Schema and Identity for Linked Data

2012 INTERNATIONAL ASIAN SUMMER SCHOOL IN LINKED DATA
IASLOD 2012, August 13-17, 2012, KAIST, Daejeon, Korea

Identity and schema for Linked Data

Hideaki Takeda
National Institute of Informatics
takeda＠nii.ac.jp
Hideaki Takeda / National Institute of Informatics

How to put the data into computer?
• How to describe the data?
– The way to describe individual data
• Schema/Class/Concept
– The way to describe relationship among
schema/class/concept
• Ontology/Taxonomy/Thesaurus

• How to refer the data?
– The way to identify individual data
• Identifier
– Relationship among identifiers

Architecture for the Semantic Web
 The world of classes (Ontologies)
 The world of instances
(Linked Data)

Tim Berners-Lee http://www.w3.org/2002/Talks/09-lcs-sweb-tbl/

Layers of Semantic Web
• Ontology
– Descriptions on classes
– RDFS, OWL
– Challenges for ontology building
• Ontology building is difficult by nature
– Consistency, comprehensiveness, logicality
• Alignment of ontologies is more difficult

Descriptions on classes
Ontology

インスタンスに関する記述
Linked Data


Layers of Semantic Web
• Linked Data
– Descriptions on instances (individuals)
– RDF + (RDFS, OWL)
– Pros for Linked Data
• Easy to write (mainly fact description)
• Easy to link (fact to fact link)
– Cons for Linked Data
• Difficult to describe complex structures
• Still need for class description (-> ontology)
Descriptions on classes
Ontology

Description on instances
Linked Data


Importance of Identifiers for Entities
• Everything should be identifiable!
• Human can identify things with vague
identifiers or even without identifiers with
help from the context around things
• On the web, the context is usually not
available and the computer can seldom
understand the context even if it exists
• So we need identifiers for all things


Identification System
• Identification is one of the primary functions for
human information processing
– Naming: e.g., names for people, pets, and some daily
things
• OK if the number of things is not so big
– Systematic Identification
• e.g., phone number, post-code, passport number, product number,
ISBN
• If the number of things is big enough
• Requirements for Systematic Identification
– Identifier is stable and sustainable
– Uniqueness is guaranteed
– Identifier publisher is reliable and sustainable


Identification system for Web
• Not so different from conventional identification systems
• Difference
– Cross-system use
– Truly digitized

• Requirements for Systematic Identification for web
– Identifier is stable and sustainable (even after an entity may
disappear)
– Uniqueness is guaranteed over all systems
– Description on should be associated to identifiers
• since entities may not accessible
– Identifier publisher is reliable and sustainable


Solutions for the Requirements by LOD
• Requirements for Systematic Identification for
web
– 1. Identifier is stable and sustainable (even after an
entity may disappear)
• (up to each identifier publisher)
– 2. Uniqueness is guaranteed over all systems
• URI (not URN)
– 3. Description on should be associated to identifiers
• Dereferenceable URI
– If URI is accessed, a description associated to it should be
returned
– 4. Identifier publisher is reliable and sustainable


Some examples
ISBN(International Standard Book Number)
• Abstract
– a unique numeric commercial book identifier
– 13 digits
• Prefix: 978 or 979 (for compatibility with EAN code)
• Group(language-sharing country group): 1 to 5 digits
• Publisher code:
• Item number:
• Check num: 1 digit
– Management: two layers
• National ISBN Agency – Publisher
• Requirement Satisfaction
– 1. (Stable ID) Maybe (versioning often matters, and sometimes publisher may
re-use ISBN)
– 2. (Unique ID) Uniqueness is guaranteed but not URI
– 3. (Dereferenceable) No mechanisms (amazon does instead!)
– 4. (Reliable publisher) Yes Hideaki Takeda / National Institute of Informatics

