. • Procedural demons can be defined within relations, too. They activate in correspondence to certain events: • if-added: when the relation is established among frames; • if-removed: when the relation is removed. • The behavior of relations with respect to inheritance can be defined, i.e., which slots they allow to inherit.

17. Relations: description • Relation is-adjacent-to spanish-name domain(list of frames) range(list of frames) cardinality(1 or N) inverse <name> (cardinality: 1 or N) transitivity yes/no (default is no) composition (no / description of composition) demons ({if-added / if-removed} action <name.action>) conditions of inheritance (list of slots) (default: empty list) • Actions associated to demons have no parameters. • The expression <name frame>.<name relation> returns the frame (if cardinality is 1) or the list of frames (if cardinality is N) with which <name frame> is connected via <name relation>.

18. Relations: description • To consult the cardinality, the function card(<name frame>.<name relation>) is used, which returns an integer. • Predefined relations: • IS-A (inverse: has-subtype) • transitive • INSTANCE-OF (inverse: has-instance) • It can be obtained by composition: INSTANCE-OF ⊕ IS-A • Predefined Boolean functions: • <relation-1>?(<frame>): returns true if <frame> is connected to some other frame through the relation <relation-1> • <relation-2>?(<frame-o>,<frame-d>): returns true if there is a relation tagged <relation-2> between <frame-o> and <frame-d>

19. Relations: examples • Relation Description • has-subtype inverse of is-a relation • is-property-of object with given salient character • is-caused-by indicates force that is the origin of something • has-property salient property of an object • has-part a part of a physical object • has-high-level implication for the activity (e.g., intelligence) • is-antonym-of generally used for lexical opposition • is-conceptual-part-of parts of other entities (in case relations) • has-metaphor non-literal reference to the word • uses a tool needing active manipulation • is-performed-by human actor for the event • is-function-of artifact that passively performs the function • has-result more specific type of causes • has-conceptual-part generalization of has-part • is-used-in activity or some desired effect for the entity • is-part-of distinguishes part from group membership • causes inverse of is-caused-by • has-method method used to achieve some goal • has-consequence causation due to a natural association • has-commencement state that commences with the action • is-location-of absolute location of an object

20. Inheritance • In the case of taxonomic relations, inheritance (of slots and values) is given by default. • In the rest of relations, it has to be explicitly defined. • There are slots inherently non inheritable, e.g.: • has-instance • It is possible that a frame inherits a value through multiple relations (multiple inheritance).

21. Simple and multiple inheritance • If the taxonomy is a tree, the inheritance is simple. • The inheritance is multiple if: • the taxonomy is a graph; • there are other (non taxonomic) relations, which allow inheritance. • If there is inheritance of slots and values, there may be conflict of values.

22. Multiple inheritance: example • What are the Size, Weight and Color of Clyde? • The inferential distance algorithm allows to define from which other frame a given frame inherits.

23. Inferential distance Is the cause of the several species' possessing a given property the middle which is next to the universal, or the middle which is next to the species? Clearly it is that which is nearest to the particular species which is its subject, because this middle is the cause of the proximate subject's falling under the universal. E.g., C is the cause of D's being B; then C is the cause of D's being A, and B is the cause of being A for both C and itself. • Aristotle, from The Categories 23

24. Inferential distance • Create the set of frames from which the value of the slot can be explicitly inherited → Candidates. • Remove from Candidates all frames which are parents of other frames of the set. • If the resulting number of candidates is: • 0 → The slot cannot be inherited • 1 → This is the value to be inherited • N > 1 → There is a multiple-inheritance problem, if slot’s cardinality is not at least N

25. Ontologies • An ontology is nothing exotic, at least in artificial intelligence. • It is a formalexplicit representation of a shared understanding of the important concepts in some domain of interest. • In philosophy the term ‘ontology’ refers to a more delicate, but related, concept: • the study of being as such. 25

26. Formality • Ontologies are formal: they are readable by computers. • Level of formality: • from “highly informal”: natural language • to “rigorously formal”: terms with formal semantics and axioms • In mature ontologies, the descriptions allowed to users are required to be consistent with a set of axioms limiting their use. 26

