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It was presented at IASLOD2012(International Asian Summer School on Linked Data ) http://semanticweb.kaist.ac.kr/2012lodsummer/
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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/
ISWC ESWC ASWCSWWS @ California, USA
ISWC2002 @ Sardinia, Italy
ISWC2003 @ Sanibel Island,FL,USASymposium@Osamka,
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
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…
ISWC ESWC ASWCSWWS @ California, USA
ISWC2002 @ Sardinia, Italy
ISWC2003 @ Sanibel Island,FL,USASymposium@Osamka,
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
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.
Research Trends in Semantic Web Conferences(2/3)
ISWC ESWC ASWCSWWS @ California, USA
ISWC2002 @ Sardinia, Italy
ISWC2003 @ Sanibel Island,FL,USASymposium@Osamka,
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
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.
Research Trends in Semantic Web Conferences(3/3)
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
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…)
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
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
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
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
Types of Usage of Ontology for a SW Application(3/5)
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
Types of Usage of Ontology for a SW Application(4/5)
(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.
2012/08/15 IASLOD 2012 29
Types of Usage of Ontology for a SW Application(5/5)
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
(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
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
(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
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
(9) Knowledge Systematization
(1) Vocabulary (2) Search (3) Index
(8) Knowledge Modeling
(4)Data Schema (5) Knowledge Sharing
(6) Semantic Analysis
(7) Information Extraction
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
(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
The Combinations of the Types of Usage
2012/08/15 IASLOD 2012 40
(9) Knowledge Systematization
(1) Vocabulary (2) Search (3) Index
(8) Knowledge Modeling
(4)Data Schema (5) Knowledge Sharing
(6) Semantic Analysis
(7) Information Extraction
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
(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
(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
(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
Domains (number of systems) The number of the types of usage
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
(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
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%
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
(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
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
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
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
2012/08/15 50IASLOD 2012
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
2012/08/15 IASLOD 2012 52
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
Connections with other web systems through concepts defined in the ontology
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
2012/08/15 IASLOD 2012 54
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
2012/08/15 IASLOD 2012 59
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]
2012/08/15 IASLOD 2012 60
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.
2012/08/15 IASLOD 2012 61
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.
2012/08/15 IASLOD 2012 62
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.
2012/08/15 IASLOD 2012 63
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)
2012/08/15 IASLOD 2012 64
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)
2012/08/15 IASLOD 2012 65
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.
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
2012/08/15 IASLOD 2012 66
(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 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
2012/08/15 IASLOD 2012 67
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.
2012/08/15 68IASLOD 2012
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
2012/08/15 69IASLOD 2012
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.
2012/08/15 IASLOD 2012 73
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.
2012/08/15 IASLOD 2012 75
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
Angina diabetesMyocardialinfarction Stroke
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
Angina diabetesMyocardialinfarction Stroke
disease
hyperglycemiadisease
disease
heartdisease
braindisease
Angina diabetesMyocardialinfarction Stroke
blooddisease
Ontology mapping
Our approach
2012/08/15 IASLOD 2012 77
Ontology Viewpoints
Generation of is-a hierarchies
Dynamic Is-a Hierarchy Generation based on User's Viewpoint
Understanding from their own
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
Angina diabetesMyocardialinfarction Stroke
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
Understanding from their own
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)