Some examples
DOI (Digital Object Identifier)
• Abstract
– An identifier for scientific digital objects (mostly scientific articles)
– An unfixed string: “prefix/suffix”
• Prefix: assigned for publishers
• Suffix: assigned for each object
– Management: three layers
• IDF (International DOI Foundation) – Registration Agency – Publisher
– 1. (Stable ID) Yes (not re-usable)
– 2. (Unique ID)Uniqueness is guaranteed and URI accessible
(http://dx.doi.org/”DOI”)
– 3. (Dereferenaceable)Mapping to object pages but no RDF
– 4. (Reliable publisher) Maybe


Some examples
Dbpedia (as Identifier)
• Abstract
– A wikipedia page
– Name of wikipedia page
• Maintained manually
– Disambiguation page
– Redirect page
– 1. (Stable ID) maybe (sometimes disappear, sometimes change names,
sometime change contents)
– 2. (Unique ID) Uniqueness is mostly guaranteed and URI accessible
– 3. (Dereferenceable) RDF
– 4. (Reliable publisher) Maybe

•


Identification of relationship between
identifiers
• Co-existence of multiple identification systems on a
field
– Difference of coverage
– Difference of Viewpoint
 An entity can have multiple identifiers
 Need for mapping between identifiers in different
identification systems
 Method: Use special properties
 owl:sameAs, (rdfs:seeAlso, skos:exactMatch)
 http://sameas.org
 Some problems
– Logical inconsistency with owl:sameAs
– Maintainance


LOD Cloud
(Linking Open Data)


Summary for ID
• Identification is the crucial part in LOD
– Data availability
– Data inconsistency
– Data interoperability
• Establishment of a good identification system
leads a reliable and sustainable LOD.


Structuring Information
• A wide range of structuring information
– Keywords, tags
• A freely chosen word or phrase just indicating some features
– Controlled vocabulary
• Mapping to the fixed set of words or phrases
• e.g., the list of countries, the name authorities
– Classification
• System for classifying entities. Often hierarchical. Class may not carry meaning.
– Taxonomy
• Hierarchical term system for classification. Upper/lower relation usually means
general/specific relation
• e.g., the subject headings of LC
– Thesaurus
• System for semantics. More different types of relations: (hypersym, hyposym),
synonym, antonym, homonym, holonym, meronym
– Ontology
• System of concepts. Concepts rather than words. More various relations, the
definitions of concepts

Examples in Library Science
• Many systems in the library community
• Classification
– Universal Decimal Classification (UDC)
• Controlled Vocabulary
– the authority files for person names, organizations, location names
• Library of Congress : 8 Million records, MADS &SKOS
• British Library: 2.6 million records, foaf & BIO (A vocabulary for
biographical information)
• National Diet Library (Japan): 1 million records, foaf
• Deutsche Nationalbibliothek (DNB, Germany): 1.8 & 1.3 million records
(names & organization),
• Virtual International Authority File (VIAF): 4 million records
• Taxonomy
– Subject Heading: LC, NDL,
• Library of Congress: MADS &SKOS
• British Library:
• National Diet Library (Japan): 0.1 million records, SKOS
• Deutsche Nationalbibliothek (DNB, Germany): 0.16 million records

UDC ELEMENT DEFINITION
UDC as Linked Data SKOS TERM UDC
SUBPROPERTY

UDC number (notation) UDC notation is combination of symbols (numerals, signs and letters) that represent a class, its skos:notation ---
position in the hierarchy and its relation to other classes. Notation is a language-independent
indexing term that enables mechanical sorting and filing of subjects. Also called 'UDC number'
and 'UDC classmark'
class identifier (URI) A unique identifier assigned to each UDC class. It identifies the relationship between a class' skos:Concept ---
meaning and its notational representation
broader class (URI) Superordinate class: the class hierarchically above the class in question skos:broader ---
caption Verbal description of the class content skos:prefLabel ---
including note Extension of the caption containing verbal examples of the class content (usually a selection of skos:note udc:includingN
important terms that do not appear in the subdivision) ote
application note Instructions for number building, further extension and specification of the class skos:note udc:application
Note
scope note Note explaining the extent and the meaning of a UDC class. Used to resolve disambiguation or skos:scopeNot ---
to distinguish this class from other similar classes e
examples Examples of combination are used to illustrate UDC class building i.e. complex subject skos:example ---
statements
see also reference Indication of conceptual relationship between UDC classes from different hierarchies skos:related ---

