ontology engineering to enrich linked data

Ontology Engineering to　　　 Enrich Linked Data

Kouji KozakiThe Institute of Scientific and Industrial Research (I.S.I.R),

Osaka University, Japan

IASLOD 2012 -International Asian Summer School on Linked Data13-17 Aug. 2012, KAIST, Daejeon, Korea

2012/08/15 1IASLOD 2012

Self introduction: Kouji KOZAKI

Brief biography 2002 Received Ph.D. from Graduate School of Engineering, Osaka University. 2002- Assistant Professor, 2008- Associate Professor in ISIR, Osaka University.

Specialty Ontological Engineering

Main research topics Fundamental theories of ontological engineering Ontology development tool based on the ontological theories Ontology development in several domains and ontology-based application

Hozo( 法造 ) -an environment for ontology building/using- (1996- ) A software to support ontology （ = 法） building （ = 造）

and use It’s available at http://www.hozo.jp 　 as a free software

Registered Users ： 3,500 (June 2012) Java API for application development is provided. Support formats: Original format, RDF(S), OWL. Linked Data publishing support is coming soon.

2012/08/15 IASLOD 2012 2

Cooperator: Enegate Co, ltd.

My history on Ontology Building

2002-2007　Nano technology ontology Supported by NEDO(New Energy and Industrial Technology Development Organization)

2006-　Clinical Medical ontology Supported by Ministry of Health, Labour and Welfare, Japan Cooperated with: Graduate School of Medicine, The University of Tokyo.

2007-2009　Sustainable Science onology Cooperated with: Research Institute for Sustainability Science （RISS） , Osaka

University. 2007-2010　 IBMD(Integrated Bio Medical Database)

Supported by MEXT through "Integrated Database Project". Cooperated with: Tokyo Medical and Dental University, Graduate School of Medicine, Osaka U.

2008-2012　Protein Experiment Protocol ontology Cooperated with: Institute for Protein Research, Osaka University.

2008-2010　Bio Fuel ontology Supported by the Ministry of Environment, Japan.

2009-　Disaster Risk ontology Cooperated with: NIED (National Research Institute for Earth Science and Disaster Prevention)

2012- Bio mimetic ontology Supported by JSPS KAKENHI Grant-in-Aid for Scientific Research on Innovative

Areas2012/08/15 IASLOD 2012 3

Agenda (1) Trends of Linked Data in Semantic

Web Conferences from ontological viewpoints.

(2) How ontologies are used in Linked Data An analysis of Semantic Web applications. 9 types of ontology usages x 5 types of ontologies

(3) Ontology Engineering to Enrich Linked Data

2012/08/15 IASLOD 2012 4

Semantic Web Conference

ISWC ： International Semantic Web Conference 2001 Symposium@ Stanford University, California, USA

Participants 245, submissions 58, acceptance rate 60 ％ No workshops, 3 tutorials

2002- Annual conference, Venue: Europe → USA → Asia 2011 ISWC2011@Bonn, Germany

Participants 597, submissions 264, acceptance rate 19 ％ 16 workshops, 6 tutorials

ESWC ： European Semantic Web Conference 2004 Symposium, 2005- Annual conference. 2010- Extended Semantic Web Conference.

ASWC ： Asian Semantic Web Conference 2006- twice / three years 2011 JIST2011 （ The Join International Semantic Technology

Conference) Jointed with CSWC2011 (The 5th Chinese Semantic Web

Conference)2012/08/15 IASLOD 2012 5

Venues of International Semantic Web Conferences

2012/08/15 IASLOD 2012 6

ISWC ESWC ASWCSWWS ＠ California, USA

ISWC2002 ＠ Sardinia, Italy

ISWC2003 ＠ Sanibel Island,FL,USA Symposium@Osaka, WS@Nara

ISWC2004 ＠ Hiroshima, Japan ESWS ＠ Heraklion, Greece

ISWC2005 ＠ Galway, Ireland ESWC2005 ＠ Heraklion, Greece

ISWC2006 ＠ Athens, GA, USAESWC2006 ＠

Budva,MontenegroASWC2006@Beijing,China

ISWC2007&ASWC2007 ＠

Busan,Korea

ESWC2007 ＠ Innsbruck,

Austria

ISWC2008 ＠ Karlsruhe, Germany ESWC2008 ＠ Tenerife, Spain ASWC2008@Bangkok, Thailand

ISWC2009 ＠ Washington

D.C.Area,USAESWC2009 ＠ Heraklion, Greece ASWC2009@Shanghai, China

ISWC2010 ＠ Shanghai, China ESWC2010 ＠ Heraklion, Greece

ISWC2011 ＠ Bonn.Germany ESWC2011 ＠ Heraklion, Greece JIST2011@Hangzhou, China

ISWC2012 ＠ Boston, USA ESWC2012 ＠ Heraklion, Greece JIST2012@Nara, Japan

ISWC2013 ＠ Sydney, Australia ESWC2013@Montpellier, France (JIST2013@Korea)

2012/08/15 IASLOD 2012 7

JIST 2012, 2-4 Dec. 2012, Nara, Japan - Submission due : 24 Aug. 2012. - It has a Special Track on Linked Datahttp://www.ei.sanken.osaka-u.ac.jp/jist2012/



ISWC2003 ＠ Sanibel Island,FL,USASymposium@Osamka,

WS@Nara






Busan,Korea


Austria






ISWC2012 ＠ Boston, USA ESWC2012 ＠ Heraklion, Greece

ISWC2013 ＠ Sydney, Australia

Research Trends in Semantic Web Conferences(1/3)

2012/08/15 IASLOD 2012 8

Frequency Question / Discussion:“I can understand the basic idea of Semantic Web. However, who describes meta data?”

