jung 2010

Copyright © 2004-2010, KISTI1소셜 시맨틱 웹 세미나

1 Apr. 2010

Hanmin Jung

KISTI

Current Issues OfSemantic Web Technologies

in Korea


Presentations (2009)미래연구정보포럼 2009, 2009.12 (대한상공회의소)

Korean Semantic Web Conference 2009, 2009.12 (국립중앙도서관)

Search Technology Summit 2009, 2009.9 (그랜드인터콘티넨탈호텔)

메타데이터표준화포럼국제세미나, 2009.6 (코엑스컨퍼런스센터)

충남대학교 SOREC 연구소세미나, “Semantic Service and Service Mashup”, 2009.12

KISTI 기술이전설명회, “시맨틱웹기술기반정보서비스플랫폼”, 2009.12

KISTI 하반기간부리더쉽교육, “OntoFrame Project”, 2009.12

독일Wolters Kluwer GmbH 세미나, “Semantic Web Research of KISTI”, 2009.12

독일München Univ. 세미나, “Semantic Web Research of KISTI”, 2009.11

KAIST 특강, “시맨틱웹과미래인터넷”, 2009.11

중국 ISTIC 세미나, “Semantic Service Platform and Service Mashup”, 2009.10

영국 Southampton Univ. 세미나, “Semantic Service Researches Of DITR”, 2009.10

행정안전부제2기미래 ICT 리더과정, “ICT 신기술이해”, 2009.10

행정안전부제2기최신 ICT 동향과정, “미래정보서비스와시맨틱웹”, 2009.9

솔트룩스세미나, “특허동향고찰과관련기술분석”, 2009.9

KERIS 세미나, “Toward Web 3.0”, 2009.8

고려대협력워크숍, “시맨틱서비스파이프라이닝”, 2009.8

통계청세미나, “정보서비스에서의시맨틱웹역할과활용방안”, 2009.5

충남대학교대학원특강, “시맨틱웹과서비스플랫폼”, 2009.5

행정안전부제1기최신 ICT 동향과정, “미래정보서비스와시맨틱웹”, 2009.5

충남대학교특강, “시맨틱웹을적용한정보서비스”, 2009.5

충남대학교세미나, “시맨틱서비스플랫폼 OntoFrame”, 2009.4

삼성전자세미나, “전문용어구축및활용”, 2009.4

행정안전부제1기미래 ICT 리더과정, “ICT 신기술이해 -시맨틱웹, 모바일, 차세대미디어등 –”, 2009.4

서울대학교세미나, “국내시맨틱웹시장동향및온토프레임소개”, 2009.4

배재대학교세미나, “차세대인터넷기술”, 2009.4

한국정보사회진흥원세미나, “온톨로지, 시맨틱웹의이해와적용”, 2009.4

KISTEP 세미나, “미래검색동향과시맨틱플랫폼의역할”, 2009.3

정보통신연구진흥원세미나, “미래검색동향과시맨틱플랫폼의역할”, 2009.3

한의학온톨로지세미나, “언어자원구축 –용어수집부터구조정보구축까지 –”, 2009.3

국회도서관설명회, “지능형입법지원시스템 (L-Cube System), 2009.3

한국표준과학연구원세미나, “시맨틱서비스플랫폼 OntoFrame 소개”, 2009.1


Presentations (2010)소셜시맨틱웹세미나, “Current Issues of Semantic Web Technologies in Korea”, 2010.4

NIPA-PS협의체세미나, “An Insight into Future Information Services”, 2010.3

솔트룩스세미나, “Benchmarking Semantic Repositories”, 2010.3

기술사업화정보실세미나, “Semantic Web Research @ KISTI”, 2010.2

국사편찬위원회세미나, “Understanding Semantic Web Technologies with Use Cases”, 2010.2

국민권익위원회세미나, “Use Cases of KISTI”, 2010.2

한국표준과학연구원세미나, “시맨틱웹기술을이용한참조표준온라인보급활성화방안”, 2010.1


NIPA 주간기술동향트리플레파지토리벤치마킹 (Vol.1439)

