1. IASLOD 2012 -International Asian Summer
School on Linked Data
13-17 Aug. 2012, KAIST, Daejeon, Korea
Ontology Engineering to
Enrich Linked Data
Kouji Kozaki
The Institute of Scientific and Industrial Research (I.S.I.R),
Osaka University, Japan
2012/08/15 IASLOD 2012 1
2. 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) Cooperator:
Enegate Co, ltd.
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
3. 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 Areas
2012/08/15 IASLOD 2012 3
4. 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
5. 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
6. Venues of International
Semantic Web Conferences
ISWC ESWC ASWC
SWWS@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, USA ESWC2006@Budva,Montenegro ASWC2006@Beijing,China
ISWC2007&ASWC2007@Busan,Korea ESWC2007@Innsbruck, Austria
ISWC2008@Karlsruhe, Germany ESWC2008@Tenerife, Spain ASWC2008@Bangkok, Thailand
ISWC2009@Washington D.C.Area,USA ESWC2009@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 6
7. JIST 2012, 2-4 Dec. 2012, Nara, Japan
- Submission due : 24 Aug. 2012.
- It has a Special Track on Linked Data
http://www.ei.sanken.osaka-u.ac.jp/jist2012/
2012/08/15 IASLOD 2012 7
8. Research Trends in
Semantic Web Conferences(1/3)
ISWC ESWC ASWC
SWWS@California, USA
ISWC2002@Sardinia, Italy
ISWC2003@Sanibel Island,FL,USA Symposium@Osamka, WS@Nara
ISWC2004@Hiroshima, Japan ESWS@Heraklion, Greece
Basic technologies of Semantic WebGreece mainly discussed.
ISWC2005@Galway, Ireland ESWC2005@Heraklion,
are
DAML, OIL→ predecessor of OWL, Rule-ML, Ontology…
ISWC2006@Athens, GA, USA ESWC2006@Budva,Montenegro ASWC2006@Beijing,China
ISWC2007&ASWC2007@Busan,Korea ESWC2007@Innsbruck, Austria
ISWC2008@Karlsruhe, Germany ESWC2008@Tenerife, Spain ASWC2008@Bangkok, Thailand
Frequency QuestionESWC2009@Heraklion, Greece
ISWC2009@Washington D.C.Area,USA
/ Discussion: ASWC2009@Shanghai, China
“I can understand the basic idea of Semantic Web.
ISWC2010@Shanghai, China ESWC2010@Heraklion, Greece
However, who describes meta data?”
ISWC2011@Bonn.Germany ESWC2011@Heraklion, Greece JIST2011@Hangzhou, China
ISWC2012@Boston, USA ESWC2012@Heraklion, Greece
ISWC2013@Sydney, Australia
2012/08/15 IASLOD 2012 8
9. Research Trends in
Semantic Web Conferences(2/3)
ISWC ESWC ASWC
SWWS@California, USA
ISWC2002@Sardinia, Italy
ISWC2003@Sanibel Island,FL,USA Symposium@Osamka, WS@Nara
ISWC2004@Hiroshima, Japan ESWS@Heraklion, Greece
ISWC2005@Galway, Ireland ESWC2005@Heraklion, Greece
ISWC2006@Athens, GA, USA ESWC2006@Budva,Montenegro ASWC2006@Beijing,China
ISWC2007&ASWC2007@Busan,Korea ESWC2007@Innsbruck, Austria
As an answer to the question “Who describes ASWC2008@Bangkok, Thailand
ISWC2008@Karlsruhe, Germany ESWC2008@Tenerife, Spain meta data?”
Usage of Social Network System, Web2.0 were actively China
ISWC2009@Washington D.C.Area,USA ESWC2009@Heraklion, Greece ASWC2009@Shanghai,
FOAF, WiKi …
ISWC2010@Shanghai,Blog, RSS, ESWC2010@Heraklion, Greece
discussed. China
ISWC2011@Bonn.Germany ESWC2011@Heraklion, Greece JIST2011@Hangzhou, China
・Collaborative Development of Ontologies was one of
ISWC2012@Boston, USA ESWC2012@Heraklion, Greece
hot topics.Australia
ISWC2013@Sydney,
・Many Semantic Web based applications were developed.
2012/08/15 IASLOD 2012 9
10. Research Trends in
Semantic Web Conferences(3/3)
ISWC ESWC ASWC
SWWS@California, USA
ISWC2002@Sardinia, Italy
ISWC2003@Sanibel Island,FL,USA Symposium@Osamka, WS@Nara
★The first presentation ofESWS@Heraklion, Greece
ISWC2004@Hiroshima, Japan DBPedia.