Basic technologies of Semantic Web are mainly discussed. DAML, OIL→ predecessor of OWL, Rule-ML, Ontology…




WS@Nara






Busan,Korea


Austria







ISWC2013 ＠ Sydney, Australia2012/08/15 IASLOD 2012 9

As an answer to the question “Who describes meta data?”Usage of Social Network System, Web2.0 were actively discussed. Blog, RSS, FOAF, 　 WiKi 　…

・ Collaborative Development of Ontologies was one of hot topics.・ Many Semantic Web based applications were developed.





WS@Nara






Busan,Korea


Austria







ISWC2013 ＠ Sydney, Australia

： the numbers of research track papers whose title includes “Linked Data”.

2012/08/15 IASLOD 2012 10

★The first presentation of DBPedia.(DBPedia was presented also at WWW2007.)

A Special Session on Linked Data

10

8 3

4 Debate - Linked Data: Now what?

After DBPedia, Linked Data became the hottest research topic in Semantic Web Conference.


Summary of the trends in SWC

Changes of main research topics Semantic processing using metadata based on ontologies “Who describes meta data?” → Collaborative building, Web2.0 Linking between Data (instances) ： Linked Data

2012/08/15 IASLOD 2012 11

Rich

seman

tics

Scalability

(Ideal) Semantic Web

Simple/ easy to use Tag （ RSS,FOAF ）

SNS ・ Web2.0

Linked Data×

ISWC2011/ESWC2011: Keynote Keynotes in ISWC2011/ESWC2011 also

discussed trends of Semantic Web research ． ISWC2011: Keynote by Frank van Harmelen

10 Years of Semantic Web: 　　　　　　　　　　　　

does it work in theory?Available at 　 http://www.cs.vu.nl/~frankh/spool/ISWC2011Keynote/

ESWC2011: Keynote by James A. Hendler “Why the Semantic Web 　　　　　　　　　　

　　will Never Work”

Available at 　 http://www.eswc2009.org/

Common claims Ontology 　 << 　 Data (instance) ＝ LOD LOD is main application in resent Semantic

Web2012/08/15 IASLOD 2012 12

http://www.eswc2009.org/



2012/08/15 IASLOD 2012 13

From ISWC2011: Keynote by Frank van Harmelen

Terminological knowledge is much smaller than the factual knowledge

From ESWC2011: Keynote by James A. Hendler

2012/08/15 IASLOD 2012 14

What does “Ontology << Data” means?

It is true that the number of data (instances) linked in LOD is many more than the number of concepts (types) .

However, it is not the right claim ”We do not need ontology.”, “Minimum ontologies are enough (for LOD).” , “Linking data is more important.” ．

Because we can use huge scales of LOD, it is required to deal with their semantics appropriately and to realize advanced semantic processing.

2012/08/15 15

Rich sem

antics

Scalability

(Ideal) Semantic Web

Simple/ easy to use Tag （ RSS,FOAF ）

SNS ・ Web2.0

Linked Data×

It is an importantproblem tobridge the GAP.

How to use LOD.

How to deal with semantics.

IASLOD 2012

From ISWC2011 ：Opening

2012/08/15 IASLOD 2012 16

increase

increase decreaseNot change

ISWC2011:Research Papers

Research Tracks (three papers in each sessions) Web of Data Social Web User Interaction RDF Query - Alternative Approaches RDF Query - Performance Issues RDF Query - Multiple Sources RDF Data Analysis Policies and Trust MANCHustifications and Provenance KR – Reasoners KR - Semantics Formal Ontology & Patterns Ontology Evaluation Ontology Matching, Mapping

2012/08/15 IASLOD 2012 17

How to use Linked Data

How to deal with Semantics

ISWC2011:Wrokshops Consuming Linked Data※ Detection, Representation, and Exploitation of Events Knowledge Evolution and Ontology Dynamics Linked Science※ Multilingual Semantic Web Ontologies come of Age Ontology Matching Ordering and Reasoning Scalable Semantic Web Knowledge Base Systems Semantic Personalized Informaton Management Semantic Sensor Networks Semantic Web Enabled Software Engineering Social Data on the Web Terra Cognita - Foundations, Technologies and Applications of

the Geospatial Web Uncertainty Reasoning for the Semantic Web Web Scale Knowledge Extraction

2012/08/15 IASLOD 2012 18

※Workshops whose main topic is Liked Data

※Workshops whose main topic is Liked Data

ISWC2011:Wrokshops Consuming Linked Data※ Detection, Representation, and Exploitation of Events Knowledge Evolution and Ontology Dynamics Linked Science※ Multilingual Semantic Web Ontologies come of Age Ontology Matching Ordering and Reasoning Scalable Semantic Web Knowledge Base Systems Semantic Personalized Informaton Management Semantic Sensor Networks Semantic Web Enabled Software Engineering Social Data on the Web Terra Cognita - Foundations, Technologies and Applications of

the Geospatial Web Uncertainty Reasoning for the Semantic Web Web Scale Knowledge Extraction

2012/08/15 IASLOD 2012 19

The 2nd workshop on Consuming Linked Data・ big workshop (participants: 70-80)・ acceptance rate: about 50%・ Papers about basic technologies are more than applications.★Some organizers (participants) argue that　“ I want to got more paper about application of LOD.”　“ We have to know (practical/concrete) Needs for LOD”

Linked Data-a-thon・ A contest whose theme is to develop LOD application within 2 weeks.・ Given Resources for the subject is conference information of ISWC.・ Only 3 submissions. (All of them got prize…)

http://iswc2011.semanticweb.org/?id=100




Web Conferences from ontological viewpoints. SW → 　 Web2.0 　→　 LOD How to use LOD? How to deal with semantics?

(2) How ontologies are used in Linked Data It is based on my presentation in ASWC2008,

“Understanding Semantic Web Applications”. An analysis of Semantic Web applications (including

LOD). Method: 9 types of ontology usages x 5 types of

ontologies


2012/08/15 IASLOD 2012 20

Motivation for SW application analysis Background

About 10 years after the birth of Semantic Web (SW) [A roadmap to the Semantic Web, Sep 1998, Tim Berners-Lee]

Fundamental technologies for SW RDF(S), OWL, SPARQL, SWRL … etc.