27. Explicit specification • Types of concepts used and their use restrictions are explicitly defined. • Accessibility • Transparency: • documentation of technical details 27

28. Shared understanding • Ontologies contain consensual knowledge: • accepted by a group of people 28

29. Conceptualization • Ontologies are an abstract model of some “existing” domain of the world. • Commitment: • make as few claims as possible about the domain being modeled, • giving ontology reusers freedom to specialize and instantiate as needed. 29

30. Why ontologies are useful? • Sharing: • “communication” among people / agents with different needs and viewpoints due to their different contexts • unified framework within an “organization” to reduce conceptual and terminological confusion • inter-operability among systems by “translating” between different modeling methods, languages and software tools • ontologies as inter-lingua • integration of ontologies 30

31. Why ontologies are useful? • Modeling • System engineering: • re-usability • Use of standards • Highly configurable ontologies and libraries of ontologies support re-usability among different software systems. • reliability • Formal ontologies enable the use of consistency checking resulting in more reliable systems. 31

32. Ontological engineering • In toy domains, the choice of representation is not important. • It is easy to come up with a consistent vocabulary. • Complex domains require more general and flexible representations: • shopping on the Internet • controlling a robot in a changing environment • diagnosing problematic situations in wastewater management 32

33. Ontological engineering • Ontological engineering consists of representing the abstract concepts that occur in many different domains: • Actions • Time • Physical Objects • Beliefs • ... • It is related to knowledge engineering, but operates on a larger scale. 33

34. Ontological engineering • Representing everything in the world can be a problem. • We leave placeholders where new knowledge can fit in. • For example, we can define what it means to be a physical object. • The different types of object can be filled later: • robots • televisions • books • ... 34

35. A general example 35

36. Ontological engineering • The general framework of concepts is called an upper ontology, • because of the convention of drawing graphs with: • the general concepts at the top • the more specific concepts below them 36

37. An upper ontology

38. Ontological engineering • Certain aspects of the real world are hard to capture in FOL: • Almost all generalizations have exceptions • They hold only to a degree • Example: • “Tomatoes are red” is a useful rule, but... • Some tomatoes are green, yellow or orange. • The ability to handle exceptions and uncertainty is extremely important. 38

39. Ontological engineering • For any special-purpose ontology, it is possible to move toward greater generality. • Do all ontologies converge on a general-purpose ontology? • Possibly 39

40. Ontological engineering • Main characteristics of general-purpose ontologies that distinguish them from collections of special-purpose ontologies: • A general-purpose ontology should be applicable in any special-purpose domain. • In any sufficiently demanding domain, different areas of knowledge have to be unified. • Reasoning and problem solving could involve several areas simultaneously. 40

41. Categories and objects • Concepts are organized in taxonomies, linked by relations and conforming to axioms. • Axioms can be very general, e.g.: “a fox is not a shifty deceptive person” or “a Firefox is not a fox”. • The organization of objects into categories is a vital part of knowledge representation. 41

42. Categories and objects • Interaction with the world takes place at the level of individual objects, but: • much reasoning takes place at the level of categories. • Example: • A shopper might have the goal of buying a mouse, rather than a particular mouse such as a V470 Cordless Laser Mouse. 42

43. Categories and objects • Categories can serve to make predictions about objects, once they are classified. • Inferring the presence of certain objects from perceptual input • Inferring category membership from the perceived properties of the object • Example: • From an object being a watermelon, one infers that it would be useful for fruit salad. 43

44. Ontology builders • Knowledge engineers (KEs) build ontologies: • based on consensual knowledge • about a domain • to enable a stipulated group of people (users of the ontology) to share and reusethat knowledge • within the chosen area of work (the domain). 44

45. Application of ontologies to natural language processing

46. Application of ontologies to natural language processing

47. Application of ontologies to natural language processing • Current AI challenges: • Natural language understanding • Software requirements understanding • Image/video understanding • Process/agents/services understanding • Database understanding • Web understanding

48. Application of ontologies to natural language processing

49. Application of ontologies to natural language processing

50. Application of ontologies to natural language processing