Interpretations of Rules
26.5.5 Interpretations of Rules
There are two main alternatives for interpreting the theoretical meaning of rules: proof-theoretic and model-theoretic. In practical systems, the inference mechanism
within a system defines the exact interpretation, which may not coincide with either of the two theoretical interpretations. The inference mechanism is a computational procedure and hence provides a computational interpretation of the meaning of rules. In this section, first we discuss the two theoretical interpretations. Then we briefly discuss inference mechanisms as a way of defining the meaning of rules.
In the proof-theoretic interpretation of rules, we consider the facts and rules to be true statements, or axioms. Ground axioms contain no variables. The facts are
ground axioms that are given to be true. Rules are called deductive axioms, since they can be used to deduce new facts. The deductive axioms can be used to con- struct proofs that derive new facts from existing facts. For example, Figure 26.12 shows how to prove the fact SUPERIOR (james, ahmad) from the rules and facts
1. SUPERIOR(X, Y ) :– SUPERVISE(X, Y ).
(rule 1)
Figure 26.12
2. SUPERIOR(X, Y ) :– SUPERVISE(X, Z ), SUPERIOR(Z, Y ). (rule 2) Proving a new fact. 3. SUPERVISE(jennifer, ahmad).
(ground axiom, given) 4. SUPERVISE(james, jennifer).
(ground axiom, given) 5. SUPERIOR(jennifer, ahmad).
(apply rule 1 on 3)
976 Chapter 26 Enhanced Data Models for Advanced Applications
given in Figure 26.11. The proof-theoretic interpretation gives us a procedural or computational approach for computing an answer to the Datalog query. The process of proving whether a certain fact (theorem) holds is known as theorem proving .
The second type of interpretation is called the model-theoretic interpretation. Here, given a finite or an infinite domain of constant values, 33 we assign to a predi- cate every possible combination of values as arguments. We must then determine whether the predicate is true or false. In general, it is sufficient to specify the combi- nations of arguments that make the predicate true, and to state that all other combi- nations make the predicate false. If this is done for every predicate, it is called an interpretation of the set of predicates. For example, consider the interpretation shown in Figure 26.13 for the predicates SUPERVISE and SUPERIOR . This interpre- tation assigns a truth value (true or false) to every possible combination of argu- ment values (from a finite domain) for the two predicates.
An interpretation is called a model for a specific set of rules if those rules are always true under that interpretation; that is, for any values assigned to the variables in the rules, the head of the rules is true when we substitute the truth values assigned to the predicates in the body of the rule by that interpretation. Hence, whenever a par- ticular substitution (binding) to the variables in the rules is applied, if all the predi- cates in the body of a rule are true under the interpretation, the predicate in the head of the rule must also be true. The interpretation shown in Figure 26.13 is a model for the two rules shown, since it can never cause the rules to be violated. Notice that a rule is violated if a particular binding of constants to the variables makes all the predicates in the rule body true but makes the predicate in the rule head false. For example, if SUPERVISE (a, b) and SUPERIOR (b, c) are both true under some interpretation, but SUPERIOR (a, c) is not true, the interpretation can- not be a model for the recursive rule:
SUPERIOR (X, Y) :– SUPERVISE (X, Z), SUPERIOR (Z, Y) In the model-theoretic approach, the meaning of the rules is established by provid-
ing a model for these rules. A model is called a minimal model for a set of rules if we cannot change any fact from true to false and still get a model for these rules. For example, consider the interpretation in Figure 26.13, and assume that the SUPERVISE predicate is defined by a set of known facts, whereas the SUPERIOR predicate is defined as an interpretation (model) for the rules. Suppose that we add the predicate SUPERIOR (james, bob) to the true predicates. This remains a model for the rules shown, but it is not a minimal model, since changing the truth value of SUPERIOR ( james , bob ) from true to false still provides us with a model for the rules. The model shown in Figure 26.13 is the minimal model for the set of facts that are defined by the SUPERVISE predicate.
