This document summarizes an OKFN Korea hackathon event focused on open data. It discusses modeling Seoul open government data using ontologies, linking it to external datasets like cultural heritage data, and publishing the enriched data in RDF format. It covers topics like linked data, modeling with RDF/RDFS/OWL, reusing existing vocabularies, ontology development best practices, and triple store storage solutions.

1. OKFN Korea
Hackathon Day
2013. 06. 22.
Toward Open Data World

2. OKFN Korea2
What is linked
data, Open
data?
Refine
Modelling
Access
Triple
Storage
other topics
image: Leo Oosterloo @ flickr.com

3. 서울시 데이터 Enrichment
 목표
 서울시 데이터 상세화를 위한 온톨로지 설계 또는 매핑
 구조화, 의미화, 그리고 연결: 서울시 데이터 (비정형 데이터)를 온톨로지를 이용해
모델링하고, 외부 데이터와 연결
 영문화: 비 한국어권 사용자가 사용할 수 있는 서울시 데이터 제공
 범위
 서울시 데이터셋 약 40종
 문화재: 문화재청에서 수집한 국내 문화재 (국보, 보물, 지정문화재, 무형문화재 등)
 방법론: 기존 RDF 어휘의 재사용을 통해 데이터 모델링
 1) 데이터 선정: 서울시 열린데이터 광장에서 모델링 대상 데이터셋 선정
 2) 데이터 셋 항목 검토: 데이터 셋의 개별 항목과 Dbpedia 온톨로지 (클래스, 속성)
의 매핑 관계 검토
• Dbpedia 온톨로지: 사물에 대한 개념 및 위키피디아 infobox 항목을 포함하고 있음
OKFN Korea3

4. 서울시 데이터 Enrichment
 예를 들어, '박물관'을 모델링 할 경우,
• 박물관에 대한 infobox 템플릿을 위키피디아에서 선택
• Dbpedia에서 박물관 infobox와 매핑한 어휘 선택
• 어휘와 데이터셋 항목 매핑
• 매핑되지 않는 항목의 모델링 여부 결정 (클래스, 속성 포함): 모델링 도구 결정 필요
• URI 체계 (별도 설계 필요) 적용
• 온톨로지 스키마 설계 완료
 3) 데이터 정제
• Google Refine을 통해 데이터 정제
• Refine에서 추가하기 전에 할 작업
• 위치 데이터: 원본 데이터 (서울시)에 위치값을 변환 또는 추가
• 영문명: 한글명의 변환, 매핑 (수작업 필요)
• Refine에서 할 작업
– 한글, 영문 위키피디아 URL 추가
– Dbpedia, Freebase URL 추가: Refine reconciliation을 이용해서 추가
– RDF 변환 매핑 Skelton 작업
– RDF, Excel 추출
 4) 데이터 업로드 (RDF 또는 Excel)
 데이터 스토어 선택
 Jena, 4Store, …
OKFN Korea4

5. Contents
OKFN Korea
Modeling Issues1
Management Issues2
5

6. Modelling – RDF
Subject Predicate Object

7. Modelling – RDF
Subject Predicate Object
some school has a name/label some literal

8. Modelling – RDF
Subject Predicate Object
http://education.data.gov.uk
/id/school/401874
has a name/label ―Cardiff High School‖

9. Modelling – RDF
Subject Predicate Object
http://education.data.gov.uk
/id/school/401874
http://www.w3.org/2000/01/
rdf-schema#label
―Cardiff High School‖

10. Modelling – RDF
Subject Predicate Object
school:401874 rdfs:label ―Cardiff High School‖
where
school: = http://education.data.gov.uk/id/school/
rdfs: = http://www.w3.org/2000/01/rdf-schema#

11. Modelling – RDF
Subject Predicate Object
school:401874 rdfs:label ―Cardiff High School‖
school:401874 ont:districtAdministrative la:00PT
la:00PT rdfs:label Cardiff