<skos:Concept rdf:about="http://udcdata.info/025553">
69,000 records <skos:inScheme rdf:resource="http://udcdata.info/udc-schema"/>
40 Languages <skos:broader rdf:resource="http://udcdata.info/025461"/>
<skos:notation rdf:datatype="http://udcdata.info/UDCnotation">510.6</skos:notation>
<skos:prefLabel xml:lang="en">Mathematical logic</skos:prefLabel>
<skos:prefLabel xml:lang="ja">記号論理学</skos:prefLabel>
<skos:related rdf:resource="http://udcdata.info/000016"/>
http://udcdata.info/ </skos:Concept>

http://id.loc.gov/authorities/names/n79084664.html <http://id.loc.gov/authorities/names/n79084664>
<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>
<http://www.loc.gov/mads/rdf/v1#PersonalName> .
<http://id.loc.gov/authorities/names/n79084664>
<http://www.loc.gov/mads/rdf/v1#Authority> .
<http://www.loc.gov/mads/rdf/v1#authoritativeLabel>
"Natsume, Sōseki, 1867-1916"@en .
<http://www.loc.gov/mads/rdf/v1#elementList>
_:bnode7authoritiesnamesn79084664 .
_:bnode7authoritiesnamesn79084664
<http://www.w3.org/1999/02/22-rdf-syntax-ns#first>
<http://www.w3.org/1999/02/22-rdf-syntax-ns#rest>
_:bnode010 .
<http://www.loc.gov/mads/rdf/v1#FullNameElement> .
<http://www.loc.gov/mads/rdf/v1#elementValue>
"Natsume, Sōseki,"@en .
_:bnode010 <http://www.w3.org/1999/02/22-rdf-syntax-ns#first>
_:bnode010 <http://www.w3.org/1999/02/22-rdf-syntax-ns#rest>
<http://www.w3.org/1999/02/22-rdf-syntax-ns#nil> .
<http://www.loc.gov/mads/rdf/v1#DateNameElement> .

http://id.loc.gov/authorities/subjects/sh85008180.html


http://data.bnf.fr/11932084/intelligence_artificielle/


Some examples
Scientific Names for Species and Taxa
• Abstract
– Names for biological species and other taxa (kingdom, divison, class,
order, family, tribe, genus)
– A string
• Binomial name for species
• Academic societies maintain taxon names individually
– E.g., Papilo xuthus (Asian Swallowtail, ナミアゲハ,호랑나비)

– 1. Mostly yes (sometimes disappear, change names, change contents)
– 2. Uniqueness is generally guaranteed but precise speaking some ambiguity
because of change.
– 3. No. Many systems exists but none covers all species
– 4. Maybe


植物藻類菌類動物
分類群 Taxon
Plants Algae Fungi Animals
ドメイン Domain
界 Kingdom
門 Division/Phylum -phyta -phyta -mycota
亜門 Subdivision/Subphylum -phytina -phytina -mycotina
綱 Class -opsida -phyceae -mycetes
亜綱 Subclass -idae -phycidae -mycetidae
目 Order -ales -ales -ales
亜目 Suborder -ineae -ineae -ineae
上科 Superfamily -acea -acea -acea -oidea
科 Family -aceae -aceae -aceae -idae
亜科 Subfamily -oideae -oideae -oideae -inae
族/連 Tribe -eae -eae -eae -ini
亜族/亜連 Subtribe -inae -inae -inae -ina
属 Genus
亜属 Subgenus
種 Species
亜種 Subspecies Hideaki Takeda / National Institute of Informatics

