1. LDK R Logics for Data and Knowledge Representation Modeling First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang

2. Outlines  Modeling  Logical Modeling  Domain  Language  Interpretation  Model  Theory  Expressiveness  Expressiveness VS. Efficiency  Efficiency VS. Complexity  Decidability  Algorithm First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang 11/20092

3. Modeling 11/20093 First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang World Language L Theory TH Domain D Model M Data Knowledge Meaning Model Semantic Gap

4. Outlines  Modeling  Logical Modeling  Domain  Language  Interpretation  Model  Theory  Expressiveness  Expressiveness VS. Efficiency  Efficiency VS. Complexity  Decidability  Algorithm First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang 11/20094

5. Logical Modeling 11/20095 First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang

6. Logical Modeling Elements The basic elements in the diagram are: 1. Domain (objects, classes, relations, functions) 2. Logical Language 3. Interpretation (of the language) 4. Model 5. Theory / Knowledge Base (data and knowledge) 6. Truth-relation / logical entailment (|=) (deduction reasoning, inference) We illustrate each element in turn. First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang 11/20096

7. Domain  Domain (D) = the chosen objects from the world  Example (LDKR class): the members of the LDKR class define a domain D;  D is a finite set.  The “type” of the elements in D is: person. We will deal only with finite domains! Question: what are we leaving out? First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang 11/20097

8. Language  Language (L) = a logical language 1. L’s alphabet of symbols Σ contains at least one of the logical symbols: ∧, ∨, ¬, →, ∀, ∃ ; 2. L has clear formation rules for formulas.  Example (cont’): any logical language with =, (, ), professor, student1, student2,... in Σ.  Note that English can be restricted to a propositional language,...but it is not logical (informal syntax). First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang 11/20098

9. Logical Language (Syntax)  The first step in setting up a logical language (viz. a formal language) is to list the symbols, that is, the alphabet of (formal) symbols ( Σ ).  formal symbol = a character, or group of characters taken from some alphabet.  Symbols in Σ can be divided in ‘descriptive’ (non-logical) and ‘non-descriptive’ (logical). First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang 11/20099

10. Example (Monkey-Bananas Problem)  In the sentence “There is a monkey in a laboratory with some bananas”:  Descriptive symbols are: ‘monkey’, ‘laboratory',‘bananas’,...  Non-descriptive symbols are: ‘there is’, ‘with some’, ‘a’, ‘in’,... First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang 11/200910

11. Formal Syntax  Formal Syntax: the set of “rules” saying how to construct the expressions of the language from the alphabet of symbols, (i.e., the syntax) is a grammar (i.e., formal).  Formal syntax is often called an abstract syntax, in contrast to the concrete syntax used, e.g., in implementations.  Example: context-free grammars. First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang 11/200911

12. Interpretation  Interpretation (I) = a mapping of L into D.  I must be effective (i.e., computable).  Exampe 1: D = {Fausto, Aundrey}  I(prof 1 ) = Fausto,  I(studen 2 ) = Audrey  Example: 2: D =  I(HighBox) = T  I(canTakeExam) = F … First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang 11/200912

13. Model  Model (M) = the abstract (mathematical sense) representation of the intended truths via interpretation I of language L. M is called L- model of D. M |=A to be read as: M satisfies A A holds in M A is TRUE in M M yields A  Example (cont’): D=, M such that  I(HighBox) = T then M |= Highbox  I(canTakeExam) = F then M |= ¬ canTakeExam... First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang 11/200913

14. Theory  Theory T (also L-Theory) = set of facts of L.  A fact defines a piece of knowledge (about D), something true in the model.  A finite theory T is called a knowledge base (KB).  A database (DB) is the simplest kind of knowledge base.  Example (cont’): T = {¬ canTakeExam, HighBox}. We have: M |= T iff M |=A, for each Ai in T … A model M of a theory T is an interpretation function that satisfies all the facts in T First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang 11/200914

15. What is a Logic? Logic = where  L: Language = set of phrases/ formulas (alphabet + formation rules)  I: Interpretation Function (what phrases mean in a chosen domain D) I: L -> D  |= : how to compute the fact that a formula A holds in M (A is TRUE in M), notationally M |= A with A |= B in M iff M|=A implies M |= B First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang 11/200915

16. How to use logics 1) Define a logic (most often by reseachers), once for all (not a trivial task!) 2) Given a problem the computer scientist must choose the right logic, most often one of the many available 3) The computer scientist writes a theory T 4) The computer scientist uses reasoning services to solve her programs First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang 11/200916

17. First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang Reasoning Services Model Checking (EVAL): Is a sentence ψ true in model M? Check M |= ψ Satisfiability (SAT): Is there a model M where ψ is true? find M such that M |= ψ Validity(VAL): Is ψ true according to all possible models check whether for all M, M |= ψ Entailment(ENT): ψ 1 true in M (all models) implies ψ 2 is true in M (all models). check A |= B in M by checking M|=A implies M |= B 9/2009

18. Outlines  Logical Modeling  Domain  Language  Interpretation  Model  Theory  Expressiveness  Expressiveness VS. Efficiency  Efficiency VS. Complexity  Decidability  Algorithm First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang 11/200918

19. An Important Trade-Off  There is a trade-off between expressive power (expressiveness) and computational efficiency provided by a (logical) language.  This trade-off is a measure of the tension between specification and automation. To use logic for modeling, the modeler must trading off expressiveness in the language for more tractable forms of reasoning services. First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang 11/200919

20. Examples  Propositional:  “I like skiing” : I-like-skiing  “Fausto likes skiing”: Fausto-like-skiing  Modal:  “I believe I like skiing”: B(I-like-skiing), B I (like-skiing)  First-order:  “Every person likes skiing”: ∀ person.like-skiing(person)  “I like skiing” : like-skiing (I)  “Fausto likes skiing” : like-skiing(Fausto)  Description Logics  “Every person likes cars”: person < exists likes. Car  … First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang 11/200920

21. Efficiency VS. Complexity  Efficient (Webster). Performing in the best possible manner; satisfactory and economical to use.  In modeling it applies to reasoning;  In this case we use the more specific terminology computational complexity (time, space,...) in place of term efficiency.  Complexity: (or computational complexity) of reasoning is the difficulty to compute a reasoning task expressed by using a logic.  with degrees of expressiveness, we may classify the logical languages according to some “degrees of complexity”. First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang 11/200921

22. Degrees of Complexity (NEW)  The basic “degrees of complexity” are:  Here comes the axis of complexity First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang 11/200922

23. Decidability First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang 11/200923

24. What is an Algorithm?  Decidability is strictly related to the notion of “algorithm.” There were two main approaches, and many equivalent definitions:  K. Gödel (1934): recursive functions. Alonzo Church (1936): lambda-calculus. S.C. Kleene (1936): functional equations.  Alan M. Turing (1936-37): Turing machines. Emil L. Post (1936): Post machines. Functional approaches Computational approaches First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang 11/200924

25. Decision Procedures and Decidable Logics  A decision procedure is an algorithm that, given a decision problem, terminates with the correct yes/no answer.  A logic is decidable if there exists a decision procedure for that logic.  In this course we focus on logics that are expressive enough to model real problems but are still decidable. First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia and Rui Zhang 11/200925