차세대 IT 기기와HCI 기술동향전망 (Vol.1435)

시맨틱검색기술동향 (Vol.1431)

시맨틱웹국내특허동향 (Vol.1420)

감성분석과브랜드모니터링기술동향 (Vol.1396)

시맨틱웹이경제⋅사회에미치는영향 (Vol.1372)

웹매핑서비스비교분석 (Vol.1352)

시맨틱웹 2.0 기술동향 (Vol.1344)

국내포털검색시장및특허동향 (Vol.1341)

Open API 기술동향 (Vol.1296)

엔터프라이즈검색기술동향 (Vol.1276)

전자상거래검색기술동향 (Vol.1273)

시맨틱웹포털기술동향 (Vol.1264)

기타동향분석보고서미래의인터넷을만드는핵심기술, 시맨틱웹 –월간웹 (2010년 1월호)

시맨틱웹 – 2009 국방정보기술조사서웹 2.0의개념과의의 – KERIS@ Vol.2

미래정보사회와시맨틱웹기술 –디지털행정 (녹색정보화특집, 제 113호)

시맨틱웹서비스 – KERIS 이슈리포트 (2008-22)

시맨틱웹기반플랫폼상에서의웹 2.0 활용서비스 –정보처리학회지 Vol.14

시맨틱웹포털해외사례–지식정보인프라지 Vol.26

Trend Reports


유관사업자문KAIST:국가 IT 온톨로지인프라구축사업한국한의학연구원:온톨로지기반한의학지능형정보체계연구사업안보경영연구원: 지능형 통합검색과 품질관리 기능을 향상시킨 군수목록정보체계 아키텍쳐링 연구사업정보통신산업진흥원: IT 통계분석및동향분석사업솔트룩스:오픈이노베이션타킷발견엔진연구개발사업탑쿼드란트코리아: u-City 서비스용개방형 SW 플랫폼개발사업선도소프트:산재예방통합정보시스템정보화전략계획수립방안에관한연구사업네오플러스:표준화활동지원및관리시스템구축사업DAUM: 디지털문화콘텐츠융⋅복합서비스를위한시맨틱웹매쉬업플랫폼기술개발사업

Advisory Activities (2009-2010)


DB vs. Ontology

Legacy DB

Ontology Schema

Ontology Instances

RDF Triples

Portability & Connectibility

For ServicePlanning ServicesDefining ConceptsExploiting Relations

For Storing & Managing


Ontologies Modeled by KISTI


Ontology Engineering

Key Activities

Understand business objectives

Understand people

Understand processes and systems

Understand technologies

Understand contents

Wlodarczyk, “Implementing Semantic Search in the Enterprise”


Embracing Web 3.0

“We could use Semantic Web technologies’ representational power to describe things in the real world. One view is that the physical objects will become Web-accessible in that we will be able to represent them via metadata. … Describing physical things will expand our scope beyond the current Web.” by Ora Lassila & James Hendler

Internet of Things


Linking Data of Real World

Semantic Web is rapidly becoming real

through

evolutionary step in leading the Web to its potential


Linking Data of Real World

Meaning is learned “inferentially” from a body of data

Tim O’Reilly and John Battelle, “Web Squared: Web 2.0 Five Years On”, 2009.


