OntoFrame기반 시맨틱 서비스와 서비스 매쉬업

Semantic Service and Service Mashup
on OntoFrame

4 Dec. 2009

Hanmin Jung
KISTI

Korean Semantic Web Conference 2009 1 Copyright © 2004-2009, KISTI

From the Oral
to the Electric Age

Derrick, Brainframes of the new economy, 2008


Next Generation Web

Web of Documents
↓
Web of Data


Linking Data of Real World

Semantic Web is rapidly becoming real


LOD Project
W3C Linking Open Data Community Project
Aims at making data freely available to everyone
Extends the Web with a data commons
By publishing various open data sets as RDF on the Web
By setting RDF links between data items from different data sources

Opens the (meta)data silos and get rid of repository-centric mindset
Publishes (meta)data of public interest on the Web
In a way that other applications can access and interpret the data
Using common Web technologies

http://esw.w3.org/topic/SweoIG/TaskForces/CommunityProjects/LinkingOpenData/
Bernhard Haslhofer, “Linked Data Tutorial”, 2009.


LOD Project
over 500 million RDF triples (2007.5)

http://blogs.sun.com/bblfish/resource/2007/LinkingOpenData.png


LOD Project
over 2 billion RDF triples (2008.4)
Available in RDF and SVG (Scalable Vector Graphics) versions

KISTI

http://richard.cyganiak.de/2007/10/lod/


LOD Project
over 4.5 billion RDF triples (2009.3)

http://www4.wiwiss.fu-berlin.de/bizer/pub/lod-datasets_2009-03-27_colored.png


LOD Project
over 4.7 billion RDF triples (2009.5)

http://esw.w3.org/topic/SweoIG/TaskForces/CommunityProjects/LinkingOpenData/


LOD Project
LOD Meetings and Gatherings
Face-face meetings
ISWC2007~2009
ESWC2007~2009
WWW2007~2009

Conferences
Linked AI: Linked Data Meets Artificial Intelligence at AAAI2010
Linked Data on the Web Workshop at WWW2009
How to Publish Linked Data on the Web tutorial at ISWC2008
Data Integration through Semantic Technology Workshop at ASWC2008
Linked Data Planet 2008
Linked Data on the Web Workshop at WWW2008

http://esw.w3.org/topic/SweoIG/TaskForces/CommunityProjects/LinkingOpenData


LOD Project
ISWC Billion Triple Challenge (2008 ~)
focuses on pushing the limits in tool design on the fronts of scalability in
size and robustness in the face of data typically found on the Web
Generates new application ideas, i.e. to show what is possible with Web
metadata today
2009 Dataset
1.14 billion statements
1,464,829,200 nodes (resources of 866,094,100, blank nodes of 352,032,700,
literals of 246,702,400)
Includes 3 ontology classes modeled by KISTI out of Top-50 classes
(1,343,200+ instances)

http://ebiquity.umbc.edu/blogger/2007/12/06/the-semantic-web-billion-triples-challenge-at-iswc-2008/
http://vmlion25.deri.ie/index.html


LOD Project
Data.gov
A product of Obama’s Open Government Initiative project
Launched in late May 2009 by the Federal CIO, Vivek Kudra
Aims to increase public access to high value, machine readable datasets
generated by the Executive Branch of the Federal Government
Major sources of dataset (2009-06-24)
Environmental Protection Agency (315)
Department of Defense (122)
Centers for Medicare and Medicaid Services (108)
Department of Health and Human Services (87)
Department of Homeland Security (43)
Department of the Treasury (37)
Department of the Interior (35)
US Bureau of Labor Statistics (35)
Department of Labor (35)
…

http://internet.suite101.com/article.cfm/datagov_provides_showcase_for_public_data
http://data-gov.tw.rpi.edu/wiki/What%27s_in_data.gov


LOD Project
Data-gov Wiki
Intents to assist users to create new Web applications that will help
individuals, communities, and business use public data
977 datasets contributed by 54 US government agencies (2009-11-08)
116 translated RDF datasets
5,074,936,293 triples.
7,564 properties.