(DBPedia was presented also at ESWC2005@Heraklion, Greece
WWW2007.)
ISWC2005@Galway, Ireland
A Special Session
ISWC2006@Athens, GA, USA on Linked Data
ESWC2006@Budva,Montenegro ASWC2006@Beijing,China
ISWC2007&ASWC2007@Busan,Korea ESWC2007@Innsbruck, Austria
ISWC2008@Karlsruhe, Germany ESWC2008@Tenerife, Spain ASWC2008@Bangkok, Thailand
8 3
ISWC2009@Washington D.C.Area,USA ESWC2009@Heraklion, Greece ASWC2009@Shanghai, China
10 4 Debate
ISWC2010@Shanghai, China ESWC2010@Heraklion, Greece
- Linked Data: Now what?
ISWC2011@Bonn.Germany ESWC2011@Heraklion, Greece JIST2011@Hangzhou, China
After DBPedia, Linked Data became the hottest
ISWC2012@Boston, USA ESWC2012@Heraklion, Greece
research topic in Semantic Web Conference.
ISWC2013@Sydney, Australia
:the numbers of research track papers whose title includes “Linked
Data”.
2012/08/15 IASLOD 2012 10
11. 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
(Ideal) Semantic Web
Rich semantics
× Linked Data
SNS・Web2.0
Simple/ easy to use Tag(RSS,FOAF)
Scalability
2012/08/15 IASLOD 2012 11
12. 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 Web
2012/08/15 IASLOD 2012 12
13. From ISWC2011: Keynote
by Frank van Harmelen
Terminological knowledge
is much smaller than the
factual knowledge
2012/08/15 IASLOD 2012 13
15. 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.
How to deal
(Ideal) Semantic Web
Rich semantics
with semantics.
It is an important
problem to
× bridge the GAP.
Linked Data How to
use LOD.
SNS・Web2.0
Simple/ easy to use Tag(RSS,FOAF)
2012/08/15 IASLOD 2012 Scalability 15
16. From ISWC2011:Opening
Not change increase decrease
increase
2012/08/15 IASLOD 2012 16
17. ISWC2011:Research
Papers
Research Tracks (three papers in each sessions)
Web of Data
Social Web
User Interaction
RDF Query - Alternative Approaches How to use
RDF Query - Performance Issues
Linked Data
RDF Query - Multiple Sources
RDF Data Analysis
Policies and Trust
MANCHustifications and Provenance
KR – Reasoners
KR - Semantics
Formal Ontology & Patterns How to deal with
Ontology Evaluation Semantics
Ontology Matching, Mapping
2012/08/15 IASLOD 2012 17
18. ISWC2011:Wrokshops
Consuming Linked Data※
Detection, Representation, and Exploitation of Events
Knowledge Evolution and Ontology Dynamics
Linked Science※
Multilingual Semantic Web ※Workshops whose main topic
Ontologies come of Age is Liked Data
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
19. ISWC2011:Wrokshops
Consuming Linked Data※
Detection, Representation, and Exploitation of Events
Knowledge Evolution and Ontology Dynamics
nd workshop on
Linked 2
The Science※
Multilingual Semantic Web Data ※Workshops whose main topic
Consuming Linked
Ontologies come(participants: 70-80)
・big workshop of Age is Liked Data
Ontology Matching about 50%
・acceptance rate:
・Papers about basic technologies are more than applications.
Ordering and Reasoning
★Some organizers (participants) argue Systems
Scalable Semantic Web Knowledge Basethat
“I want to got more Informaton application of
Semantic Personalizedpaper about Management LOD.”
“We have to know (practical/concrete) Needs for LOD”
Semantic Sensor Networks
Semantic Web Enabled Software Engineering
Linked Data-a-thon
Social Data on the Web
Terracontest whose theme is to develop LOD application within 2 weeks.
・A Cognita - Foundations, Technologies and Applications of the
・Given Resources for the subject is conference information of ISWC.