LOD Project

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

W3C Linking Open Data Community Project

Aims at making data freely available to everyone

Extends the Web with a data commonsBy 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 WebIn a way that other applications can access and interpret the data

Using common Web technologies

http://images.google.co.kr/imgres?imgurl=http://www.clker.com/cliparts/f/3/8/f/1216181212629163782jean_victor_balin_icon_arrow_left_blue.svg.hi.png&imgrefurl=http://www.clker.com/clipart-23553.html&usg=__DptGtUT9rNIyJMOLeTFU3O7CpWU=&h=548&w=600&sz=27&hl=ko&start=18&sig2=6uU9oMsJ6iS6_JdowejNAw&um=1&tbnid=ZRBNiR3CFMQhHM:&tbnh=123&tbnw=135&prev=/images%3Fq%3Darrow%2Bicon%26imgtbs%3Dt%26imgtype%3Dclipart%26hl%3Dko%26rlz%3D1T4GPTB_koKR288KR288%26um%3D1%26newwindow%3D1&ei=_58xS7H1C4HC6gPYpoGNBQ


LOD Project

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


over 500 million RDF triples (2007.5)


LOD Project

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


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

KISTI

OntoFrame 2007 Data Set


LOD Project

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


over 4.5 billion RDF triples (2009.3)


LOD Project

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


over 13.1 billion RDF triples, around 142 millions of links (2009.11)

http://esw.w3.org/TaskForces/CommunityProjects/LinkingOpenData/DataSets/Statistics


LOD Project

http://internet.suite101.com/article.cfm/datagov_provides_showcase_for_public_data

http://data-gov.tw.rpi.edu/wiki/What%27s_in_data.gov

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 (Over 100 agencies)

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)

…


LOD Project

http://www.dailymail.co.uk/sciencetech/article-1244930/Data-gov-uk-Public-website-offering-open-access-Government-data-launched-internet-inventor.html

Data.gov.uk

Prime Minister Gordon Brown appointed Sir Tim and Professor Nigel Shadbolt to open up the official data to the general public (2009.6)

Teamed up with Stephen Trimms, Minister for Digital Britain

More than 2,500 sets of data from across government including information about house prices, local amenities and services, and access to local hospitals (2010.1)


LOD Project

Data.gov.uk Demos


LOD Project

Christian Bizer, Tom Heath, and Tim Berners-Lee, “Linked Data – The Story So Far”, 2009.

URI Aliases

URIs that refer to the same real-world objectsE.g. http://dbpedia.org/resource/Berlin (for Berlin in DBpedia)

E.g. http://sws.geonames.org/2950159 (for Berlin in Geonames)

Information providers can set owl:sameAs links to URI aliases they know about

Resolution of Data Conflicts in Data Fusion

Choosing a value in situations where multiple sources provide different values for the same property of an object


Finding Coreferences –Sindice & <sameAs>

Interlinking the Web of Data


Finding Coreferences –Object Coref in Falcons

Bootstrapping Object Coreference Service

Input URI

http://iws.seu.edu.cn/services/ObjectCoref/


LOD Project

Tim Berners-Lee, “Putting Government Data Online”, 2009.

Government Data

Reasons to put onlineIncreasing citizen awareness of government functions to enable greater accountability

Contributing valuable information about the world

Enabling the government, the country, and the world to function more efficiently


Reasoning

P (Precondition), R (Rule), C (Conclusion)Deduction: P + R → C (for forward-chaining)

Induction: P + C → R

Abduction: C + R → P (for backward-chaining)

E.g. “If man is mortal (R) and Socrates is a man (P), then Socrates is mortal (C)”

S. Lee, “Research Trends on Reasoning Technologies”, 2010.

Semantic Repository


Trends on Reasoning (2009)

Semantic Repository



Trends on Reasoning (2008~2009)

77 oral/poster/demo/PhD/workshop papers in ESWC & ISWC conferences

Semantic Repository



Reasoning

Standard reasoningDL-based

Rule-based

Hybrid (e.g. DL for Tbox, Rule for Abox)

Non-standard reasoningInconsistency handling

Uncertainty reasoning: probabilistic/fuzzy

Inductive reasoning: clustering

Justification finding: exploration of entailments

Approximate reasoning: scarifying for soundness/completeness for efficiency

Distributed reasoning: on multiple ontologies

Parallel reasoning: like multi-threading

Stream reasoning: on rapidly changing information


Semantic Repository


Semantic Repository

Combines characteristics of DBMS and inference engines

Uses ontologies as semantic schemata, which allows them to automatically reason about the data