Published a Linked Data version of the eGovernment data on 2009-10-15

http://wiki.dbpedia.org/About


LOD Project
Data.gov Demos


LOD Project
Data.gov in Linked Data Demos


Case Study – Rich Snippets
Google Rich Snippets
Helps to annotating Web pages with structured data in a standard format
Microformats and RDFa

Gives users convenient summary information about their search results
Reviews and ratings for a product or service
Distinguishing between people with the same name

http://googlewebmastercentral.blogspot.com/2009/05/introducing-rich-snippets.html


Case Study – Semantic Dog Food
Semantic Web Conference Corpus


Case Study – <sameAs>
Interlinking the Web of Data


OntoFrame Project (2009)

KATS MOJ

OntoURI® Mariner®
OntoReasoner®

KISTI Ontologies KATS Ontologies MOJ Ontologies


OntoFrame Project (2010)

2010

2010 LOD KATS MOJ

OntoURI® Mariner®
OntoReasoner®

LOD Cloud KATS Ontologies MOJ Ontologies

KISTI Ontologies


OntoFrame - Introduction
What is?
Semantic Web-based service platform (providing implementation
environments)
Purpose
Search information and discover implicit knowledge in the information
for helping users to achieve their needs efficiently
“This bi-modal existence makes sense because people behave in two way.”
by Jesse


OntoFrame – 2007 Data
Entities
Papers: 362,653 (CiteSeer international papers with full-texts)
Persons: 983,677
Topics: 425,294
Institutions: 17,820
Locations: 451,763 (with GPS coordinate)
RDF Triples: 58,885,581 (2007.12)


OntoFrame – 2008 Data
Entities
Papers: 453,124 (Elsevier international journal papers with full-texts)
Persons: 1,352,220
Topics: 339,947
Institutions: 91,514
Locations: 409,575 (with GPS coordinate)
RDF Triples: 283,087,518 (2008.11)


OntoFrame – S3 Data
Entities
Papers: 3,560,601 (Elsevier international journal papers)
Others: TBD, Over 11 millions for persons


OntoFrame – S3
STAR-enabled Semantic Service Version
Features
Cross-lingual IR
User-center UI design
Entity-centric searches gathered by type
Coupling with KISTI-STAR services
Sophisticated semantic services
Research topics of a research group
Leading researchers on the major topics of a researcher
Topic trends of a researcher
Articles similar with an article
Expanded tooltips


OntoFrame
OntoURI Architecture

User Interface

Mapping Rule Manager

DB-to-OWL Mapping Rule
DB-to-OWL Mapping Rule
Reasoning
Web Services Interface

RDF Triple Store
Engine
RDF Triple Store

RDF Triple Generator Ontology Browser

Identity Generator
Identity Resolver
Search Ontology
Ontology
Engine

Topic Scheduler
Extractor

DB Crawler

Database
Database


OntoFrame – Reasoning


OntoFrame – Reasoning

57건 (2009.12 기준)


Mashup
What’s the Next Step in Services?
“Ontologies are reasoned,
Queries are resolved,
Workflows are executed” by Mark Wilkinson (UBC)

http://paggr.com/media/2008/10/paggr_poster_1000.png


Mashup
Yahoo Pipes
a composition tool to aggregate, manipulate, and mashup content from
around the web


Mashup
Yahoo Pipes
Use case: US Population by State


Mashup
Service Mashup Editor

Florian Daniel, “Mashing up Search Services”


Mashup
Service Mashup Platform

Florian Daniel, “Mashing up Search Services”


OntoPipeliner
Level of Semantics Service Classification

Search &
Semantically- Fully-automatic
Semantically- Reasoning
operated Services Services Composition/
operated Services Mashup

Classes and properties are OntoPipeliner
embedded and referred

Semantic Web Semi-automatic
Semantic Web OWL-S,
Services WSMO Composition/
Services Mashup
Web service discovery, WSMO Studio,
invocation, and composition alphaWorks, …
with ontologies

Manual
Web Services
Web Services Google Maps, Composition/
Amazon E-commerce, … Mashup

Interoperable machine-to-machine interaction over a network with WSDL Yahoo Pipes MS,
Popfly, …

Level of Ontology Usage

OntoPipeliner
What is?
Semantic service mashup framework
Purpose
Discover and Compose semantically-operated services to get meaningful
and valuable composite services or workflows


OntoPipeliner
Architecture

Semantic User Interface
Service
Pipeliner Constraint Manager Pipeline Visualizer

Pipeline Generator Pipeline Executor
Semantic
Broker
Semantic Service Searcher

URI Server
URI Server
Semantic Service Register
User Interface

Semantic Semantic
Semantic
Service
Service
Service Repository
Repository
Manager
Ontology Manager Ontology
Ontology


OntoPipeliner – Data
Services
Services: 57 (KISTI, KATS, MOJ)


OntoPipeliner
Steps on OntoFrame S3


OntoPipeliner
Pipeline Example


Demonstration


Presentations (2009)
미래 연구정보 포럼 2009, 2009.12 (대한상공회의소)
Korean Semantic Web Conference 2009, 2009.12 (국립중앙도서관)
Search Technology Summit 2009, 2009.9 (그랜드 인터콘티넨탈 호텔)
메타데이터표준화포럼 국제세미나, 2009.6 (코엑스 컨퍼런스 센터)