Geospatial Web
Uncertainty Reasoning (All the Semantic Web
・Only 3 submissions. for of them got prize…)
Web Scale Knowledge Extraction
2012/08/15 IASLOD 2012 19
20. Agenda
(1) Trends of Linked Data in Semantic 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
(3) Ontology Engineering to Enrich Linked Data
2012/08/15 IASLOD 2012 20
21. 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
22. 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
23. applications which is
analyzed
Number
Conferences Dates Venues
of Apps
International Semantic Web Conference (ISWC)
ISWC2002 Jun. 9-12, 2002 Sardinia, Italy 9
ISWC2003 Oct.20-23, 2003 Sanibel Island,FL,USA 19
ISWC2004 Nov. 7-11, 2004 Hiroshima, Japan 18
ISWC2005 Nov. 6-10, 2005 Galway, Ireland 25
ISWC2006 Nov.5-9, 2006 Athens, GA, USA 26
ISWC2007&ASWC2007 Nov.11- 15, 2007 Busan, Korea 18
European Semantic Web Conference (ESWC)
ESWC2005 May29-Jun.1,2005 Heraklion, Greece 24
ESWC2006 Jun.11-14, 2006 Budva, Montenegro 11
ESWC2007 Jun. 03 - 07, 2007 Innsbruck, Austria 17
Asian 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.
2012/08/15 IASLOD 2012 23
24. 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
25. Types of Usage of Ontology for
a SW Application(1/5)
Types of Usage of Ontology
Ontology applications scenarios
[Uschold 99]
Shallow (1) Common Vocabulary 1)neutral authoring
(2) Semantic Search 2)common access to information
(3) Systematized Index 3)indexing for search
LOD
(4) Data Schema
The role of an ontology
1)a common vocabulary[Mizoguchi03]
(5) Media for Knowledge
2)data structure
Sharing
3)explication of what is left implicit
(6) Semantic Analysis
4)semantic interoperability
(7) Information Extraction
5)explication of design rationale
(8) Rule Set for Knowledge
6)systematization of knowledge
Models 7)meta-model function
Deep (9) Systematizing Knowledge 8)theory of content
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.
2012/08/15 IASLOD 2012 25
26. Types of Usage of Ontology for
a SW Application(2/5)
(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 Index
ontologies for semantic search.
O O ntol
ntol ogy
ogy Us
(3) Usage as an Index Search
hie
str
Applications of this category utilize in
not only the index vocabulary defined Ind
Annotation
in ontologies but also its structural
of knowle
information (e.g., an index term’s Common Vocabulary concepts
position in the hierarchical structure) ontology
Usage
as systematized indexes when vocab
accessing the knowledge resources. searc
e.g.) Indexes for Knowledge Portal, D ocum ents / Law D ata analy
Semantic Navigation D ocum ents / Law D ata
2012/08/15 IASLOD 2012 26
27. Types of Usage of Ontology for
a SW Application(3/5)
(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 Reference ontology Ontology A Ontology B
Mapping to the
Reference Ontology Ontology Mapping
Knowledge Knowledge Knowledge Knowledge
A B A B
(i) Knowledge Sharing through (ii) Knowledge Sharing using
a Reference Ontology Multiple Ontologies
2012/08/15 IASLOD 2012 27
28. Types of Usage of Ontology for
a SW Application(4/5)
(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
29. Types of Usage of Ontology for
a SW Application(5/5)
(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.
O ntol
ogy
(9) Usage for Systematizing Knowledge
To integrate these usages from (1) to (8),
Meta
Model
ontologies can be used for Knowledge
Systematization.
e.g. integrated knowledge systems,
knowledge management systems
and contents management systems
D atabases / K now l
edge M odel
s
2012/08/15 IASLOD 2012 29
30. 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 Without depending on other characteristics
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
2012/08/15 IASLOD 2012 30
31. Types of Ontology
5 Categories from the viewpoint of semantic
feature of ontologies.
LOD
(A) Simple Schema
e.g. RSS and FOAF for uniform description of data for SW.
RDF(S)
OWL
OWL SWRL
(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).
2012/08/15 IASLOD 2012 31
32. Results of the Analysis
The result of our analysis is available at the URL:
http://www.hozo.jp/OntoApps/
2012/08/15 IASLOD 2012 32
33. Distribution of Types of Usage of
Ontology
イプの分布 Mainly deal with
There is not so big difference among
利用タof usage.
the ratios of each type “data” processing
1)共通語彙 Vocabulary
(1) Common
4% 4%
(2) Search
2)検索
20% 19% 3)イIndex ス
(3) ンデッ ク
LOD
4)データ Schema
(4) Data
スキーマ
(5) Knowledge Sharing
5)知識共有の媒体
8% 11% (6) Semantic Analysis
6)分析
(7) Information Extraction
7)抽出
9%
13% (8) Knowledge Modeling
8)知識モデルの規約
12%
9)知識の体系化 Systematization
(9) Knowledge
Most of current studies in the SW Explicitly deal with
application deal with “data” “knowledge” processing
processing on structured data.
2012/08/15 IASLOD 2012 33
34. Distribution of Types of
Ontology
A few ontologies have
Rule descriptions.