So many SW applications In spite of so many efforts on research and development of

SW technologies, “Killer Application” of SW is still unknown [Alani 05, Motta 06].

Motivation It would be beneficial for us to get an overview of the

current state of SW applications to consider next direction of SW.

Our approach We analyzes SW Apps from the view point of ontology. Especially we focus on “What type of ontologies is used”

and “How ontologies are used.” 　2012/08/15 IASLOD 2012 21

Steps for Analyzing SW Applications from Ontological Viewpoint

We analyzed 190 SW applications which utilize ontologies extracted from Semantic Web conferences according to the following steps: (1) Giving short explanations about the application.

(One sentence for each) (2) Identifying the type of usage of ontology

(9 categories). (3) Identifying the target domain. (4) Identifying types of ontology (5 categories). (5) Identifying the language for description.

(RDF(S), OWL, DAML+OIL, …etc) (6) Identifying the scale of ontology.

(number of concepts and/or instance models)

On the way of this analysis, we discussed about the criteria for classification of applications interactively.

2012/08/15 IASLOD 2012 22

The number of SW applications which is analyzed

2012/08/15 23

Conferences Dates VenuesNumber

　 of Apps

International Semantic Web Conference (ISWC)ISWC2002 Jun. 9-12, 2002 Sardinia, Italy 9ISWC2003 Oct.20-23, 2003 Sanibel Island,FL,USA 19ISWC2004 Nov. 7-11, 2004 Hiroshima, Japan 18ISWC2005 Nov. 6-10, 2005 Galway, Ireland 25ISWC2006 Nov.5-9, 2006 Athens, GA, USA 26ISWC2007&ASWC2007 Nov.11- 15, 2007 Busan, Korea 18European Semantic Web Conference (ESWC)ESWC2005 May29-Jun.1,2005 Heraklion, Greece 24ESWC2006 Jun.11-14, 2006 Budva, Montenegro 11ESWC2007 Jun. 03 - 07, 2007 Innsbruck, Austria 17Asian Semantic Web Conference (ASWC) ASWC2006 Sep.3- 7, 2006 Beijing, China 23

※SW and ontology engineering tools (e.g. ontology editors, ontology alignment tool) are not the target of the analysis.

IASLOD 2012

Steps for Analyzing SW Applications from Ontological Viewpoint

We analyzed 190 SW applications which utilize ontologies extracted from Semantic Web conferences according to the following steps: (1) Giving short explanations about the application.

(One sentence for each) (2) Identifying the type of usage of ontology

(9 categories). (3) Identifying the target domain. (4) Identifying types of ontology (5 categories). (5) Identifying the language for description.

(RDF(S), OWL, DAML+OIL, …etc) (6) Identifying the scale of ontology.

(number of concepts and/or instance models)

On the way of this analysis, the authors discussed about the criteria for classification of applications interactively.

2012/08/15 IASLOD 2012 24

Types of Usage of Ontology (1) Common Vocabulary (2) Semantic Search (3) Systematized Index (4) Data Schema (5) Media for Knowledge

Sharing (6) Semantic Analysis (7) Information Extraction (8) Rule Set for Knowledge

Models (9) Systematizing Knowledge

2012/08/15 IASLOD 2012 25

Shallow

Deep

Ontology applications scenarios 1)neutral authoring

2)common access to information3)indexing for search

The role of an ontology1)a common vocabulary2)data structure3)explication of what is left implicit4)semantic interoperability 5)explication of design rationale6)systematization of knowledge7)meta-model function 8)theory of content

Types of Usage of Ontology for a SW Application(1/5)

Basically, a SW application is categorized to one of the types according to its main purpose.

Some SW applications which use ontology for multiple ways are categorized to multiple categories.

[Uschold 99]

[Mizoguchi03]

LO

D

Ontology

Documents / Law Data


(1) Usage as a Common Vocabulary To enhance interoperability of knowledge content, this

type of application uses ontology as a common vocabulary.

(2)Usage for Search This type of application uses semantic information of

ontologies for semantic search.

2012/08/15 IASLOD 2012 26

Common Vocabulary

Search

Index

(3) Usage as an Index Applications of this category utilize

not only the index vocabulary defined in ontologies but also its structural information (e.g., an index term’s position in the hierarchical structure) as systematized indexes when accessing the knowledge resources.

e.g.) Indexes for Knowledge Portal, Semantic Navigation

Usage of hierarchical structure in ontology as an Index

(4) Usage as a Data Schema Applications of this category use ontologies as a data schema to

specify data structures and values for target databases. (5) Usage as a Media for Knowledge Sharing

Applications of this category aim at knowledge sharing among different systems and/or people using ontologies and instance.

e. g. knowledge alignment, knowledge mapping, communication support

2012/08/15 IASLOD 2012 27


Reference ontology

Knowledge A

Knowledge B

Mapping to the Reference Ontology

Ontology A

Ontology B

Ontology Mapping

Knowledge A

Knowledge B

(i) Knowledge Sharing through a Reference Ontology

(ii) Knowledge Sharing using Multiple Ontologies

(6) Usage for a Semantic Analysis Reasoning and semantic processing on the basis of ontological

technologies enable us to analyze contents which are annotated by metadata.

e.g. automatic classification, statistical analysis, validation

(7) Usage for Information Extraction Applications which aim at extracting meaningful information

from the search result are categorized here. e.g. Recommendation, extracting some features from web pages ,

summarization of contents

Comparison among categories (2) Search, (6) and (7): (2) Search -> just output search results without modifications. (6) Semantic Analysis -> add some analysis to the output of

(2) (7) Information Extraction -> extract meaningful information

before outputting for users. 2012/08/15 IASLOD 2012 28


(8) Usage as a Rule Set (Meta Model) for Knowledge Models We can use ontologies as meta-models which rule the knowledge

(instance) models. Relations between the ontologies and the instance models

correspond to that of the database and the database schema of category (4).

