1 / 14

Knowledge Base Building Project 3 rd meeting

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

effie
Télécharger la présentation

Knowledge Base Building Project 3 rd meeting

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Knowledge Base Building Project3rd meeting 2008. 08. 30

  2. 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

  3. Additional Survey on GoogleBase • Google Base의 모델에 대한 보충 Presentation • Storage는 attribute, Description, Picture or file(Xml, HTML, so on)등의 정보로 이루어짐 • Basic Attribute는 item type에 따라 정해짐 • Category, Item, Attribute는 사용자가 추가 가능 • Item들간의 Relationship은 없는 것 같음 • File은 대략 15개 정도까지 올릴 수 있음. 20MB 정도를 한계점으로 지정 • API 리스트는 Google base Homepage에서 찾아볼 수 있음. 그것을 보고 어떤 기능을 제공해야 할 지를 생각해 볼 수 있을 것 같음

  4. 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 • 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

  5. 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. 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. Initial Data Structure RELATION OBJECT objectrelation relation map attribute relation OBJECT category relation CATEGORY instance relation ATTRIBUTE INSTANCE

  8. Initial Data Structure: Example Obj:hasRelation Obj:DocumentURL A set of relation can be adapted fromproduct ontology (cf. GoodRelation) Rel: hasWiki Rel:subClassOf Be able to specify a category for Standard categorization (cf. UNSPSC) Att and Rel type also has attribute type Rel: hasAttribute Obj:CAMERA Rel:hasCategory Obj:OpticalElectronics Att:Size Rel: hasCategory Obj:MultimediaDevices hasInstance Att:Pixel Edge has their own weight Which means the probability of confidence Att:CategoryID Obj:CANON Entity (type) Relation (type) Connection

  9. Initial Data Structure: Example (cont’d) Obj:Relation Obj:DocumentURL Rel:wikiURL Rel:hasRelation Rel:hasDirector Obj:Dark Knight Rel:Genre Obj:Action Att:MovieDirector Rel:Genre Obj:Crime Rel:ReleaseDate Rel:hasValue Obj:Christopher Nolan Att:ReleaseDate Entity (type) Obj:6 August2008 Relation (type) Connection

  10. Data Schema: Example

  11. Collaboration with structured Naver Kin Object QuestionURL Question hasAtt URL AnswerURL Answer CategoryId Rel:Genre Author UserId Category

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

  13. Another View of System Framework ServiceRequest RequestMessage Knowledge Base API Module Service Controller App. #1 Data Structure App. #2 API set App. #3 ServiceResult KnowledgeRequest Query Usage Log DataExchangeModule LoggingModule Structured Log Data ExtractedData RequestedKnowledge Result Storage Engine Log RuleEngine DataVersioning OutsourcingData Optimizer I/OHandler Other Knowledge Base Freebase Wikipedia StructuredNaverKin Stored Knowledge RDBMS

  14. 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

More Related