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