Compared to the category (4), Knowledge models need more flexible descriptions in terms of meaning of the contents.

2012/08/15 IASLOD 2012 29


Ontology

Databases / Knowledge Models

(9) Usage for Systematizing Knowledge To integrate these usages from (1) to (8),

ontologies can be used for Knowledge Systematization.

e.g. integrated knowledge systems, knowledge management systems and contents management systems

Meta Model

Types of Ontology Characteristics of ontologies

Design concept Focusing on efficient information processing Focusing on good conceptualizations to

capture the target world accurately as much as possible

Semantic feature cf. An ontology spectrum [Lassila and McGuninness 01]

Target domains Building process (How to be constructed)

By hand, by machine learning, by collaborative work

Description languages The scale of ontology

Number of concepts and instances, Scalability, Coverage 302012/08/15 IASLOD 2012

Without depending on other characteristics

Types of Ontology 5 Categories from the viewpoint of semantic

feature of ontologies. (A) Simple Schema

e.g. RSS and FOAF for uniform description of data for SW.

(B) Hierarchies of is-a Relationships among Concepts

A light-weight ontology described by Only rdfs:subClassOf.

e.g. Hierarchies of topics on Web portal, controlled Vocabulary.

(C) Relationships other than “is-a” is Included Other various relationships (properties) with some

Restriction (e.g. cardinality, all/someValuesFrom). (D) Axioms on Semantics are Included

Specifying further constraints among the concepts or instance by introducing axioms on semantic constraints (e.g. “transitive Property”, “inverseOf”, “disjointWith” , “one of” ).

(E) Strong Axioms with Rule Descriptions are Included

Further description of constraints on the category (D) with rule descriptions (e.g. KIF or SWRL).

RD

F(S

)O

WL

OW

L

＋SW

RL

312012/08/15 IASLOD 2012

LOD

Results of the Analysis

2012/08/15 IASLOD 2012 32

The result of our analysis is available at the URL:

http://www.hozo.jp/OntoApps/

(1) Common Vocabulary

(2) Search

(3) Index

(4) Data Schema

(5) Knowledge Sharing

(6) Semantic Analysis

(7) Information Extraction

(8) Knowledge Modeling

(9) Knowledge Systematization

4%

19%

11%

13%12%

9%

8%

20%

4%

利用タイプの分布1)共通語彙

2)検索

3)インデックス

4)データスキーマ

5)知識共有の媒体

6)分析

7)抽出

8)知識モデルの規約

9)知識の体系化

There is not so big difference among the ratios of each type of usage.

Distribution of Types of Usage of Ontology

2012/08/15 IASLOD 2012 33

Mainly deal with “data” processing

Explicitly deal with “knowledge” processing

Most of current studies in the SW application deal with “data” processing on structured data.

LOD

Distribution of Types of Ontology

2012/08/15 IASLOD 2012 34

1%

6%

79%

11%

3%

オントロジーの種類の分布

簡易スキーマ

概念階層

その他の関係

意味制約

公理あり

Most of the SW applications use ontologies including a variety types of relations.

OWL, OWL-S,

50%RDF(S),

23%

DAML+OIL,

4%

Others, 12%

Unknown, 12%

(E) Strong Axioms with Rule Descriptions are Included

Almost half of the systems use OWL or extended OWL.

(A) Simple Schema

(B) Hierarchies of is-a 　　　 Relationships among 　　 Concepts

(C) Other Relationships are Inculuded

(D) Axioms on Semantics 　　 are Included

A few ontologies have Rule descriptions.

(A) SimpleSchema

(B) Is-aHierarchies

(C) OtherRelationship

s(D)Axioms (E) Rule

Descriptions Total

(1) Common Vocabulary 0 4 7 0 0 11(2) Search 1 2 43 4 1 51(3) Index 0 3 23 3 0 29(4) Data Schema 0 0 32 5 0 37(5) Knowledge Sharing 1 0 31 1 0 33(6) Semantic Analysis 1 1 21 3 0 26(7) Information Extraction 1 2 15 3 0 21(8) Knowledge Modeling 0 1 36 9 8 54(9) Knowledge Systematization 0 2 8 1 0 11

Total 4 15 216 29 9 273

The Types of Ontology

A Correlation between the Types of Usage and the Types of Ontology

2012/08/15 IASLOD 2012 35

(A) SimpleSchema

(B) Is-aHierarchies

(C) OtherRelationship

s(D)Axioms (E) Rule

Descriptions Total

(1) Common Vocabulary 0 4 7 0 0 11(2) Search 1 2 43 4 1 51(3) Index 0 3 23 3 0 29(4) Data Schema 0 0 32 5 0 37(5) Knowledge Sharing 1 0 31 1 0 33(6) Semantic Analysis 1 1 21 3 0 26(7) Information Extraction 1 2 15 3 0 21(8) Knowledge Modeling 0 1 36 9 8 54(9) Knowledge Systematization 0 2 8 1 0 11

Total 4 15 216 29 9 273

The Types of Ontology

A Correlation between the Types of Usage and the Types of Ontology

2012/08/15 IASLOD 2012 36

Deeper type of usage needs deeper semantic feature of ontologies.

Rule description is used in mainly knowledge modeling.