Holds, interpret, and serve requests from users

Benchmarking Points

Data loading (usually includes inference)

Query evaluation

Data modification

Performance Dimensions

Scale (in terms of RDF triples)

Schema and data complexity

Hardware and software setup

A. Kiryakov, “Semantic Repositories - Performance factors and design choices”, 2010

Semantic Repository


Full-cycle Benchmarking

Loading input RDF triples from the storage system

Parsing the RDF files

Indexing and storing the triples

Forward-chaining and materialization (optional)

Query parsing

Query optimization (optional for query re-writing)

Query evaluationBackward-chaining (optional)

Fetching of the results

(post-processing)

A. Kiryakov, “Semantic Repositories - Performance factors and design choices”, 2010.

Semantic Repository

Copyright © 2004-2010, KISTI35소셜 시맨틱 웹 세미나 35

OntoFrame – Reasoning


Details


Semantic … Service

Semantic Web-based Service ≠ Semantic Service

Named-entity Recognition

Information Extraction

Natural Language Interface

Question Answering

Identity Resolution

(SPARQL) Query Interface

Reasoning

Ontology Modeling


Semantic … Search

Semantic Web-based Search ≠ Semantic Search

Concept Matching

Exploratory Session

Inferential Finding


Semantic Search

Definition

“Semantic search uses language processing to assess the meaning of contents and the meaning of search queries to return more relevant results.” by Paul Wlodarczyk (Early & Associates)

Requires technologiesTo disambiguate search queries (e.g. named entity recognition, WSD)

To map search queries to contents (e.g. information extraction)

To refine meaning of search queries (e.g. clustering, relevant term search)


Semantic Search Engines

Evri



Nate Semantic Search



Naver Lab Movie Search


Application Scopes ofNatural Language Processing

http://www.ukp.tu-darmstadt.de/fileadmin/user_upload/Group_UKP/e-learning2.0qa_sental_small_0.png

http://www.monrai.com/products/cypher/img/ad-framework.gif


Usability

Even the tiniest amount of empirical facts (2 users)vastly improves the probability of making

correct UI design decisions

Jakob Nielsen’s Alertbox, “Guesses vs. Data as Basis for Design Recommendations”, 2009.


Usability

Unless your site meets their expectationsand can be understood immediately,

They’ll beat a fast retreat back to the sitesthey already know.

Jakob Nielsen’s Alertbox, “Guesses vs. Data as Basis for Design Recommendations”, 2009.


Usability

Content Owners’ Subjective Opinions

“Yeah, see, I don’t like that.”

“I wouldn’t click there, and so neither will they.”

“Oh, they’ll know what that means, even if you don’t.”

Jakob Nielsen, “Building Respect for Usability Expertise”, 2009.


Usability

How Projects Really Work

http://www.projectcartoon.com/cartoon/1


Usability

Bad Usability Is Like a Leaky Pipe

http://www.90percentofeverything.com/wp-content/uploads/2006/11/bad_usability_is_like_a_leaky_pipe.jpg


Usability

Usability is about humans, not computers

Jakob Nielsen’s Alertbox, “Progress in Usability: Fast or Slow?”, 2010.

Organizational inertia

Limitations of the human mind


Toward Web 3.0

Recommendation & Personalization

One-sided recommendation → collaborative recommendation →contextual recommendation

“We’ll have a web that knows what we want and when we want it.” by Jemina Kiss (writer of UK’s Guardian newspaper)


Wifi Positioning System

http://www.linuxfordevices.com/files/misc/nyc-wifi-points.jpg

New York City


Recommendation & Personalization

Agents

SNS

Sensors, recognizers

Where to adopt Semantic Web technologies


Top 10 Predictions (1)Worldwide Information Access, Analysis, and Management Software