In general, the minimal model that corresponds to a given set of facts in the model- theoretic interpretation should be the same as the facts generated by the proof-
26.5 Introduction to Deductive Databases 977
Rules SUPERIOR(X, Y ) :– SUPERVISE(X, Y ). SUPERIOR(X, Y ) :– SUPERVISE(X, Z ), SUPERIOR(Z, Y ).
Interpretation Known Facts:
SUPERVISE(franklin, john) is true. SUPERVISE(franklin, ramesh) is true. SUPERVISE(franklin, joyce) is true. SUPERVISE(jennifer, alicia) is true. SUPERVISE(jennifer, ahmad) is true. SUPERVISE(james, franklin) is true. SUPERVISE(james, jennifer) is true. SUPERVISE(X, Y ) is false for all other possible (X, Y ) combinations
Derived Facts: SUPERIOR(franklin, john) is true. SUPERIOR(franklin, ramesh) is true. SUPERIOR(franklin, joyce) is true. SUPERIOR(jennifer, alicia) is true. SUPERIOR(jennifer, ahmad) is true. SUPERIOR(james, franklin) is true. SUPERIOR(james, jennifer) is true. SUPERIOR(james, john) is true. SUPERIOR(james, ramesh) is true. SUPERIOR(james, joyce) is true. SUPERIOR(james, alicia) is true.
Figure 26.13
SUPERIOR(james, ahmad) is true. An interpretation that SUPERIOR(X, Y ) is false for all other possible (X, Y ) combinations
is a minimal model.
theoretic interpretation for the same original set of ground and deductive axioms. However, this is generally true only for rules with a simple structure. Once we allow negation in the specification of rules, the correspondence between interpretations does not hold. In fact, with negation, numerous minimal models are possible for a given set of facts.
A third approach to interpreting the meaning of rules involves defining an inference mechanism that is used by the system to deduce facts from the rules. This inference mechanism would define a computational interpretation to the meaning of the rules. The Prolog logic programming language uses its inference mechanism to define the meaning of the rules and facts in a Prolog program. Not all Prolog pro- grams correspond to the proof-theoretic or model-theoretic interpretations; it depends on the type of rules in the program. However, for many simple Prolog pro- grams, the Prolog inference mechanism infers the facts that correspond either to the proof-theoretic interpretation or to a minimal model under the model-theoretic
978 Chapter 26 Enhanced Data Models for Advanced Applications
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» Fundamentals_of_Database_Systems,_6th_Edition
» Characteristics of the Database Approach
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» Retrieving Multiple Tuples with Embedded SQL Using Cursors
» Specifying Queries at Runtime Using Dynamic SQL
» SQLJ: Embedding SQL Commands in Java
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» JDBC: SQL Function Calls for Java Programming
» Database Stored Procedures and SQL/PSM
» PHP Variables, Data Types, and Programming Constructs
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» Some General Issues Concerning Indexing
» Algorithms for External Sorting
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» Catalog Information Used in Cost Functions
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» Examples of Cost Functions for JOIN
» Example to Illustrate Cost-Based Query Optimization
» Factors That Influence Physical Database Design
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» An Overview of Database Tuning in Relational Systems
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» Transaction and System Concepts
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» Introduction to Database Security Issues 1
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» Distributed Databases in Oracle 13
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» Examples of Statement-Level Active Rules
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» Incorporating Time in Object-Oriented Databases Using Attribute Versioning
» Temporal Querying Constructs and the TSQL2 Language
» Spatial Database Concepts 24
» Multimedia Database Concepts
» Clausal Form and Horn Clauses
» Datalog Programs and Their Safety
» Evaluation of Nonrecursive Datalog Queries
» Introduction to Information Retrieval
» Types of Queries in IR Systems
» Evaluation Measures of Search Relevance
» Web Analysis and Its Relationship to Information Retrieval
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» Goals of Data Mining and Knowledge Discovery
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» Market-Basket Model, Support, and Confidence
» Frequent-Pattern (FP) Tree and FP-Growth Algorithm
» Other Types of Association Rules
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» Commercial Data Mining Tools
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» Difficulties of Implementing Data Warehouses
» Grouping, Aggregation, and Database Modification in QBE
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