オント (A) Simple Schema
ロジーの種類の分布
(E) Strong Axioms with Rule
Descriptions are Included 3% 1% (B) Hierarchies of is-a
(D) Axioms on Semantics Relationships
6%
are Included 11% among 簡易スキーマ half of the
Almost
Concepts systems use OWL
概念階層 extended OWL.
or
(C) Other Relationships その他の関係
Unknown,
are Inculuded 意味制約12%
79% Others,
DAML 公理あり
12% OWL,
+OIL,
4%
OWL-S,
Most of the SW applications use
ontologies including a variety
50%
RDF(S),
types of relations. 23%
2012/08/15 IASLOD 2012 34
35. A Correlation between the Types of
Usage and the Types of Ontology
The Types of O ntol
ogy
m e (B ) Is-a (C ) O ther
(A ) Si pl (E) Rul
e
(D )A xi s
om Total
erarchi Rel onshi
Schem a H i es ati p D escri ons
pti
s
(1) C om m on V ocabulary 0 4 7 0 0 11
(2) Search 1 2 43 4 1 51
(3) Index 0 3 23 3 0 29
(4) D ata Schem a 0 0 32 5 0 37
(5) Know ledge Shari ng 1 0 31 1 0 33
(6) Sem anti A nal s
c ysi 1 1 21 3 0 26
(7) Inform ati Extracti
on on 1 2 15 3 0 21
(8) Know ledge M odelng
i 0 1 36 9 8 54
(9) Know ledge System ati on
zati 0 2 8 1 0 11
Total 4 15 216 29 9 273
2012/08/15 IASLOD 2012 35
36. A Correlation between the Types of
Usage and the Types of Ontology
The Types of O ntol
ogy
m e (B ) Is-a (C ) O ther
(A ) Si pl (E) Rul
e
(D )A xi s
om Total
erarchi Rel onshi
Schem a H i es ati p D escri ons
pti
s
(1) C om m on V ocabulary 0 4 7 0 0 11
(2) Search 1 2 43 4 1 51
(3) Index
(4) D ata Schem a
0
0
3
0
LOD23
32
3
5
0 29
0 37
(5) Know ledge Shari ng 1 0 31 1 0 33
(6) Sem anti A nal s
c ysi 1 1 21 3 0 26
(7) Inform ati Extracti
on on 1 2 Semantic3Web 0 21
15
(8) Know ledge M odelng
i 0 1 36 9 8 54
(9) Know ledge System ati on
zati 0 2 8 1 0 11
Total 4 15 216 29 9 273
Deeper type of usage needs deeper used in mainly
Rule description is
semantic feature of ontologies. modeling.
knowledge
2012/08/15 IASLOD 2012 36
37. Conference Transition of the
Types of Usage
会議毎の利用タイプの推移
The amount of papers surveyed in each conference
40
9 19 18 24 25 11 23 26 17 18 (9) Knowledge
The amounts of types of usage
(9) Knowledge Sys
35 Systematization
(7)
(8) Knowledge Mo
(8) Knowledge
30 Modeling
(6) (7) Information Ex
25 (7) Information
Extraction Analy
(6) Semantic
20 (5) (6) Semantic
(5) Knowledge Sha
Analysis
15 (4) (5) Knowledge
(4) Data Schema
Sharing
(3) Index
10
(4) Data Schema
5 (2) (3) Index
(2) Search
(2) Search
(1) Common Vocab
0 (1) Common
Vocabulary
2012/08/15 IASLOD 2012 37
38. Conference Transition of the
Types of Usage application development focuses on
The mainstream of SW
data processing, and overcoming the difficulty of knowledge
会議毎の利用タ イプの推移
processing might paperskey to create conference About 20
The amount of be a surveyed in each killer applications.