2010 (IDC)Information in Context

Prediction 1: Experiments in Context and User-Aware Filters Will Increase the Relevance and Usefulness of the Information That Is Retrieved; Location, Device, Task, Job Title, or Personal Interests Will All Provide Clues

Prediction 2: Social Graphs Derived from Social Networks Will Improve Understanding of Organizational Structure and Will Supply Recommendations, Filter Search Results and Content Streams, or Find Experts

Pervasive Business Intelligence and AnalyticsPrediction 3: Data Generated from Transactions, Events, Sensors, Conversations, Purchases, and Social Networks Will Drive Predictive Analysis and Improved Decision-Making Processes

Prediction 4: New SaaS Offerings for BI, Analytics, and Data Provided as a Service Will Address the Lack of Analytics Expertise at Many Organizations

Prediction 5: Mining for Meaning Will Rise in Importance; Text Analytics Will Be Embedded in BI Systems to Merge Content with Data; Other Applications That Require a High Degree of User Interaction Will Add Text Analytics, Search, and Multilingual Features


Top 10 Predictions (2)Worldwide Information Access, Analysis, and Management Software

2010 (IDC)Information Overload and Risk Avoidance Spur Information and Application Integration

Prediction 6: Intelligent Workspaces Will Emerge and Quickly Gain Market Share

Prediction 7: Decision Management Systems Will Emerge to Extend Information Access Systems to All Steps in the Decision-Making Process

Change, Disruption, and New OpportunitiesPrediction 8: Demand for Integrated Platforms to Support eCommerce Will Reemerge

Prediction 9: Software Delivery and Licensing Models Will Diversify to Include More SaaS Delivery, More Appliances, and More Open Source Software

Prediction 10: Chaos in the Workplace Will Increase as Business Users Either Bring Their Own Software Tools to Work or Take Charge of Software Purchases


Work Program 2011-2012

2010 (European Commission)

Challenge 1: Pervasive and Trusted Network and Service Infrastructures

Challenge 2: Cognitive Systems and Robotics

Challenge 3: Alternative Paths to Components and Systems

Challenge 4: Technologies for Digital Content and Languages

Challenge 5: ICT for Health, Ageing Well, Inclusion and Governance

Challenge 6: ICT for a low carbon economy

Challenge 7: ICT for the Enterprise and Manufacturing

Challenge 8: ICT for Learning and Access to Cultural Resources




Challenge 1: Pervasive and Trusted Network and Service InfrastructuresObjective ICT-2011.1.1: Future Networks

Objective ICT-2011.1.2: Cloud Computing, Internet of Services and Advanced Software Engineering

Objective ICT-2011.1.3: Internet-connected objects

Objective ICT-2011.1.4: Trustworthy ICT

Objective ICT-2011.1.5: Networked Media and Search Systems

Objective ICT-2011.1.6: Future Internet Research and Experimentation (FIRE)

Objective FI.ICT-2011.1.7: Technology foundation: Future Internet Core Platform

Objective FI.ICT-2011.1.8: Use Case scenarios and pilots

Objective FI.ICT-2011.1.9: Capacity Building and Infrastructure Support

Objective FI.ICT-2011.1.10: Program Management and Support




Challenge 4: Technologies for Digital Content and LanguagesObjective ICT-2011.4.1: SME initiative on Digital Content and Languages

Objective ICT-2011.4.2: Language TechnologiesMultilingual content processingInformation access and miningNatural spoken interactionDeveloping joint plans, methods and services (speech & natural language)

Objective ICT-2011.4.3: Digital Preservation

Objective ICT-2011.4.4: Intelligent Information Management


감사합니다!

[email protected]


감사합니다!

[email protected]

조직외부에서누군가여러분의문제에대해답하고, 해결해주며,

현재의기회를잘활용하는방법을알고있다면,

그들을찾아내생산적으로협업할길을찾기만하면된다.by A.G. Lafley (P&G CEO)

jung 2010

Documents

lod project

semantic repository

worldwide

semantic web

languages

semantic web

semantic repositories

search queries