Ontology
An ontology is an explicit specification of a
conceptualization [Gruber]
 An ontology is an explicit specification of a conceptualization. The
term is borrowed from philosophy, where an Ontology is a systematic
account of Existence. For AI systems, what "exists" is that which can
be represented. When the knowledge of a domain is represented in a
declarative formalism, the set of objects that can be represented is
called the universe of discourse. This set of objects, and the
describable relationships among them, are reflected in the
representational vocabulary with which a knowledge-based program
represents knowledge. Thus, in the context of AI, we can describe the
ontology of a program by defining a set of representational terms. In
such an ontology, definitions associate the names of entities in the
universe of discourse (e.g., classes, relations, functions, or other
objects) with human-readable text describing what the names mean,
and formal axioms that constrain the interpretation and well-formed
use of these terms. Formally, an ontology is the statement of a logical
theory.


Conceptualization
object

on_desk(A)
box on(A, B)
put(A,B)

red box blue box yellow box

object on_desk(A) object on(A/box, B/object)
on(A, B) put(A/box,B/object)
put(A,B)
box box desk
box
box color:{red, blue, yellow}
color:{red, blue, yellow}

There are many possible ways to conceptualize the target world
Trade off between generality and efficiency

Types of Ontologies
• Upper (top-level) ontology vs. Domain ontology
– Upper Ontology: A common ontology throughout all domains
– Domain Ontology: An ontology which is meaningful in a specific
domain
• Object ontology vs. Task ontology
– Object Ontology: An ontology on “things” and “events”
– Task Ontology: An ontology on “doing”
• Heavy-weight ontology vs. light-weight ontology
– Heavy-weight ontology: fully described ontology including
concept definitions and relations, in particular in a logical way
– Light-weight ontology: partially described ontology including
typically only is-a relations


Top-level ontology
• Ontology which covers all of the world!
• Very…. Difficult
– e.g., how does a thing exist?
• A thing is four dimensional existence?
• A thing exists three-dimensionally over time?
• Common requirements
– A small number of concepts can cover the world
– Concepts can be used in lower ontologies

– Concept should be general and abstract


• Three approaches Top-level ontology
– Formal approach
• Logical formalization
• Fully Abstract
• Pros: clean
• Cons: hardly understandable
• e.g., Sowa’s top-level ontology, DOLCE
– Linguistic approach
• Use and extension of linguistic concepts
• Partially abstract and partially general
• Pros: understandable
• Cons: limitation to the linguistic world
• e.g., Penman Upper Model, WordNet
– Empirical Approach
• Use and extension of everyday concepts
• Mostly general
• Pros: understandable and applicable to all the world
• Cons: lack of solid foundation
• e.g. SUMO, Cyc, EDR


Empirical top-level ontology
• SUMO(Suggested Upper
Merged Ontology)
– Collection and organization of Ｓｕｂｓｔａｎｃｅ

concepts used frequently Ｏｂｊｅｃｔ
ＳｅｌｆＣｏｎｎｅｃｔｅｄＯｂｊｅｃｔ

ＣｏｒｐｕｓｃｕｌａｒＯｂｊｅｃｔ
Ｏｒｇａｎｉｃ

Ｉｎｏｒｇａｎｉｃ

– Simple relationship between Ｐｈｓｉｃａｌ
Ｃｏｌｌｅｃｔｉｏｎ

Ｂｉｏｌｏｇｉｃａｌ
Ｐｈｉｓｉｏｌｏｇｉｃ
Ｐｒｏｃｅｓｓ
ＮａｔｕｒａｌＰｒｏｃｅｓｓ
concepts Ｐｒｏｃｅｓｓ
Ｐａｔｈｏｊｏｇｉｃ

ＣｈａｎｇｅＯｆＰｒｏｓｓｅｓｓｉｏｎ
ＰｒｏｃｅｓｓＩｎｔｅｎｔｉｏｎａｌｌｙ
ＣａｕｓｅｄＳｅａｒｃｈｉｎｇＣｏｍｍｕｎｉｃａｔｉｏｎ
Entity ＳｏｃｉａｌＣｏｏｐｅｒａｔｉｏｎ
Ｉｎｔｅｒａｃｔｉｏｎ
Ｃｏｎｔｅｓｔ