12. Modelling – RDF
Subject Predicate Object
school:401874 rdfs:label ―Cardiff High School‖
school:401874 ont:districtAdministrative la:00PT
la:00PT rdfs:label ―Cardiff‖
school:401874
―Cardiff High School‖
ont:districtAdministrative
la:00PT
―Cardiff‖
rdfs:label
rdfs:label

13. Modelling – RDF
Subject Predicate Object
school:401874 rdfs:label ―Cardiff High School‖
school:401874 ont:districtAdministrative la:00PT
la:00PT rdfs:label ―Cardiff‖
la:00PT rdfs:label ―Caerdydd‖@cy

14. Modelling – vocabularies
Logical modelling
modelling the domain, not a particular
data structure
 what exists
 what is asserted? what can you deduce from
that?
 not about constraints as such
 monotonic, open world
controlled
vocabulary
taxonomy
thesaurus
ontology
Ontology

15. Modelling – vocabularies
unfamiliar terminology but related to
 information architecture and conceptual
modelling
 domain-driven design
 ... and yes knowledge representation

16. Elements of:
 Vocabulary (defining terms)
• I define a relationship called “prescribed dose.”
 Schema (defining types)
• “prescribed dose” relates “treatments” to “dosagee
s”
 Taxonomy (defining hierarchies)
• Any “doctor” is a “medical professional”
16
RDF Schema is…

17. Modelling – RDFS
RDF vocabulary description language
classes, types and type hierarchy
ont:School rdfs:Class
rdf:type
―School‖
rdfs:label

18. Modelling – RDFS
RDF vocabulary description language
classes, types and type hierarchy
ont:WelshEstablishment
ont:School rdfs:Class
rdf:type
rdf:typerdfs:subClassOf
―School‖
rdfs:label

19. Modelling – RDFS
RDF vocabulary description language
classes, types and type hierarchy
school:401874
ont:WelshEstablishment
ont:WelshEstablishment
ont:School rdfs:Class rdf:typerdf:type
rdf:typerdfs:subClassOf
―School‖
rdfs:label

20. Modelling – RDFS
RDF vocabulary description language
classes, types and type hierarchy
school:401874
ont:WelshEstablishment
ont:WelshEstablishment
ont:School rdfs:Class rdf:typerdf:type
rdf:typerdfs:subClassOf
school:401874
ont:WelshEstablishment
ont:School
rdf:type

―School‖
rdfs:label
―School‖
rdfs:label

21. Modelling – RDFS
RDF vocabulary description language
properties, property hierarchy
school:401874
person:JoeBloggs
ont:staffAt
ont:headOf
rdf:Property
ont:headOf
rdf:type
rdfs:subPropertyOf

school:401874person:JoeBloggs
ont:staffAt
ont:headOf

22. Modelling – RDFS
RDF vocabulary description language
class/property relations
 domain
 range
Already have power to do some vocab
ulary mapping
 declare classes or properties from different vo
cabularies to be equivalent:
A rdfs:subClassOf B
B rdfs:subClassOf A

23. WOL OWL is…
23
Web Ontology Language

24. Elements of ontology
 Same/different identity
• “author” and “auteur” are the same relation
• two resources with the same “ISBN” are the same
“book”
 More expressive type definitions
• A “cycle” is a “vehicle” with at least one “wheel”
• A “bicycle” is a “cycle” with exactly two “wheels”
 More expressive relation definitions
• “sibling” is a symmetric predicate
• the value of the “favorite dwarf” relation must be one of
“happy”, “sleepy”, “sneezy”, “grumpy”, “dopey”,
“bashful”, “doc”
OWL is…
24

25. Answer questions of
 Consistency
• Are there any contradictions in this model?
 Classification
• What are all the inferred types of this resource?
 Satisfiability
• Are there any classes in this ontology that cannot p
ossibly have any members?
What can we do with OWL?
25

