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From OSM-L to JAVA. Cui Tao Yihong Ding. Overview of OSM. OSM. OSM (Object-oriented Systems Model) Use for system analysis, specification, design, implementation, and evaluation Structural components: object sets and relationship sets Object set: generalization/specialization - PowerPoint PPT Presentation
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From OSM-L to JAVAFrom OSM-L to JAVA
Cui Tao
Yihong Ding
Overview of OSM
OSM
OSM (Object-oriented Systems Model)– Use for system analysis, specification, design,
implementation, and evaluation– Structural components: object sets and
relationship sets • Object set: generalization/specialization• Relationship set: n-ary relationships, cardinality
constraints
– Usually shown graphically
Sample OSM for Cars(Graphic Version)
Year Price
Make Mileage
Model
Feature
PhoneNr
Extension
Car
hashas
has
has is for
has
has
has
1..*
0..1
1..*
1..* 1..*
1..*
1..*
1..*
0..1 0..10..1
0..1
0..1
0..1
0..*
1..*
OSM-L and Ontology
OSM-L: A textual language for representing OSM application models.
Ontology: A program written in OSM-L to provide the database schema, relationship sets and a knowledge base to the extractor
For each application domain, we have to write a new ontology depend on the user’s request
Car-Ads OntologyCar [->object];Car [0..1] has Year [1..*];Car [0..1] has Make [1..*];Car [0...1] has Model [1..*];Car [0..1] has Mileage [1..*];Car [0..*] has Feature [1..*];Car [0..1] has Price [1..*];PhoneNr [1..*] is for Car [0..*];PhoneNr [0..1] has Extension [1..*];Year matches [4]
constant {extract “\d{2}”; context "([^\$\d]|^)[4-9]\d,[^\d]"; substitute "^" -> "19"; }, …End;
Data Extraction
Information ExchangeSource Target
InformationExtraction
SchemaMatching
Leveragethis …
… to dothis
Extracting Pertinent Information from Documents
Recognition and Extraction
Car Year Make Model Mileage Price PhoneNr0001 1989 Subaru SW $1900 (363)835-85970002 1998 Elandra (336)526-54440003 1994 HONDA ACCORD EX 100K (336)526-1081
Car Feature0001 Auto0001 AC0002 Black0002 4 door0002 tinted windows0002 Auto0002 pb0002 ps0002 cruise0002 am/fm0002 cassette stero0002 a/c0003 Auto0003 jade green0003 gold
OSM
• Object Set
• Relationship Set {
-- connection {
object set
constraint
}
}
Structure
Nonlexical
Lexical {
object name
data frame
}
Data frame {
extraction rule
context rule
substitution rule
keyword
}
Schema Generation Interface
Schema implements
Table-Insertion Interface{
relational database tables
insert methods
}
Matching Process
Retrieved Data
Database Population Interface
Parser and Symbol Table
Generate parse tree Design the structure of symbol table
Data Extraction
Extraction Rules
Defines the expecting pattern of string to extract.
Context Rules
Defines the context constraint of the target pattern.
Substitution Rules
Defines the substitution situation if applicable.
Keywords
Defines keywords to get rid of ambiguity if it happens.
Knowledge Representation
Current knowledge base– Static– Need peripheral programs
Our predicating knowledge base– Functional– Adaptive– Object-oriented
Schema Generation
Domain
Attribute
Relation
Constraint
Schema Generation
if(!existTable(“car”)
createStatement(createTable(
“createCar”);createCar =“
create table Car(ObjNr char(4) primary key,VIN char(4) unique,
Make char(10), : PhoneNr char(20),);
Schema Generation
if(!existTable(“Feature”))
createStatement(createTable(
“createFeature”);
createFeature =“
create table Feature(
ObjNr char(4)
primary key,
Feature char(20),
);
Schema Generation
if(!existTable(“Extension”))
createStatement(createTable(
“createExtension”);
createExtension =“
create table Extension(
PhoneNr char(14)
primary key,
Extension char(3),
);
Insert Data
Collect all the values available for each object
Find out the position of each insert value Insert values for each object
Data.attribute
Data.value
Data.objNr
Data Record Table:
Populate Database