독일 Wolters Kluwer GmbH 세미나, “Semantic Web Research of KISTI”, 2009.12
독일 München Univ. 세미나, “Semantic Web Research of KISTI”, 2009.11
KAIST 특강, “시맨틱 웹과 미래 인터넷”, 2009.11
중국 ISTIC 세미나, “Semantic Service Platform and Service Mashup”, 2009.10
영국 Southampton Univ. 세미나, “Semantic Service Researches Of DITR”, 2009.10
행정안전부 제2기 미래 ICT 리더 과정, “ICT 신기술 이해”, 2009.10
행정안전부 제2기 최신 ICT 동향 과정, “미래 정보 서비스와 시맨틱 웹”, 2009.9
솔트룩스 세미나, “특허 동향 고찰과 관련 기술 분석”, 2009.9
KERIS 세미나, “Toward Web 3.0”, 2009.8
고려대 협력 워크숍, “시맨틱 서비스 파이프라이닝”, 2009.8
통계청 세미나, “정보 서비스에서의 시맨틱 웹 역할과 활용 방안”, 2009.5
충남대학교 대학원 특강, “시맨틱 웹과 서비스 플랫폼”, 2009.5
행정안전부 제1기 최신 ICT 동향 과정, “미래 정보 서비스와 시맨틱 웹”, 2009.5
충남대학교 특강, “시맨틱 웹을 적용한 정보 서비스”, 2009.5
충남대학교 세미나, “시맨틱 서비스 플랫폼 OntoFrame”, 2009.4
삼성전자 세미나, “전문용어 구축 및 활용”, 2009.4
행정안전부 제1기 미래 ICT 리더 과정, “ICT 신기술 이해 - 시맨틱 웹, 모바일, 차세대 미디어 등 –”, 2009.4
서울대학교 세미나, “국내 시맨틱 웹 시장 동향 및 온토프레임 소개”, 2009.4
배재대학교 세미나, “차세대 인터넷 기술”, 2009.4
한국정보사회진흥원 세미나, “온톨로지, 시맨틱 웹의 이해와 적용”, 2009.4
KISTEP 세미나, “미래 검색 동향과 시맨틱 플랫폼의 역할”, 2009.3
정보통신연구진흥원 세미나, “미래 검색 동향과 시맨틱 플랫폼의 역할”, 2009.3
한의학 온톨로지 세미나, “언어 자원 구축 – 용어 수집부터 구조 정보 구축까지 –”, 2009.3
국회도서관 설명회, “지능형 입법 지원 시스템 (L-Cube System), 2009.3
한국표준과학연구원 세미나, “시맨틱 서비스 플랫폼 OntoFrame 소개”, 2009.1


Trend Reports
NIPA 주간기술동향
시맨틱 검색 기술 동향 (To appear in 2010)
시맨틱 웹 국내 특허 동향 (Vol.1420)
감성 분석과 브랜드 모니터링 기술 동향 (Vol.1396)
시맨틱 웹이 경제⋅사회에 미치는 영향 (Vol.1372)
웹 매핑 서비스 비교 분석 (Vol.1352)
시맨틱 웹 2.0 기술 동향 (Vol.1344)
국내 포털 검색 시장 및 특허 동향 (Vol.1341)
Open API 기술 동향 (Vol.1296)
엔터프라이즈 검색 기술 동향 (Vol.1276)
전자상거래 검색 기술 동향 (Vol.1273)
시맨틱 웹 포털 기술 동향 (Vol.1264)

기타 동향 분석 보고서
시맨틱 웹 – 2009 국방정보기술 조사서 (To appear)
웹 2.0의 개념과 의의 – KERIS 사보 (Vol.6(1))
미래 정보 사회와 시맨틱 웹 기술 – 디지털행정 (녹색 정보화 특집, 제 113호)
시맨틱 웹 서비스 – KERIS 이슈리포트 (2008-22)
시맨틱 웹 기반 플랫폼상에서의 웹 2.0 활용 서비스 – 정보처리학회지 Vol.14
시맨틱 웹 포털 해외 사례– 지식정보인프라지 Vol.26


“A lot of times, people don’t know what they want until you show it to them.”
by Steve Jobs

“Many people won’t be convinced until they’ve seen it for themselves.”
by Jakob Nielsen

Thank you
jhm@kisti.re.kr


OntoFrame기반 시맨틱 서비스와 서비스 매쉬업

Recomendados

Recomendados

Mais conteúdo relacionado

Semelhante a OntoFrame기반 시맨틱 서비스와 서비스 매쉬업

Semelhante a OntoFrame기반 시맨틱 서비스와 서비스 매쉬업 (20)

Mais de webscikorea

Mais de webscikorea (7)

Último

Último (20)

OntoFrame기반 시맨틱 서비스와 서비스 매쉬업