Ｍｅｅｔｉｎｇ

Transfer Impelling

Putting
Impacting
Ｍｏｔｉｏｎ
Removing
BringingTogether
Abstract ＣｈａｎｇｅＯｆ Transportation
State Separating

Formal Ontology: DOLCE
• DOLCE(a Descriptive Ontology for Linguistic
and Cognitive Engineering)
– Intended to a reference system for top-level
ontology
– Logical definition
– Particular (DOLCE) vs. Universal
• Particular: ontology about things, phenomena, quality…
• Universal: ontology for describing particular like
categories and attributes


M

Formal Ontology: DOLCE
Amount of
Matter

PED
F
Physical APO
Feature
Endurant Agentive
Physical Object
POB
Physical
• Concepts Object NAPO
Non-agentive

– Endurant / Perdurant / Quality / Abstract NPED
Non-Physical
Physical Object

NPOB MOB
• Endurant: ED
Endurant
Non-physical
Object
Mental Object
Endurant
– “Things” AS
Arbitrary
SOB
Social Object

– An existence over time Sum
ACH

– May change its attribute
Achievement
EV
Event
PD ACC
• Perdurant ALL
Entity
Perdurant
Occurence
Accomplishment

– “process” STV
ST
State
– No change over time Stative
PRO
– May switch a part to the other Process

• Relations Q
TQ
Temporal　Quality
TL
Temporal　Location

– Parthood (abstract or perdurant) Quality
PQ
SL
Physical　Quality
– Temporally Parthood (endurant) AQ
Spatial Location

– Constitution (endurant or perdurant) Abstract　Quality

– Participation between perdurant and endurantAB Fact TR
Temporal　Region
Abstract T
Set Time　Interval
PR
Physical　Region
R S
Region Space　Region
AR
Abstract　Region

Linguistic top-level ontology
• WordNet
– A lexical reference system
• “Link-based electronic dictionary”
http://www.cogsci.princeton.edu/cgi-bin/webwn

– Concepts
• synset
– Noun 79,689
– Verb 13,508
– Relations
• synonym
• hypernym/hyponym (is-a)
• holonym/meronym (a-part-of)


•
Linguistic top-level ontology
WordNet
– Top-level
• { entity, physical thing (that which is perceived or known or inferred to
have its own physical existence (living or nonliving)) }
• { psychological_feature, (a feature of the mental life of a living organism) }
• { abstraction, (a general concept formed by extracting common features
from specific examples) }
• { state, (the way something is with respect to its main attributes; "the
current state of knowledge"; "his state of health"; "in a weak financial
state") }
• { event, (something that happens at a given place and time) }
• { act, human_action, human_activity, (something that people do or cause
to happen) }
• { group, grouping, (any number of entities (members) considered as a
unit) }
• { possession, (anything owned or possessed) }
• { phenomenon, (any state or process known through the senses rather
than by intuition or reasoning) }


Summary for structuring information
• Keywords, tags/Controlled vocabulary
/Classification/Taxonomy /Thesaurus/Ontology
– The difference is not clear, not important
– The trend is to go more structured ones
– The same requirements to Identification systems


Summary
• Requirements for Successful Structuring
Systems
– 1. Entity is stable and sustainable
LOD Tech.
– 2. Uniqueness is guaranteed over all systems can help
– 3. Description on should be associated to entity
– 4. System publisher is reliable and sustainable
• Learn from success in the library community


Schema/Vocabulary for LOD
• Class/Concept description
– Axiom of a concept in ontology
– Database schema for a table in Relational database
– Object definition in Object-Oriented Programming/DB
• Class description in Semantic Web
– RDFS/OWL description for a class
• RDFS: Simple class system
• OWL: Description Logic-based
• Class description in Linked Data
– Mostly RDFS-based (exception: owl:sameAs)
– Simple Structure (mostly property-value pair)