Semantic Web

LOD

0

5

10

15

20

25

30

35

40

会議毎の利用タイプの推移






(4) Data Schema

(3) Index

(2) Search


The amount of papers surveyed in each conference9 19 18 24 25 11 23 26 17 18T

he amountsof typ

es of usage

The Conference-by-Conference Transition of the Types of Usage

2012/08/15 IASLOD 2012 37

(9) Knowledge　　Systematization(8) Knowledge 　　 Modeling(7) Information 　　 Extraction(6) Semantic 　　 Analysis(5) Knowledge 　　　 Sharing(4) Data Schema(3) Index(2) Search(1) Common Vocabulary

(2)

(4)

(6)

(5)

(7)

0

5

10

15

20

25

30

35

40

会議毎の利用タイプの推移






(4) Data Schema

(3) Index

(2) Search


The amount of papers surveyed in each conference9 19 18 24 25 11 23 26 17 18T

he amountsof typ

es of usage

The Conference-by-Conference Transition of the Types of Usage

2012/08/15 IASLOD 2012 38

About 20

there is no significant change in the use of ontology as vocabulary or for retrieval ((1)-(3))

the use for higher-level semantic processing ((4)-(9)) are increasing gradually.

(9) Knowledge　　Systematization(8) Knowledge 　　 Modeling(7) Information 　　 Extraction(6) Semantic 　　 Analysis(5) Knowledge 　　　 Sharing(4) Data Schema(3) Index(2) Search(1) Common Vocabulary

(2)

(4)

(6)

(5)

(7)

The mainstream of SW application development focuses on data processing, and overcoming the difficulty of knowledge processing might be a key to create killer applications.

The amounts of types of usage are increasing year by year.

The Combinations of the Types of Usage

2012/08/15 IASLOD 2012 39


(1) 　 Vocabulary (2) Search (3) Index


(4)Data Schema (5) Knowledge Sharing



4%

19%

11%

13%12%

9%

8%

20%

4%


2)検索




6)分析

7)抽出




(2) Search

(3) Index

(4) Data Schema





(9) Knowledge 　　 Systematization

The Combinations of the Types of Usage

2012/08/15 IASLOD 2012 40


(1) 　 Vocabulary (2) Search (3) Index


(4)Data Schema (5) Knowledge Sharing



4%

19%

11%

13%12%

9%

8%

20%

4%


2)検索




6)分析

7)抽出




(2) Search

(3) Index

(4) Data Schema






(2)(6)

(7)

(2) Search, (6)Analysis and (7)Info. Extraction are usages mainly for semantic retrieval.->(1) common vocabularies tend to be used for search systems.

The combinations of (2) search and (5) Knowledge sharing->integrated search across several information resources.

Combined with (8) Knowledge modeling more frequently in compare with (2) Search and (6) Semantic Analysis.

Combined with all other types systematically.

The distribution of the types of usage per a domain(1/2)

2012/08/15 IASLOD 2012 410 10 20 30 40 50

medical(11)bio(9)

scientific information(13)education(4)

geographical(4)e-government(4)

business(17)knowledge …

Semantic Desktop(4)Web community(6)

Wiki(4)Webpage(11)

agent(2)ontology(7)software(9)

access management(3)service(21)

multimedia(24)multipurpose(27)

ドメイン毎の利用タイプ


(2) Search

(3) Index

(4) Data Schema






Domains (number of systems) The number of the types of usage

4%

19%

11%

13%12%

9%

8%

20%

4%


2)検索




6)分析

7)抽出




(2) Search

(3) Index

(4) Data Schema






Knowledge Management(9)

Multipurpose

Multimedia

Software

Service

knowledge management

Business

Scientific information

BioMedical

Webpage

The distribution of the types of usage per a domain(2/2)

2012/08/15 IASLOD 2012 42

4%

19%

11%

13%12%

9%

8%

20%

4%


2)検索




6)分析

7)抽出




(2) Search

(3) Index

(4) Data Schema






1) 2) 3) 4) 5) 6) 7) 8) 9)

✓ ✓✓✓✓✓ ✓ ✓ ✓✓ ✓

✓ ✓✓

✓✓ ✓

✓✓✓ ✓ ✓

✓✓

✓✓ ✓

✓✓

✓ ✓✓

✓ ✓ ✓ ✓✓ ✓✓ ✓

Types of Usage of Ontology

In the software and service domains, the percentage of (8) knowledge modeling is higher in comparison with scientific domains

scientific domains

In KM and ontology domains, the percentage of (9) knowledge systematization is higher.

The numbers of the use for higher-level semantic processing ((4)-(9)) are increasing gradually.

Summary: analysis of SW applications Summary

Analysis of 190 SW applications from the viewpoint of

Types of Usage of Ontology for a SW Application Types of Ontology .

This classifications can be applied to LOD apps. The result of our analysis is available at the URL:

http://www.hozo.jp/OntoApps/ Open questions

How rich semantics are needed for LOD? It is important viewpoints of the users (domain expert).

Ontology can add richer semantics to LOD, but is it valuable to pay building cost?

We have to consider balance between cost and benefit. 2012/08/15 IASLOD 2012 43


Web Conferences from ontological viewpoints.

(2) How ontologies are used in Linked Data An analysis of Semantic Web applications. 9 types of ontology usages x 5 types of ontologies


2012/08/15 IASLOD 2012 44

Ontology Engineering to Enrich Linked Data

Features of ontology in class level It reflects understanding of the target world. Well organized ontologies have generalized rich

knowledge based on consistent semantics. Ontologies are systematized knowledge of domains.

My research interest on LOD How can I use ontologies in class level for semantic

processing? When I combine it with LOD, how does it enrich LOD?

Possible applications Flexible viewpoint management from multi-perspectives. Integrated understanding support of domain experts. Idea/Innovation supporting system.

2012/08/15 IASLOD 2012 45

Examples Understanding an Ontology through

Divergent Exploration Presented at ESWC2011

Ontology of disease “River Flow Model of Diseases”

presented at ICBO (International Conference on Biomedical Ontology) 2011

Dynamic Is-a Hierarchy Generation System based on User's Viewpoint Presented at JIST2011

2012/08/15 IASLOD 2012 46

Motivation: Understanding an Ontology through Divergent Exploration Issue: A serious gap exists between interests of

ontologists and domain experts Ontologists try to cover wide areas domain-independently Domain experts are well-focused and interest in domain

specificity.→Ontologies are sometimes regarded as verbose and too

general by domain experts

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Target World

Experts in energy

Experts in ecosystem

Experts in policy

Ontologists×

×Knowledge

sharing is difficult

Understanding the target world from the domain-

specific viewpoints

Understanding the target world from the domain-

specific viewpoints

Knowledge systematization

Ontology

Interest in common properties of concepts

and generality.

