Knowledge Base Building Project 3 rd meeting 2008. 08. 30.

Knowledge Base Building Project3rd meeting

2008. 08. 30

Copyright 2008 by CEBT

What We Did In Last Week

1st meeting (2008. 08. 25)

Surveying several papers and applications

Presenting and discussing the survey results

2nd meeting (2008. 08. 27)

Discussing several initial issues in our project

– Additional presentation about GoogleBase

– Motivation (users, services)

– Initial data structure

– Benchmark systems and application

– Project maintenance

– Attachment with structured Naver Kin service


Additional Survey on GoogleBase

Google Base 의 모델에 대한 보충 Presentation

Storage 는 attribute, Description, Picture or file(Xml, HTML, so on) 등의 정보로 이루어짐

Basic Attribute 는 item type 에 따라 정해짐

Category, Item, Attribute 는 사용자가 추가 가능

Item 들간의 Relationship 은 없는 것 같음

File 은 대략 15 개 정도까지 올릴 수 있음 . 20MB 정도를 한계점으로 지정

API 리스트는 Google base Homepage 에서 찾아볼 수 있음 . 그것을 보고 어떤 기능을 제공해야 할 지를 생각해 볼 수 있을 것 같음


Motivation

“What is the main purpose of this project?”

Assistant (product) knowledge base which is able to provide some richer information about domain information to existing applications

“Who is the main target users?”

Should be application designer – they can attach easily our knowledge base to their application by API for enrich the informa-tion

“What we have to do?”

Designing the general data structure of our own

– 1st goal : building the database that is similar to Freebase

Providing graph visualization of knowledge base

Supposing general APIs to retrieve the information in knowledge base

Deciding initial data structure and framework model


Initial Data Model

First step

Adapting flat scheme of Freebase data model

– Refer to structure of PPS ontology and GoodRelation

Using the PPS ontology as initial data instance

Providing the way to identify the special relation between the objects (products)

– Supporting to navigate the standard classification of the product : UNSPSC, eCl@ss, EOTD, …

Second step

Considering the expansion to accommodate the public web re-sources as target knowledge in general way

Regarding how to gather and reflect users’ collective intelli-gence in our system easily

Attaching the structured Naver Kin service into the system


Abstract Data Model in Freebase

Simple and flexible

Can be transformed into RDF or XML datasets with post processed tag

We are trying to add the set of ontological properties like GoodRelation in the RDBMS schema, and adapt the data schema of PPS ontology on the basis of Freebase data structure


Initial Data Structure

OBJECTcategoryrelation

CATEGORY

instancerelation

INSTANCE

relationmap

attributerelation

ATTRIBUTE

RELATION

objectrelation

OBJECT


Initial Data Structure: Example

Obj:CAMERARel:

hasCategoryObj:OpticalElectronics

hasInstance

Obj:CANON

Rel:subClassOf

Rel:hasAttribute

Att:Size

Obj:hasRelation

Rel:hasWiki

Obj:DocumentURL

Att:Pixel

Rel:hasCategory

Obj:MultimediaDevices

Att:CategoryID

A set of relation can be adapted from

product ontology (cf. GoodRelation)

Be able to specify a category forStandard categorization (cf. UN-

SPSC)

Att and Rel type also

has attribute type

Entity (type)

Relation (type)

Connection

Edge has their own weight

Which means the probability

of confidence


Initial Data Structure: Example (cont’d)

Obj:Dark Knight

Rel:Genre

Obj:Action

Rel:hasRelation

Rel:hasDirector

Att:MovieDirector

Obj:Relation

Rel:wikiURL

Obj:DocumentURL

Att:ReleaseDate

Rel:Genre

Obj:Crime

Entity (type)

Relation (type)

Connection

Rel:ReleaseDate

Obj:Christopher

Nolan

Rel:hasValue

Obj:6 August

2008


Data Schema: Example

Obj_Pro

Id

Obj_Rel

Id

Obj_Att

Id

Obj_Cat

Id

Rel_ProAtt

Pro_Id

Att_Id

Weight

Rel_RelAtt

Att_Id

Rel_Id

Weight

Rel_RelAtt

Cat_Id

Rel_Id

Weight

Rel_AttVal

Att_Id

Value_Id

Value

Weight

Rel_ProVal

Pro_id

Value_Id

Weight

Rel_Pro

Pro_id

Pro_id

Weight

Rel_ProCat

Pro_Id

Cat_Id

Weight


Collaboration with structured Naver Kin

Object Question

Answer

QuestionURL

AnswerURL

Author UserId

hasAtt URL

CategoryIdRel:

Genre

Category

Object_ID Relation_ID Question_URL

Answer_URL

Question_User_Id

Answer_User_Id Att_Id

00000001 00000010 http://kin.naver.com/q1.html

http://kin.-naver.com/a1.html

questioner Walkdic 00000100

00000002 00000011 http://kin.-naver.com/q2.html

http://kin.-naver.com/a2.html

newbie masterofkin 00000101

…… …… …… …… …… …… ……

http://kin.naver.com/q1.html

http://kin.naver.com/q1.html


Simple System Framework

Knowledge Base

End-user Applications

Service Interface

Web Browser

API Engine

Mobile App

ServiceController

Data ExchangeModule

Storage Engine

InferenceModule

Physical I/OHandler

VersioningModule

Rule Engine

Direct API

Optimizer

Other KB Ser-vice

FreebaseGRDDL

Microfor-mat

Physical Storage

LoggingModule

Log

Logical Data Model

Object Attribute CategoryRelationInstance

Wikipedia

RDBMS

NavigationModule


Another View of System Frame-work

Knowledge Base

ServiceController

LoggingModule

App. #1

App. #2

App. #3

Data Structure

RuleEngine

Storage Engine

DataVersioning

AP

I Mod

ule

OptimizerI/O

Handler

Log

RDBMS

DataExchangeModule

Other Knowledge Base

Freebase WikipediaStructured

NaverKin

OutsourcingData

Usage Log

Structured Log Data

ServiceRequest

ServiceResult

RequestMessage

API set

Stored Knowledge

ExtractedData

RequestedKnowledgeResult

KnowledgeRequestQuery


Issues and ToDo

ToDo

Detailed Data Structure

– Types of object, attribute, relation and category

– Set of attribute and relation

Implementing data schema in RDBMS

– Setting table and columns up

Collaborating with structured Naver Kin

– Refining the target objects in Naver Kin

Issues

Data gathering method

Data Navigate UI

Knowledge Base Building Project 3 rd meeting 2008. 08. 30.

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Transcript of Knowledge Base Building Project 3 rd meeting 2008. 08. 30.