40
9 19 18 24 25 11 23 26 17 18 (9) Knowledge
The amounts of types of usage
The amounts higher-level semantic
the use for of types of usage are (9) Knowledge Sys
35 processing ((4)-(9)) are increasing Systematization
increasing year by year. (7)
(8) Knowledge Mo
(8) Knowledge
30 gradually. Modeling
(6) (7) Information Ex
25 (7) Information
Extraction Analy
(6) Semantic
20 (5) (6) Semantic
(5) Knowledge Sha
Analysis
15 (4) (5) Knowledge
(4) Data Schema
Sharing
(3) Index
10
(4) Data Schema
5 (2) (3) Index
(2) Search
(2) Search
(1) Common Vocab
0 (1) Common
there is no significant change in the use of ontology Vocabulary
as vocabulary or for retrieval ((1)-(3))
2012/08/15 IASLOD 2012 38
39. The Combinations of the
Types of Usage
(1) Vocabulary (2) Search
利用タ イプの分布
(3) Index
1)共通語彙 Vocabulary
(1) Common
4% 4%
(2) Search
2)検索
20% 19% (3) Index ク
3)イ ンデッ ス
(4)Data Schema (5) Knowledge (6) Semantic (4) Data Schema
4)データ スキーマ
Sharing Analysis
(5) Knowledge Sharing
5)知識共有の媒体
8% 11% (6) Semantic Analysis
6)分析
(7) Information Extraction
7)抽出
9%
13% (8) Knowledge Modeling
8)知識モデルの規約
(7) Information
Extraction 12% (9) Systematization
(8) Knowledge
Modeling
Knowledge
(9) Knowledge
9)知識の体系化
Systematization
2012/08/15 IASLOD 2012 39
40. The Combinations of the
Types of Usage
(1) Vocabulary
(7)
(2) Search
利用タ イプの分布
(3) Index
(2) 1)共通語彙 Vocabulary
(1) Common
(6) 4% 4%
(2) Search
2)検索
20% 19% (3) Index ク
3)イ ンデッ ス
(4)Data Schema (5) Knowledge (6) Semantic (4) Data Schema
4)データ スキーマ
Sharing
(2) Search, (6)Analysis and of (2) search and
The combinations Analysis
(5) Knowledge sharing
(7)Info. Extraction are (5) Knowledge Sharing
5)知識共有の媒体
->integrated search across several
usages mainly for semantic
8% 11% (6) Semantic Analysis
6)分析
retrieval. information resources.
->(1) common vocabularies (7) Information Extraction
7)抽出
tend to be used for search 9%
systems. 13% (8) Knowledge Modeling
8)知識モデルの規約
(7) Information (8) Knowledge (9) Knowledge
Extraction Modeling12% Systematization (9) Knowledge
Combined with all other 9)知識の体系化
Combined with (8) Knowledge
types systematically. Systematization
modeling more frequently
in compare with (2) Search and
(6) Semantic Analysis.
2012/08/15 IASLOD 2012 40
41. The distribution of the types
of usage per a domain(1/2) イプ
ド イン毎の利用タ
メ
Domains
(number of systems) The number of the types of usage Multipurpose
multipurpose(27) (1) Common Vo
multimedia(24) Multimedia
service(21)
access management(3)
利用タイプの分布 Service
(2) Search
(3) Index
software(9) Software 1)共通語彙 Vocabulary
(1) Common (4) Data Schema
ontology(7) 4% 4%
(2) Search
2)検索 (5) Knowledge S
agent(2)
Webpage(11) Webpage
19% (3) Index ク (6) Semantic An
3)イ ンデッ ス
20%
Wiki(4) (7) Information
Web community(6) (4) Data Schema
4)データ スキーマ
knowledge (8) Knowledge M
Semantic Desktop(4)
management (5) Knowledge Sharing
5)知識共有の媒体
Knowledge Management(9) …
knowledge (9) Knowledge S
business(17)
8% 11% (6) Semantic Analysis
6)分析
e-government(4) Business (7) Information Extraction
7)抽出
geographical(4)
9% Scientific information
education(4) 13% (8) Knowledge Modeling
8)知識モデルの規約
scientific information(13) 12%
bio(9) Bio (9) Knowledge
9)知識の体系化
medical(11) Medical Systematization
2012/08/15 0 10 IASLOD 2012
20 30 40 41
50
42. Types of U sage of O ntol
ogy
The distribution and servicetypes the percentage
In the software of the domains,
1) 2) 3) 4) 5) 6) 7) 8) 9)
of KM and ✓domain(2/2) percentage of (9)
In
per aontology domains, the
of usage (8) knowledge modeling isishigher in comparison
✓
knowledge systematization higher.
✓
with scientific domains
✓
✓
利用タイプの分布
✓ ✓ ✓ ✓ 1)共通語彙 Vocabulary
(1) Common
✓ ✓ 4% 4% The numbers of the Search
✓ ✓ 2)検索 for
(2) use
higher-level semantic
✓ (3) Index ス
20%
✓ processing ((4)-(9)) are ク
19% 3)イ ンデッ
✓ increasing gradually.Data Schema
✓ (4)
4)データ スキーマ
✓
✓
(5) Knowledge Sharing
5)知識共有の媒体
8% ✓ ✓ ✓ 11% (6) Semantic Analysis
6)分析
✓
✓ (7) Information Extraction
7)抽出
9%
✓
13% (8) Knowledge Modeling
8)知識モデルの規約
✓ ✓
12%
✓ (9) Knowledge
9)知識の体系化
✓ Systematization
✓ ✓
✓
✓ ✓ ✓ ✓
✓ ✓ scientific domains
2012/08/15 ✓ ✓
IASLOD 2012 42
43. 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
44. 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 44
45. 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
46. 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
47. 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
Understanding the target
Interest in common
world from the domain- GAP properties of concepts
specific viewpoints
and generality.
