Representation of Fuzzy Knowledge in Relational Databases Authors: José Galindo ; Angélica Urrutia ; Mario Piattini Public:Database and Expert Systems Applications (DEXA’04) Adviser ： RC. Chen Speaker: Chih-Hung Hsu Date:2006/12/14

2 Outline Abstract Introduction Fuzzy Attributes Representation of Fuzzy Attributes Representation of Fuzzy Metaknowldege Data ： The FMB Conclusions and Future Lines

3 Abstract Implement fuzzy databases based on the relational model Two aspects of fuzzy information –how to represent fuzzy data –how to represent fuzzy metaknowledge data

4 Introduction Fuzzy relation database allow storing and/or treating vague and uncertain information FuzzyEER model is an extension of the EER model to create conceptual schemas with fuzzy semantics and notations fuzzy attributes, fuzzy entities, fuzzy relationships, fuzzy specializations incorporate the FuzzyEER concepts in a relational DBMS

5 Fuzzy Attributes (1/6) Fuzzy Sets as Fuzzy Values Type 1 –precise data –can be transformed or manipulated using fuzzy conditions Type 2 –imprecise data over an ordered referential –allow the storage of imprecise information

6 Fuzzy Attributes (2/6) Type 3 –data of discreet non-ordered dominion with analogy Type 4 –as Type 3 –they are defined in the same way as Type 3 attributes

7 Fuzzy Attributes (3/6) Fuzzy Degrees as Fuzzy Values –only use degrees in the interval [0,1] –most important possible meanings of the degrees: Fulfillment degree Uncertainty degree Possibility degree Importance degree –associated and non-associated degrees

8 Fuzzy Attributes (4/6) Type 5 –Degree in each value of an attribute –some attributes may have a fuzzy degree associated to them –need to know the meaning of the degree and the meaning of the associated attribute

9 Fuzzy Attributes (5/6) Type 6 –Degree in a set of values of different attributes –the degree is associated to some attributes and this is an unusual case Type 7 –Degree in the whole instance of the relation –can represent something like the “membership degree” of this tuple to the relation of the database

10 Fuzzy Attributes (6/6) Type 8 –Non-associated degrees –there are cases in which the imprecise information, which we wish to express, can be represented by using only the degree, without associating this degree to another specific value or values

11 Representation of Fuzzy Attributes(1/5) Fuzzy attributes Type 1 doesn’t allow fuzzy values Fuzzy attributes Type 2 need five classic attributes: –One stores the kind of value (Table 1) –the others four store the crisp values representing the fuzzy value

12 Representation of Fuzzy Attributes(2/5)

13 Representation of Fuzzy Attributes(3/5)

14 Fuzzy attributes Type 3 need a variable number of attributes: –one stores the kind of value (Table 2) number 3 needs only two values, but number 4 needs 2n values, where n is the maximum length for possibility distributions for each fuzzy attribute Fuzzy attributes Type 4 are represented just like Type 3 Representation of Fuzzy Attributes(4/5)

15 Representation of Fuzzy Attributes(5/5)

16 Representation of Fuzzy Metaknowledge Data: The FMB (1/2) Information in the FMB Attributes with fuzzy capabilities: fuzzy attributes and fuzzy degrees (Type 1 to 8) The metaknowledge of each attribute is different according to its type –Types 1 and 2: This last value is used in comparisons like “much greater than” –Types 3 and 4: Value n, name of linguistic labels and, only for Type 3, the similarity relationship between whatever two labels

17 –Types 5 and 6: Meaning of the degree and attribute or attributes to which the degree is associated –Types 7 and 8: Meaning of the degree Other objects: –fuzzy qualifiers (Give me employees who belong to most of projects) –fuzzy quantifiers (An employee must work in many projects) Representation of Fuzzy Metaknowledge Data: The FMB (2/2)

19 Conclusions and Future Lines Implement fuzzy databases modeled with the FuzzyEER model Represent fuzzy data and fuzzy metaknowledge data FSQL (Fuzzy SQL) language may be used in those databases