2009 Qing Li Semantic Data Modeling Concepts Semantic Data Modeling Concepts Background: Semantic modeling research started in ‘70s Semantic modeling research started in ‘70s (mainly late ‘70s) Originally as schema design aids/tools for traditional record-based models (eg, ER) Emphasis: to accurately model data relationships => more complex inherently, and encourage a more navigational view Results: Results: Semantically more expressive and powerful modeling concepts <embodied by a set of semantic data models>
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2009 Qing Li Semantic Data Modeling Concepts Background: Semantic modeling research started in ‘70s Semantic modeling research started in ‘70s (mainly.
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2009 Qing Li
Semantic Data Modeling ConceptsSemantic Data Modeling Concepts
Background: Semantic modeling research started in ‘70sSemantic modeling research started in ‘70s (mainly
late ‘70s) Originally as schema design aids/tools for traditional
record-based models (eg, ER) Emphasis: to accurately model data relationships
=> more complex inherently, and encourage a more navigational view
Results:Results: Semantically more expressive and powerful modeling concepts
<embodied by a set of semantic data models>
2009 Qing Li
Semantic Data Modeling ConceptsSemantic Data Modeling Concepts
Background (cont’d): Later trends:
1) to build full-fledged DBMSs based on semantic models directly
2) merge into the “object” database family Continue to evolve:Continue to evolve: up till late ‘80s / early ‘90s
Central Components of Semantic Modeling: Objects / Entities Attributes / Properties Abstraction relationships (semantic primitives)
2009 Qing Li
Semantic Data Modeling ConceptsSemantic Data Modeling Concepts
On Semantic Primitives (“data abstractions”): Classification Classification andand Instantiation Instantiation
the former involves classifying similar objects into classes
the latter refers to the generation and specific examination of distinct objects of a class
inverse of each other Related issuesRelated issues
class class vs.vs. type type class properties class properties vs.vs. type properties type properties classes as instances of classes as instances of meta-classesmeta-classes
2009 Qing Li
Semantic Data Modeling ConceptsSemantic Data Modeling Concepts
On Semantic Primitives (cont’d) IdentificationIdentification
refers to the abstraction process whereby all abstract concepts and concrete objects are made uniquely identifiable by means of identifiers
needed at two levels
1) to distinghish among DB objects & classes1) to distinghish among DB objects & classes
2) to identify DB objects and relate them to real-2) to identify DB objects and relate them to real-world counterpartsworld counterparts
“identifiers” vs. “keys”
2009 Qing Li
Semantic Data Modeling ConceptsSemantic Data Modeling Concepts
On Semantic Primitives (cont’d) Specification Specification andand Generalization Generalization (“is-a”)(“is-a”)
the former is the process of further classifying a class of objects into more specialized subclasses (“conceptual refinement”)
the latter is the inverse of the former, where several classes are generalized into a higher-level abstract class (“conceptual synthesis”)
Semantic Data Modeling ConceptsSemantic Data Modeling Concepts
On Semantic Primitives (cont’d) Aggregation Aggregation (“is-part-of”)(“is-part-of”)
an abstraction concept for building composite/ complex objects from their component objects
three cases: we aggregate attribute values of an object to we aggregate attribute values of an object to
form the whole objectform the whole object represent an aggregation relationship as an represent an aggregation relationship as an
ordinary relationshipordinary relationship combine related objects into a combine related objects into a higher-levelhigher-level
aggregate object aggregate object (with attributes whose value (with attributes whose value ranges are non-atomic objects)ranges are non-atomic objects)
2009 Qing Li
Semantic Data Modeling ConceptsSemantic Data Modeling Concepts
On Semantic Primitives (cont’d) Association Association (“is-associated-with”)(“is-associated-with”)
an abstraction concept for associating objects from several independent classes
similar to the 3rd case of aggregation, with a distinction:
when an association instance is deleted, the when an association instance is deleted, the participating objects continue to exisitparticipating objects continue to exisit
(not the case for aggregation!)(not the case for aggregation!)
2009 Qing Li
Semantic Data Modeling ConceptsSemantic Data Modeling Concepts
Related Work in AI Knowledge Representation (KR)Knowledge Representation (KR)
Semantic networks frame-based ones
Similarities & DifferencesSimilarities & Differences both use an abstraction process to identify common properties
& important aspects of objects, while attempting to surpress insignificant detailed differences
both provide concepts, constraints, operations, and languages for the object definition & representation
scope of KR is broader (can answer queries involving inference and deduction over objects)
KR tools are in-memory ones, could not scale up.
2009 Qing Li
The Extended ER ModelThe Extended ER Model
Introduction Numerous extensions to the ER model (since ‘79)Numerous extensions to the ER model (since ‘79) collectively referred as EER model Aiming at incorporating existing semantic concepts
and primitives into the original ER model EER Model Concepts & PrimitivesEER Model Concepts & Primitives
sub-/super-class (specialization/generalization)sub-/super-class (specialization/generalization) attribute inheritance superclass/subclass as an explicitly defined
and supported relationships
2009 Qing Li
The Extended ER ModelThe Extended ER Model
EER Diagram Notation
Class 1 Class 2
Constraints on specialization/generalization:Constraints on specialization/generalization: predicate-defined constraint
Sub-/super-class (cont’d) the disjointness and completeness constraints are
independent, hence we can have:
a) disjoint, totala) disjoint, total
b) disjoint, partialb) disjoint, partial
c) overlapping, totalc) overlapping, total
d) overlapping, partiald) overlapping, partial a generalization superclass usually is total, hence
only a) and c) apply to generalization specialization can have all above 4 kinds!
2009 Qing Li
The Extended ER ModelThe Extended ER Model
Sub-/super-class (cont’d) insertion and deletion rules:
deleting an entity from a superclass => deleting it from all the subclasses
inserting an entity in a superclass => inserting it to all predicate-defined subclasses if the entity satisfies the predicate
inserting an entity in a superclass of a total specialization => it is inserted in at least one of the subclasses of the specialization
specialization lattice & shared subclassesspecialization lattice & shared subclasses a shared subclass: the result of a shared subclass: the result of intersectionintersection!!
2009 Qing Li
The Extended ER ModelThe Extended ER Model
2009 Qing Li
The Extended ER ModelThe Extended ER Model
Categories and Categorization for modeling a single superclass/subclass
relationship with more than one superclass to derive to derive a class (an entity set) that is of a class (an entity set) that is of