1 / 18

Representation

Temporal Reasoning and Planning in Medicine Frame-Based Representations and Description Logics Yuval Shahar, M.D., Ph.D. Representation. Knowledge systems are a model of a domain, a process, or a task Representations enable making distinctions and inferences appropriate for relevant tasks

arien
Télécharger la présentation

Representation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Temporal Reasoning and Planning in MedicineFrame-Based Representations and Description LogicsYuval Shahar, M.D., Ph.D.

  2. Representation • Knowledge systems are a model of a domain, a process, or a task • Representations enable making distinctions and inferences appropriate for relevant tasks • Representations can differ with respect to expressiveness and/or computational complexity of answering certain queries

  3. A Representation: First-Order Logic • Constants: Mr_Smith, Dr._Jones, anemia • Variables: X, Y • Functions: Address(X), Age(Y) • Predicates: Diagnosis(X, anemia); Male(Y); Patient(Z) • Negation: ¬Male(X); ¬Name(X, Smith) • Connectors: • Conjunction (AND): Patient(X)  Male(X) • Disjunction (OR): Doctor(X)  Nurse(X) • Logical implication: Female(X)  ¬Male(X) • Quantifiers: • Universal quantifier: X (Patient(X)  Doctor(X)) • Existential quantifier: Y (Patient(Y)  Name(Y, Jones))

  4. Graphs • A graph G: a set <V, E> • V: set of vertices (nodes) Vi • E: set of edges (links) Ei,j: (Vi, Vj) • If edges are ordered pairs the graph is directed • if edges are nonordered pairs the graph is undirected E1,2 V1 V2 V3 V4

  5. A Semantic Network • A directed graph where Vi are concepts and Ei,j are relations Mamal AKA Person 27 years IS-A 5 Days Duration Age Jim Has Disease Patient Diagnosis Mumps

  6. Semantic Networks:Arity of Relations • Unary relations • Person(Jim): IS-A link • Binary relations • Age(Jim, 27 years): Age link • N-ary relations • Disease(Jim, Mumps, 5 days): By creating a reified disease-relation object with several cases (patient, diagnosis, duration)

  7. Frames (Minksy, 1975) • Semantic networks • Typically represented graphically as hierarchies of concepts such as person • Concepts have roles, or properties, (also known in OOLs as slots), such as age • Frames encapsulate more meaningful chunks of knowledge (e.g., birthday party)

  8. A Frame Representation Mammals Legs: 4 AKA AKA AKA Bats Humans Lions Legs: 2 Legs: 2 IS-A IS-A IS-A Jim John Bibi Age:27 Age:16

  9. Inheritance • Assume property P for class C, then: • x (IS-A(x, C) => P(x)) • That is, all instances of C have property P • Exceptions can be handled by allowing for overriding values of properties if there is an intervening node with a different value for P • Values of properties are thus only defaults

  10. Implications of Inheritance • Determination of properties of instances involves a search of the semantic-network graph • Default reasoning is enabled • high-level nodes can have values that are inherited by many lower-level nodes unless these values are overridden • Exceptions imply a nonmonotonic logic • Multiple inheritance is possible, but might be ambiguous when conflicts occur

  11. Advantages of Frames • Classes and instances organize a flat knowledge base (unlike FOL) by introducing structure on an epistemological level • E.g., specialization of subclasses through restriction of a range of values for a property • Simple; easy to understand • Inheritance is captured in a natural, modular fashion • Efficient inference (e.g., for validation) by following links, compared to standard logics

  12. Problems with Frames • Negation cannot be represented • Jim does not have pneumonia • Disjunction cannot be represented naturally • Jim has Mumps or Rubella • Qualification is not a part of the language • All of Jim’s diseases are infectious => Thus, procedural attachments are often added • The semantics of the links are often not well defined [“What’s in a Link,” Woods, 1975]

  13. Description Logics • A subset of FOL designed to focus on categories and their definitions in terms of existing relations • More expressive than semantic networks • Major inference tasks: • Subsumption (is category C1 a subset of C2?) • Classification (Does Object O belong to C?)

  14. Examples of Definition Logics • KL-One: The first, prototypical language • Classic • Krypton • Loom • Grail (medical ontologies; part of Galen project)

  15. KL-One • A structured inheritance network • Basic elements: • Concepts • generic • individual • Roles: Conceptual subpieces of an entity • parts, attributes, function arguments, linguistic cases • Structured descriptions: Relations among roles

  16. A Classic Example A patient with at least 2 diseases, both of which have a diagnosis of either Mumps or Rubella: And (Patient, Atleast (2, Diseases), All(Diseases, Fills(Diagnosis, Mumps, Rubella)))

  17. Features of description Logics • Subsumption is derived from category descriptions • Inference is tractable (polynomial) • However, that must preclude representation of certain models • Complex models might require exponential representations • Users might be tempted to circumvent the language • Negation and disjunction typically do not exist

  18. Summary • There are multiple representation formalisms • Frames are a type of semantic networks • A fundamental tradeoff exists in all formalisms [Levesque and Brachman, 1984], between: • 1. Expressive power of a representation language • 2. computational tractability of inference with it

More Related