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 Relationship set: n-ary relationships, cardinality constraints – Usually shown graphically

Sample OSM for Cars (Graphic Version) YearPrice Make Mileage Model Feature PhoneNr Extension Car has is for has 1..* * * 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 Ontology Car [->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 Exchange SourceTarget Information Extraction Schema Matching Leverage this … … to do this

Extracting Pertinent Information from Documents

Recognition and Extraction Car Year Make Model Mileage Price PhoneNr Subaru SW $1900 (363) Elandra (336) HONDA ACCORD EX 100K (336) Car Feature 0001 Auto 0001 AC 0002 Black door 0002 tinted windows 0002 Auto 0002 pb 0002 ps 0002 cruise 0002 am/fm 0002 cassette stero 0002 a/c 0003 Auto 0003 jade green 0003 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(creat eTable( “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