GAP

Motivation: It is highly desirable to have not only knowledge structuring from the general perspective but also from the domain-specific and multiple-perspectives.

Target World

Experts in energy

Experts in energy Experts in ecosystem

Experts in ecosystem

Experts in policy

Experts in policy

Ontology developer×

×

✓

✓Knowledge sharing

is difficult

Understanding from the domain-

specific viewpoints

Understanding from the domain-

specific viewpoints

Ontology

Integrated understanding of the ontology and cross-

domain knowledge

Capturing of the essential conceptual structure

as generally as possible

GAP

Conceptual map

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Our approach: Divergent exploration of ontology

It would stimulate their intellectual interests and could

support idea creation

It bridges the gap between ontologies and domain experts

①Systematizing the conceptual structure focusing on common characteristics

②On the fly reorganizing some conceptual structures from the

ontology as visualizations

②On the fly reorganizing some conceptual structures from the

ontology as visualizations

(Divergent) Ontology exploration tool

Exploration of an ontology

“Hozo” – Ontology Editor

Multi-perspective conceptual chains represent the explorer’s understanding of ontology from the specific viewpoint. Conceptual maps

Visualizations as conceptual maps from different view points

1) Exploration of multi-perspective conceptual chains2) Visualizations of conceptual chains

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Referring to another concept

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Node represents a

concept(=rdfs:Class)

slot represents a relationship

(=rdf:Property)

Is-a (sub-class-of) relationshp

Viewpoints for exploration■The viewpoint as the combination of a starting point and an aspect.

・ The aspect is the manner in which the user explores the ontology. It can be represented by a set of methods for tracing concepts according to its relations.

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Starting point

Aspects

Aspects for tracing concept

　Related relationships

Kinds of extractionin Hozo in OWL

(A) is-a relationship rdfs:subClassOf(1) Extraction of sub concepts (2) Extraction of super concepts

(B) part-of/attribute-of relationship

properties which are referred in owl:restriction

(3)Extraction of concepts referring to other concepts

(4) Extraction of concepts to be referred to

(C) Depending on relationship

　(5) Extraction of contexts (6) Extraction of role concepts

(D) play(playing) relationship

　(7) Extraction of player (class constraint) (8) Extraction of role concepts

rdfs:subClassOf

Other properties

+ restriction on property names and/or tracing classes

System architecture

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Ontology Exploration Tool

aspect dialogconceptual map visualizer

concept extraction module

Hozo-ontology editor

Ontology exportation

OWL ontologyimport

Ontology buildingcommands

flows of dataLegends

inputs by users

Publish conceptual maps on the Web

Connections with other web systems through concepts defined in the ontology



Browsing conceptual maps using web browser

A Java client application version and a web service version are available.

Concept tracing module

532012/08/15 IASLOD 2012

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Aspect dialog

constriction tracing classes

Option settings for exploration

property names

Conceptual map visualizer

Kinds of aspects

Selected relationships are traced and shown as links in conceptual map

55

Explore the focused (selected) path.

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Ending point (1)

Ending point (3)Ending point (2)

Search Path

Starting point

Selecting of ending pointsFinding all possible paths from stating point to ending points

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Search Path

Selected ending points

Functions for ontology exploration

Exploration using the aspect dialog: Divergent exploration from one concept using the aspect

dialog for each step Search path:

Exploration of paths from stating point and ending points.

The tool allows users to post-hoc editing for extracting only interesting portions of the map.

Change view: The tool has a function to highlight specified paths of

conceptual chains on the generated map according to given viewpoints.

Comparison of maps: The system can compare generated maps and show the

common conceptual chains both of the maps. 2012/08/15 IASLOD 2012 58

Usage and evaluation of ontology exploration tool

Step 1: Usage for knowledge structuring in sustainability science

Step 2: Verification of exploring the abilities of the ontology exploration tool

Step 3: Experiments for evaluating the ontology exploration tool

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Usage for knowledge structuring in sustainability science

Sustainability Science (SS) We aimed at establishing a new

interdisciplinary scheme that serves as a basis for constructing a vision that will lead global society to a sustainable one.

It is required an integrated understanding of the entire field instead of domain-wise knowledge structuring.

Sustainability science ontology Developed in collaboration with domain

expert in Osaka University Research Institute for Sustainability Science (RISS).

Number of concepts ： 649, Number of slots ： 1,075

Usage of the ontology exploration tool It was confirmed that the exploration was fun

for them and the tool had a certain utility for achieving knowledge structuring in sustainability science. [Kumazawa 2009]

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http://en.ir3s.u-tokyo.ac.jp/about_susSustainability Science

RISS, Osaka Univ.

If we ask domain experts to explore the SS ontology using the tool and verify whether it can generate maps they wish to do, it means that we verify not only exploring capability of the ontology exploration tool but also the ontology itself.

Verification of exploring capability of ontology exploration tool

Verification method1) Enrichment of SS ontologyWe enriched the SS ontology on the basis of 29 typical scenarios which a domain expert organized problem structures in biofuel domains by reviewing existing research.

2) Verification of scenario reproducing operationsWe verified whether the ontology exploration tool could generate conceptual maps which represent original scenarios.

Result 93% (27/29) of original scenarios were successfully

reproduced as conceptual maps. The rest (2 scenarios) could not be reproduced because we

missed to add some relationships in the ontology.