Experts in policy Target World
×
Ontologists
Motivation:ecosystem
Experts in It is highly desirable to have
Ontology
Knowledge
Knowledge knowledge structuring from the general perspective
not only
sharing × the domain-specific and multiple-perspectives.
systematization
isbut also from
difficult
Experts in energy
2012/08/15 IASLOD 2012 47
48. Divergent exploration of
ontology
It bridges the gap between
ontologies and domain experts
Understanding
Capturing of the essential
from the domain- GAP conceptual structure
specific viewpoints
②On the fly reorganizing
as generally as possiblesome
conceptual structures from the
Experts in policy Target World ontology as visualizations
×
Ontology developer Conceptual
Experts in ecosystem map
Ontology
①Systematizing the
× Experts in policy
conceptual in energy
Experts
structure focusing
on common characteristics ✓
Knowledge sharing
is difficult
Experts in energy Experts in ecosystem
✓
It would stimulate their Integrated understanding of
intellectual interests and could the ontology and cross-
support idea creation domain knowledge
2012/08/15 IASLOD 2012 48
49. (Divergent)
Ontology exploration tool
1) Exploration of multi-perspective conceptual chains
2) Visualizations of conceptual chains
Visualizations as
Exploration of an ontology conceptual maps from
different view points
“Hozo” – Ontology Editor
Multi-perspective conceptual chains
represent the explorer’s understanding of
ontology from the specific viewpoint. Conceptual maps
2012/08/15 IASLOD 2012 49
50. Node represents Is-a (sub-class-of)
a concept relationshp Referring to
(=rdfs:Class) another concept
slot represents
a relationship
(=rdf:Property)
2012/08/15 IASLOD 2012 50
51. 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.
Aspects for tracing concept
Starting point
rdfs:subClassOf
Related relationships
Kinds of extraction
in Hozo in OWL
(1) Extraction of sub concepts
Aspects (A) is-a relationship rdfs:subClassOf
(2) Extraction of super concepts
Extraction of concepts referring to other
properties which (3)
(B)
part-of/attribute-of
are referred in
concepts
relationship
owl:restriction
(4) Extraction of concepts to be referred to
Depending on (5) Extraction of contexts
(C)
Other properties
relationship (6) Extraction of role concepts
play(playing) (7) Extraction of player (class constraint)
(D) relationship (8) Extraction of role concepts
+ restriction on property names
and/or tracing classes
2012/08/15 IASLOD 2012 51
52. System architecture
A Java client application version and
a web service version are available.
Ontology Exploration Tool Browsing conceptual
maps using web browser
Ontology exportation
Publish conceptual
conceptual
aspect dialog map visualizer maps on the Web
Connections with
Connections with
Connections with
other web
other web
other web
Concept tracing module
concept extraction module systems through
systems through
systems through
concepts defined
concepts defined
concepts defined
in the ontology
in the ontology
in the ontology
import
Hozo-ontology editor OWL ontology
Legends
Ontology building inputs by users flows of data
commands
2012/08/15 IASLOD 2012 52
56. Search
Path Ending point (1)
Selecting of ending points
Finding all possible
paths from stating
point to ending points
Starting point
Ending point (2)
Ending point (3)
2012/08/15 IASLOD 2012 56
58. 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
59. 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
2012/08/15 IASLOD 2012 59
60. 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: Sustainability Science
1,075 http://en.ir3s.u-tokyo.ac.jp/about_sus
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 RISS, Osaka Univ.
science. [Kumazawa 2009]
2012/08/15 IASLOD 2012 60
61. Verification of exploring capability of
ontology exploration tool
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 method
1) Enrichment of SS ontology
The enriched the SS ontology on the basis of 29 typical scenarios which a domain
We concepts appearing in these
expert organized problem structures in biofuel domains by reviewing existing research.
scenarios were extracted and
generalized to add into scenario reproducing operations
2) Verification of the ontology
We verified whether the ontology exploration tool could generate conceptual maps
which represent original scenarios.
burn agriculture=(deforestation, soil deterioration caused by farmland development for
Result
biofuel crops)⇒ harvest sugarcanes (air pollution caused by intentional burn),disruption of
ecosystem93% (27/29) of original scenarios were successfully reproduced as
caused by deforestation(water pollution)
conceptual maps.
