Ontology Engineering to Enrich Linked Data
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Ontology Engineering to Enrich Linked Data

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It was presented at IASLOD2012(International Asian Summer School on Linked Data ...

It was presented at IASLOD2012(International Asian Summer School on Linked Data
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http://semanticweb.kaist.ac.kr/2012lodsummer/

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  • 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, Japan2012/08/15 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 Areas2012/08/15 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 Data2012/08/15 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, Australia2012/08/15 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) Scalability2012/08/15 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 Web2012/08/15 IASLOD 2012 12
  • 13. From ISWC2011: Keynote by Frank van Harmelen Terminological knowledge is much smaller than the factual knowledge2012/08/15 IASLOD 2012 13
  • 14. From ESWC2011: Keynote by James A. Hendler2012/08/15 IASLOD 2012 14
  • 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 increase2012/08/15 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, Mapping2012/08/15 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 Extraction2012/08/15 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 Extraction2012/08/15 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 Data2012/08/15 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 ata2012/08/15 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 Ontologies2012/08/15 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 s2012/08/15 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, Coverage2012/08/15 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 2732012/08/15 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. knowledge2012/08/15 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 Vocabulary2012/08/15 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 Systematization2012/08/15 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 severalusages 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 Systematization2012/08/15 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 domains2012/08/15 ✓ ✓ 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 Data2012/08/15 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 Users Viewpoint  Presented at JIST20112012/08/15 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 KnowledgeKnowledge knowledge structuring from the general perspective not only sharing × the domain-specific and multiple-perspectives. systematization isbut also from difficult Experts in energy2012/08/15 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 knowledge2012/08/15 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 maps2012/08/15 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 classes2012/08/15 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 commands2012/08/15 IASLOD 2012 52
  • 53. 2012/08/15 IASLOD 2012 53
  • 54. Option settings for exploration Selected relationships Kinds of aspects are traced and shown as links in conceptual map property names constriction tracing classes Conceptual map visualizer Aspect dialog2012/08/15 IASLOD 2012 54
  • 55. Explore the focused (selected) path.2012/08/15 IASLOD 2012 55
  • 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
  • 57. Search Path Selected ending points2012/08/15 IASLOD 2012 57
  • 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 tool2012/08/15 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 exploration2012/08/15 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 wrong2012/08/15 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 12012/08/15 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 12012/08/15 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 map2012/08/15 IASLOD 2012 creation 69
  • 70. The first experimental workshop usingthe consensus-building supportsystem Discussion using integrated maps displayed on a touch-table display Participants - 5 experts in sustainability science - 4 students in environmental engineering2012/08/15 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 structures2012/08/15 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 disease2012/08/15 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 HierarchyGeneration System based on UsersViewpoint 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 viewpoints2012/08/15 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 diabetes2012/08/15 IASLOD 2012 76
  • 77. Our approach Multi-perspective issue Dynamic Is-a Hierarchy Understanding Generation based on Users 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-inheritance2012/08/15 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 bloodinfarction 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 Users 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
  • 80. 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
  • 81. Acknowledgement 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: kozaki@ei.sanken.oaka-u.ac.jp2012/08/15 IASLOD 2012 81
  • 82. 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 …. Natural type  Teacher  Walker Role (dependent concept)  Murderer  Mother2012/08/15 IASLOD 2012 82