26. Building Useful Ontologies
 Developing and maintaining quality ontolgies is very
challenging
 Users need tools and services, e.g., to help check
if ontology is:
 Meaningful — all named classes can have instances
http://www.aber.ac.uk/compsci/public/media/presentations/OUCL-seminar.ppt

27. Building Useful Ontologies
 Developing and maintaining quality ontolgies is very
challenging
 Users need tools and services, e.g., to help check
if ontology is:
 Meaningful — all named classes can have instances
 Correct — captures intuitions of domain experts

28. Building Useful Ontologies
 Developing and maintaining quality ontolgies is very
challenging
 Users need tools and services, e.g., to help check if ont
ology is:
 Meaningful — all named classes can have instances
 Correct — captures intuitions of domain experts
 Minimally redundant — no unintended synonyms

Banana split Banana sundae

29. Modelling - OWL
 richer modelling and semantics
 axioms on properties
 transitive, symmetric, inverseOf, ...
 functional, inverse functional
 equivalent property
 axioms on classes
 intersection, union, disjoint, equivalent
 restrictions on classes
 some value from, all values from, cardinality, has value,
one of, keys
 axioms on individuals
 same as, different from, all different
 imports

30. Modelling – OWL
supports much richer modelling
consistency checking of model
consistency checking of data
 some surprises if used to schema languages
 open world, no unique name assumption
 can extend to closed world checking
inference
 classification
 inferred relationships

31. Modelling
Spectrum of goals and styles
Lightweight vocabularies Rich ontological models
 simple modelling
 just enough agreement
to get useful work done
 removing boundaries to
enable information to be
found and connected
 global consistency not
possible
 a little semantics goes
a long way
 rich domain models
 need expressivity
 consistency is critical
 make complex infere
nces you can rely on,
across data you trust
 knowledge is power

32. Modelling
Ontology reuse
invest in complete ontology for a domain
 rich but general model, may be modular inside
 strong ―ontological commitment‖
 e.g. medical ontologies
reuse small, common, vocabularies
 FOAF, SIOC, Dublin Core, Org ...
 pick and choose classes and properties you need
 fill in a few missing links for your domain
generic reusable vocabularies
 Data cube vocabulary

33. Reusable, public on
tologies
33
Measurement Units Ontology
The Event Ontology
FOAF

34. schema.org is one of a number of
microdata vocabularies
it is a shared collection of microdata
schemas for use by webmasters
includes a type hierarchy, like an
RDFS schema
 starts with top-level Thing and DataType
types
 properties are inherited by descendant types
Schema.org
34

35. annotate an item with text-valued
properties using the “itemprop”
attribute
microdata properties
35
<div itemscope>
<p>My name is <span itemprop="name">Daniel</span>.</p>
</div>
<div itemscope>
<p>Flavors in my favorite ice cream:</p>
<ul>
<li itemprop="flavor">Lemon sorbet</li>
<li itemprop="flavor">Apricot sorbet</li>
</ul>
</div>

36. Google
Yahoo
Bing
Why should you use schema.org?
36

37. Top types
37

38. maintains schema.org ↔RDF
mappings
 there are mappings for BIBO, DBpedia,
Dublin Core, FOAF, GoodRelations, SIOC,
and WordNet
also provides examples, tutorials, and
data dumps
Schema.rdfs.org
38

39. Triple Store
OKFN Korea39

40. Triple Store & RDB
OKFN Korea
http://blog.gniewoslaw.pl/2012/11/relational-databases-vs-triple-stores/
40

41. Storage Solutions
for RDF Data
Triple Table (Basic Idea)
 Store all RDF triples in a single table
 Create indexes on combinations of S, P, and O
OKFN Korea41

42. The Internet Map
OKFN Korea
http://internet-map.net/
42

43. credits
These slides are partially based on
“Linked data and its role in the
semantic web” by Dave Reynolds,
Epimorphics Ltd.
OKFN Korea43

44. OKFN Korea