The rest (2 scenarios) could not be reproduced because we missed to
Example: Air pollution, cause of forest fire, soil deterioration, water pollution are attributed
add some relationships in the ontology.
to intentional burn when forest is logged or sugarcanes are harvested in the
We can conclude that the for biofuel crops. ability of the tool is sufficient.
farmland development exploration
2012/08/15 IASLOD 2012 61
62. 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.
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.
2012/08/15 IASLOD 2012 62
63. Experiment for evaluating
ontology exploration tool
Experimental method
1) 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.
The subjects: 3) They select paths according to their
4 experts in different fields. interests and enter a four-level general
A: Agricultural economics evaluation with free comments.
B: Social science
(stakeholder analysis) A: Interesting
C: Risk analysis B: Important but ordinary
D: Metropolitan environmental
planning
C: Neither good or poor
D: Obviously wrong
2012/08/15 IASLOD 2012 63
64. Experimental results (1)
Table.2 Experimental results . l
Number of Path distribution based on general evaluation
selected paths A B C D a
Expert A 2 2
Expert A
(second time) 1 1
Expert B 7 4 1 2
Task 1
Expert B
(second time) 6 3 3
Expert C 8 1 5 2
Expert D 3 1 1 1
Expert A 1 1 E
Task 2
Expert B 6 5 1 n
Expert C 7 2 4 1 in
Expert D 5 3 1 1
Expert B 8 4 2 2 c
Task 3 Expert C 4 2 2 n
Expert D 3 3 p
Total 61 30 22 8 1
2012/08/15 IASLOD 2012 64
65. Experimental results (1)
Table.2 Experimental results . l
Number of maps
Number of Path distribution based on general evaluation
generated: 13 selected paths A B C D a
Expert A 2 2
Number of paths evaluated:1 61
Expert A
1
(second time)
A: Expert B
Interesting 307 (49%) 4 1 85%
2
B: Expert B
Important but6 ordinary 22 (36%)
Task 1
3 3
C: Expert C good or poor 8(13%)5
Neither
(second time)
8 1 2
D: Expert D
Obviously wrong 1(2%)
3 1 1 1
Expert A 1 1 E
We can conclude that the tool could generate
Task 2
Expert B 6 1 5 n
Expert C 7 4 1 2 in
maps or paths sufficiently meaningful for experts.
Expert D 5 1 1 3
c
Expert B 8 4 2 2
n
Number of paths
Task 3 Expert C 4 2 2
Expert D 3 3 p
evaluated: 61
Total 61 30 22 8 1
2012/08/15 IASLOD 2012 65
66. Experimental results (2)
Quantitatively comparison of the anticipated maps with the
maps generated by the subjects
(N) Nodes and links (M) Nodes and links included
included in the paths in the paths of generated and
of anticipated maps selected by the experts
50 50 150
About half (50%) of N∩M
the paths About 75% of paths in the
included in the anticipated maps generated maps are new paths
were included in the maps which is not anticipated from
generated by the experts. 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.
2012/08/15 IASLOD 2012 66
67. Exploration of ontology
vs. exploration of linked data
Paths expected by Paths generated by
ontology developers the experts
50 50 150 New paths which is
unexpected from
at the time of
ontology construction.
Paths expected Unexpected (Main) Target
by developer paths of exploration
Exploration of
Liked Data
✓ Instance level
Exploration of
Ontology
✓ ✓ Class level
Liked data is based on a more rich ontologies
→more meaningful paths through divergent.
2012/08/15 IASLOD 2012 67
68. 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.
2012/08/15 IASLOD 2012 68
69. A consensus-building support system
・Display multiple concept
Map
maps
2 ・Highlight common concepts
Map ・Highlight different concepts
1
Map
4
Touch-Table
Map
3
2nd Step: Collaborative workshop
1st Step: Individual concept map
2012/08/15 IASLOD 2012
creation 69
70. The first experimental workshop using
the consensus-building support
system
Discussion using
integrated maps displayed
on a touch-table display
Participants
- 5 experts in sustainability science
- 4 students in environmental engineering
2012/08/15 IASLOD 2012 70
71. 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
2012/08/15 IASLOD 2012 71
72. An example of causal chain
constituted diabetes.