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We can conclude that the exploration ability of the tool is sufficient.

burn agriculture= （ deforestation, soil deterioration caused by farmland development for biofuel crops ）⇒ harvest sugarcanes （ air pollution caused by intentional burn ）， disruption of ecosystem caused by deforestation （ water pollution ）　

burn agriculture= （ deforestation, soil deterioration caused by farmland development for biofuel crops ）⇒ harvest sugarcanes （ air pollution caused by intentional burn ）， disruption of ecosystem caused by deforestation （ water pollution ）　

The concepts appearing in these scenarios were extracted and generalized to add into the ontology

Example: Air pollution, cause of forest fire, soil deterioration, water pollution are attributed to intentional burn when forest is logged or sugarcanes are harvested in the farmland development for biofuel crops.

Usage and evaluation of ontology exploration tool Step 1: Usage for knowledge structuring in

sustainability science

Step 2: Verification of exploring the abilities of the ontology exploration tool

Step 3: Experiments for evaluating the ontology exploration tool

1) Whether meaningful maps for domain experts were obtained.

2) Whether meaningful maps other than anticipated maps were obtained.

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Maps which are representing the contents of the scenarios anticipated by ontology developers at the time of ontology construction.

Note: the subjects don’t know what scenarios are anticipated.

Experiment for evaluating ontology exploration tool

Experimental method1) The four experts to generated

conceptual maps with the tool in accordance with condition settings of given tasks.

2) They remove paths that were apparently inappropriate from the paths of conceptual chains included in the generated maps.

3) They select paths according to their interests and enter a four-level general evaluation with free comments.

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The subjects:4 experts in different fields. A: Agricultural economics B: Social science (stakeholder analysis) C: Risk analysis D: Metropolitan environmental planning

A: Interesting B: Important but ordinaryC: Neither good or poorD: Obviously wrong

Experimental results (1)

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A B C DExpert A 2 2Expert A(second time) 1 1

Expert B 7 4 1 2Expert B(second time) 6 3 3

Expert C 8 1 5 2Expert D 3 1 1 1Expert A 1 1Expert B 6 5 1Expert C 7 2 4 1Expert D 5 3 1 1Expert B 8 4 2 2Expert C 4 2 2Expert D 3 3

61 30 22 8 1

Task 3

Total

Number ofselected paths

Path distribution based on general evaluation

Task 1

Task 2

(N) Nodes and links included in

the paths of anticipated maps

(M) Nodes and links included in the paths of generated and selected by the experts

50 15050

N∩M

Each area of circle represents the numbers of nodes and links included in paths. Note, the number in the circles represent not the actual number but the rates between each paths.

Fig.7 The rate of paths.

Experimental results (1)

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A B C DExpert A 2 2Expert A(second time) 1 1

Expert B 7 4 1 2Expert B(second time) 6 3 3

Expert C 8 1 5 2Expert D 3 1 1 1Expert A 1 1Expert B 6 5 1Expert C 7 2 4 1Expert D 5 3 1 1Expert B 8 4 2 2Expert C 4 2 2Expert D 3 3

61 30 22 8 1

Task 3

Total

Number ofselected paths

Path distribution based on general evaluation

Task 1

Task 2

(N) Nodes and links included in

the paths of anticipated maps


50 15050

N∩M

Each area of circle represents the numbers of nodes and links included in paths. Note, the number in the circles represent not the actual number but the rates between each paths.

Fig.7 The rate of paths.

Number of maps generated: 13

Number of paths evaluated: 61

Number of paths evaluated: 61A: Interesting 30 (49%)B: Important but ordinary 22 (36%)C: Neither good or poor 8(13%) D: Obviously wrong 1(2%)

We can conclude that the tool could generate maps or paths sufficiently meaningful for experts.

85%

Experimental results (2) Quantitatively comparison of the anticipated maps

with the maps generated by the subjects

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(N) Nodes and links included in the

paths of anticipated maps


50 15050

N∩M About 75% of paths in the generated maps are new paths which is not anticipated from the typical scenarios .

It is meaningful enough to claim a positive support for the developed tool. This suggests that the tool has a sufficient possibility of presenting unexpected contents and stimulating conception by the user.

About half (50%) of the paths included in the anticipated maps 　 were included in the maps generated by the experts.

Exploration of ontology vs. exploration of linked data

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50 15050

Paths generated by the experts

New paths which is unexpected from at the time of ontology construction.

Paths expected by ontology developers

Liked data is based on a more rich ontologies　　　→ more meaningful paths through divergent.

Paths expected by developer

Unexpected paths

(Main) Target of exploration

Exploration of Liked Data

✓ Instance level

Exploration ofOntology

✓ ✓ Class level

Summary: Understanding an Ontology through Divergent Exploration

Divergent exploration of an ontology It supports to bridge a gap between interests of ontologists and

domain experts and contributes to integrated understanding of an ontology and its target world from multiple viewpoints.

Usage and evaluation of the tool Usage for knowledge structuring in sustainability science Verification of exploring the abilities of the ontology exploration tool Experiments for evaluating the ontology exploration tool

Domain experts could obtain meaningful knowledge for themselves as conceptual chains through the divergent exploration of the SS ontology.

Future plans Improvements of the tool to support more advanced problems such as

consensus-building, policy-making and so on. Application of the ontology exploration tool for ontology refinement. An evaluation of the tool on other ontologies (especially in OWL) . Divergent exploration of instances (like liked data) with an ontology.

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A consensus-building support system

Touch-Table

Screen

Map 1

Map2

Map4

Map 3

2nd Step: Collaborative workshop

1 ｓｔ Step: Individual concept map creation

・ Display multiple concept maps・ Highlight common concepts・ Highlight different concepts

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The first experimental workshop using the consensus-building support system

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Discussion using integrated maps displayed on a touch-table display

Participants- 5 experts in sustainability science- 4 students in environmental engineering

Medical ontology project in Japan

Developed ontologies Disease ontology ：

Definitions of diseases as causal chains of abnormal state.

6000+ diseases Anatomy ontology ：

Connections between blood vessel, nerves, bones : 10,000+

It based on ontological frameworks (upper level ontology) which can apply to other domains

Models for causal chains Abnormal state ontology for data

integration General framework to define

complicated structures

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An example of causal chain constituted diabetes.

