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

2. Copyright  2008 by CEBT What We Did In Last Week  1 st meeting (2008. 08. 25) Surveying several papers and applications Presenting and discussing the survey results  2 nd 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

3. Copyright  2008 by CEBT Additional Survey on GoogleBase  Google Base 의 모델에 대한 보충 Presentation Storage 는 attribute, Description, Picture or file(Xml, HTML, so on) 등 의 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

4. Copyright  2008 by CEBT 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 information  “What we have to do?” Designing the general data structure of our own – 1 st 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

5. Copyright  2008 by CEBT 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 resources as target knowledge in general way Regarding how to gather and reflect users’ collective intelligence in our system easily Attaching the structured Naver Kin service into the system

6. Copyright  2008 by CEBT 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

7. Copyright  2008 by CEBT Initial Data Structure OBJECT category relation category relation CATEGORY instance relation instance relation INSTANCE relation map relation map attribute relation attribute relation ATTRIBUTE RELATION object relation OBJECT

8. Copyright  2008 by CEBT Initial Data Structure: Example Obj:CAMERA Rel: hasCategory Obj:Optical Electronics hasInstance Obj:CANON Rel: subClassOf Rel: hasAttribute Rel: hasAttribute Att:Size Obj:hasRelation Rel: hasWiki Rel: hasWiki Obj:Document URL Att:Pixel Rel: hasCategory Rel: hasCategory Obj:Multimedia Devices Att:CategoryID A set of relation can be adapted from product ontology (cf. GoodRelation) Be able to specify a category for Standard categorization (cf. UNSPSC) Att and Rel type also has attribute type Entity (type) Relation (type) Connection Edge has their own weight Which means the probability of confidence

9. Copyright  2008 by CEBT 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:Release Date Obj: Christopher Nolan Rel:hasValue Obj: 6 August 2008

10. Copyright  2008 by CEBT 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 Weight Rel_ProCat Pro_Id Cat_Id Weight

11. Copyright  2008 by CEBT Collaboration with structured Naver Kin Object Question Answer Question URL Answer URL Author UserId hasAtt URL CategoryId Rel: Genre Category Object_IDRelation_IDQuestion_URLAnswer_URLQuestion_User_IdAnswer_User_IdAtt_Id 0000000100000010http://kin.naver. com/q1.html http://kin.nave r.com/a1.html questionerWalkdic00000100 0000000200000011http://kin.naver. com/q2.html http://kin.nave r.com/a2.html newbiemasterofkin00000101 ……

12. Copyright  2008 by CEBT Simple System Framework Knowledge Base End-user Applications Service Interface Web Browser API Engine Mobile App Service Controller Service Controller Data Exchange Module Data Exchange Module Storage Engine Inference Module Physical I/O Handler Physical I/O Handler Versioning Module Rule Engine Direct API Optimizer Other KB Service Freebase GRDDL Microformat Physical Storage Logging Module Logging Module Log Logical Data Model Object Attribute Category Relation Instance Wikipedia RDBMS Navigation Module

13. Copyright  2008 by CEBT Another View of System Framework Knowledge Base Service Controller Service Controller Logging Module App. #1 App. #2 App. #3 Data Structure Rule Engine Storage Engine Data Versioning API Module Optimizer I/O Handler Log RDBMS Data Exchange Module Other Knowledge Base Freebase Wikipedia Structured Naver Kin Outsourcing Data Usage Log Structured Log Data Service Request Service Result Request Message API set Stored Knowledge Extracted Data Requested Knowledge Result Knowledge Request Query

14. Copyright  2008 by CEBT 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