possible causes and effects
… … … …
Type I diabetes …
… Destruction of Diabetes Elevated level Diabetes-related
pancreatic Lack of insulin I
beta cells in the blood
Deficiency
of insulin
of glucose in
the blood
Blindness
loss of sight
… …
Legends
Long-term steroid
treatment … Disorder (nodes)
… Causal Relationship
Steroid diabetes … Core causal chain of a disease
(each color represents a disease)
2012/08/15 IASLOD 2012 72
73. An example of causal chain
constituted diabetes.
possible causes and effects
… … … …
Type I diabetes …
… Destruction of Diabetes Elevated level Diabetes-related
pancreatic Lack of insulin I
beta cells in the blood
Deficiency
of insulin
of glucose in
the blood
Blindness
loss of sight
… …
Legends
Long-term steroid
Based on abnormal state ontology causal chains defined in
treatment … Disorder (nodes)
…
each areas are generalized and organized across domains.
Causal Relationship
Steroid diabetes … Core causal chain of a disease
(each color represents a disease)
MD in 12 areas describe definitions (causal chains) of disease
2012/08/15 IASLOD 2012 73
74. Visualizing/reasoning
causal chains in human body
• 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.
2012/08/15 IASLOD 2012 74
75. Motivation: Dynamic Is-a Hierarchy
Generation System based on User's
Viewpoint
Understanding
Domain experts often want to understand the from their own
target world from their own domain-specific viewpoints
viewpoint.
Disease
In some domains, there are many ways to
categorize the same kinds of concepts.
How diseases are named
named by the major symptom disease classification by
diabetes, angina… the symptom
named by the abnormal object infarction stenosis hyperglucemia
heart disease, … disease disease disease
named by the cause of the disease Myocardial
Stroke Angina diabetes
Myocardial infarction, stroke infarction
named by the specific environment
Altitude sickness, … disease classification by the
disease abnormal object
named by the discoverer
heart brain blood
Grave’s disease… disease disease disease
Myocardial
infarction
diabetes Stroke Angina
Myocardial
infarction
Stroke Angina diabetes
One is-a hierarchy of diseases cannot
cope with such a diversity of viewpoints. Several is-a hierarchies of diseases
according to their viewpoints
2012/08/15 IASLOD 2012 75
76. Existing approaches
Acceptance of multiple ontologies Multiple-inheritance
based on the different perspectives infarction
disease
heart
disease
Multiple-inheritance, Ontology mapping
Myocardial
Problem infarction
If we define every possible is-a hierarchy
using multiple-inheritances or ontology Ontology mapping
mapping, they would be very verbose and disease
the user’s viewpoints would become implicit.
infarction stenosis hyperglycemia
disease disease disease
Exclusion of the multi-perspective Myocardial
infarction Stroke Angina diabetes
nature of domains from ontologies
The OBO Foundry
disease
A guideline for ontology development stating
that we should build only one ontology in
heart brain blood
each domain. disease disease disease
Myocardial
infarction
Stroke Angina diabetes
2012/08/15 IASLOD 2012 76
77. Our approach
Multi-perspective issue Dynamic Is-a Hierarchy
Understanding Generation based on User's
from their own
viewpoints
Viewpoint
Disease
Generation of
is-a hierarchies
We take a user-centric
approach based on
ontological viewpoint
management.
Ontology Viewpoints
Use single-inheritance
2012/08/15 IASLOD 2012 77
78. Our approach: Dynamic is-a Hierarchy
Generation according to User’s
Viewpoint
classification by
disease the symptom
various is-a hierarchies
infarction stenosis hyperglycemia based on individual perspectives
disease disease disease
classification by the
Myocardial
infarction
Stroke Angina diabetes abnormal object
disease
perspective A
「focus on heart brain blood
disease disease disease
symptoms」
parts of human body
abnormal state Myocardial
infarction
Angina Stroke diabetes
heart brain blood
infarction stenosis hyperglycemia perspective B
disease 「focus on abnormal
objects」
Myocardial (2) Reorganizing some
diabetes Stroke Angina
infarction conceptual structures from
(1) Fixing the conceptual structure of an the ontology on the fly as
ontology using single-inheritance based visualizations to cope with
on ontological theories various viewpoints.
2012/08/15 IASLOD 2012 78
79. Our approach: Dynamic is-a Hierarchy
Generation according to User’s
Viewpoint
Multi-perspective issue Dynamic Is-a Hierarchy
Understanding Generation based on User's
from their own
viewpoints
Viewpoint
Disease
Generation of
is-a hierarchies
We take a user-centric
approach based on
ontological viewpoint
Ontology Viewpoints
management.
Use single-inheritance
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”.
2012/08/15 IASLOD 2012 79