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Disorder (nodes)

Causal Relationship

Core causal chain of a disease(each color represents a disease)

Legends

loss of sight

Elevated level of glucose in the blood

Type I diabetesDiabetes-related Blindness

Steroid diabetes

Diabetes…

…

……

…

…

…

… … …

…

possible causes and effects

Destruction of pancreatic beta cells

Lack of insulin I in the blood

Long-term steroid treatment

Deficiency of insulin

An example of causal chain constituted diabetes.

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Disorder (nodes)

Causal Relationship

Core causal chain of a disease(each color represents a disease)

Legends

loss of sight

Elevated level of glucose in the blood

Type I diabetesDiabetes-related Blindness

Steroid diabetes

Diabetes…

…

……

…

…

…

… … …

…

possible causes and effects

Destruction of pancreatic beta cells

Lack of insulin I in the blood

Long-term steroid treatment

Deficiency of insulin

Based on abnormal state ontology causal chains defined in each areas are generalized and organized across domains.

MD in 12 areas describe definitions (causal chains) of disease

Visualizing/reasoning causal chains in human body

2012/08/15 IASLOD 2012 74

• As the result, we obtained causal chains which include about 17,000 clinical disorders defined in 6,000 diseases. They represent possible causal chains in human body.

• We also developed a browsing tool to visualizes causal chains.

• We also consider publishing the disease ontology as LOD.

Motivation: Dynamic Is-a Hierarchy Generation System based on User's Viewpoint Domain experts often want to

understand the target world from their own domain-specific viewpoint.

In some domains, there are many ways to categorize the same kinds of concepts.

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infarctiondisease

stenosisdisease

Angina diabetesMyocardialinfarction Stroke

disease

hyperglucemiadisease

classification by the symptom

How diseases are named 　　 named by the major symptom

　 diabetes, angina…　 named by the abnormal object

　 heart disease, …　 named by the cause of the

disease 　　 Myocardial infarction, stroke

　 named by the specific environment 　 Altitude sickness, … 　

　 named by the discoverer　 Grave’s disease…

disease

heartdisease

braindisease


blooddisease

classification by the abnormal object

StrokeMyocardialinfarction

diabetes Angina

disease

Several is-a hierarchies of diseases according to their viewpoints

Understanding from their own

viewpoints

Disease

One is-a hierarchy of diseases cannot cope with such a diversity of viewpoints.

Existing approaches Acceptance of multiple ontologies

based on the different perspectives

Multiple-inheritance, Ontology mapping

Problem If we define every possible is-a

hierarchy using multiple-inheritances or ontology mapping, they would be very verbose and the user’s viewpoints would become implicit.

Exclusion of the multi-perspective nature of domains from ontologies

The OBO Foundry A guideline for ontology development

stating that we should build only one ontology in each domain.

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heartdisease

Myocardialinfarction

infarctiondisease

Multiple-inheritance

infarctiondisease

stenosisdisease


disease

hyperglycemiadisease

disease

heartdisease

braindisease


blooddisease

Ontology mapping

Our approach

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Ontology Viewpoints

Generation of is-a hierarchies

Dynamic Is-a Hierarchy Generation based on User's Viewpoint


viewpoints

Disease

We take a user-centric approach based on ontological viewpoint management.

Multi-perspective issue

Use single-inheritance

Our approach: Dynamic is-a Hierarchy Generation according to User’s Viewpoint

2012/08/15 IASLOD 2012 78

abnormal state

infarction stenosis hyperglycemia

parts of human body

heart brain blood

perspective A「 focus on symptoms 」

perspective B「 focus on abnormal objects 」

various is-a hierarchiesbased on individual perspectives

(2) Reorganizing some conceptual structures from the ontology on the fly as visualizations to cope with various viewpoints.

infarctiondisease

stenosisdisease


disease

hyperglycemiadisease

classification by the symptom

disease

heartdisease

braindisease

Stroke diabetesMyocardialinfarction Angina

blooddisease

classification by the abnormal object

StrokeMyocardialinfarction

diabetes Angina

disease

(1) Fixing the conceptual structure of an ontology using single-inheritance

based on ontological theories

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Ontology Viewpoints

Generation of is-a hierarchies

Dynamic Is-a Hierarchy Generation based on User's Viewpoint


viewpoints

Disease

We take a user-centric approach based on ontological viewpoint management.

Multi-perspective issue

Use single-inheritance

Our approach: Dynamic is-a Hierarchy Generation according to User’s Viewpoint

We propose a framework for dynamic is-a hierarchy generation according to the interests of the user and implement the framework as an extended function of “Hozo-our ontology development tool”.

Summery (1) Trends of Linked Data in Semantic Web

Conferences from ontological viewpoints. SW → 　 Web2.0 　→　 LOD

(2) How ontologies are used in Linked Data 9 types of ontology usages x 5 types of

ontologies An Important question:

How rich semantics are needed for LOD from user’s viewpoint?

(3) Ontology Engineering to Enrich Linked Data An approach:

Combine semantic processing in ontology (class level) and LOD.2012/08/15 IASLOD 2012 80

Acknowledgement

2012/08/15

Thank you for your attention!

My slide is available at http://goo.gl/AYy42

Some Demos are available at

http://www.hozo.jp/Demo/Contact: [email protected]

81IASLOD 2012

Ontological topics Some examples of topics which I work

on Definition of disease

What’s “disease” ? What’s “causal chain” ? Is it a object or process ?

Role theory What’s ontological difference among the following

concepts? Person Teacher Walker Murderer Mother

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…. Natural type

Role (dependent concept)

ontology engineering to enrich linked data

Technology

semantic web conferences23is

ontology buildingusing

basic idea of semantic

korea ontology engineering

osaka university

nano technology ontology

clinical medical ontology

bio mimetic ontology