Schema/Vocabulary for LOD
• The importance of sharing schema
– Interoperability
– Generic applications
• Some famous and frequently used shemata
– Dublin Core
– FOAF (Friend-Of-A-Friend)
– SKOS (Simple Knowledge Organization System)


Usage of Common Vocabularies
Prefix Namespace Used by

dc http://purl.org/dc/elements/1.1/ 66 (31.88 %)
foaf http://xmlns.com/foaf/0.1/ 55 (26.57 %)
dcterms http://purl.org/dc/terms/ 38 (18.36 %)
skos http://www.w3.org/2004/02/skos/core# 29 (14.01 %)

akt http://www.aktors.org/ontology/portal# 17 (8.21 %)

geo http://www.w3.org/2003/01/geo/wgs84_pos# 14 (6.76 %)
mo http://purl.org/ontology/mo/ 13 (6.28 %)
bibo http://purl.org/ontology/bibo/ 8 (3.86 %)

vcard http://www.w3.org/2006/vcard/ns# 6 (2.90 %)

frbr http://purl.org/vocab/frbr/core# 5 (2.42 %)
sioc http://rdfs.org/sioc/ns# 4 (1.93 %)
LDOW2011 Presentation, Christian Bizer (Freie Universität Berlin), 2011

(Simple) Dublin Core
• Started from the library • 15 elements
community – Title
• Now maintained by DCMI (Dublin – Creator
Core Metadata Initiative) – Subject
• (Simple) Dublin Core – Description
– Just 15 elements – Publisher
– Simple is best – Contributor
– No range restriction – Date
– http://purl.org/dc/elements/1.1/ – Type
– Format
– Identifier
– Source
– Language
– Relation
– Coverage
– Rights


dc terms
• Qualified Dublin Core
– Domain & Range
– More precise terms
• Extension of simple dc

Properties in the / abstract , accessRights , accrualMethod , accrualPeriodicity , accrualPolicy , alternative , audience , available , bibliograp
hicCitation ,conformsTo , contributor , coverage , created , creator , date , dateAccepted , dateCopyrighted , dateSubmit
ted , description ,educationLevel , extent , format , hasFormat , hasPart , hasVersion , identifier , instructionalMethod , i
sFormatOf , isPartOf , isReferencedBy ,isReplacedBy , isRequiredBy , issued , isVersionOf , language , license , mediator ,
medium , modified , provenance , publisher , references ,relation , replaces , requires , rights , rightsHolder , source , sp
atial , subject , tableOfContents , temporal , title , type , valid
Properties in the contributor , coverage , creator , date , description , format , identifier , language , publisher , relation , rights , source , s
/elements/1.1/namespace ubject , title , type
Vocabulary Encoding Schemes DCMIType , DDC , IMT , LCC , LCSH , MESH , NLM , TGN , UDC
Syntax Encoding Schemes Box , ISO3166 , ISO639-2 , ISO639-3 , Period , Point , RFC1766 , RFC3066 , RFC4646 , RFC5646 , URI , W3CDTF

Classes Agent , AgentClass , BibliographicResource , FileFormat , Frequency , Jurisdiction , LicenseDocument , LinguisticSystem ,
Location ,LocationPeriodOrJurisdiction , MediaType , MediaTypeOrExtent , MethodOfAccrual , MethodOfInstruction , Pe
riodOfTime , PhysicalMedium ,PhysicalResource , Policy , ProvenanceStatement , RightsStatement , SizeOrDuration , Sta
ndard
DCMI Type Vocabulary Collection , Dataset , Event , Image , InteractiveResource , MovingImage , PhysicalObject , Service , Software , Sound , Sti
llImage , Text
Terms related to the DCMI memberOf , VocabularyEncodingScheme
Abstract Model Hideaki Takeda / National Institute of Informatics

Dcterms subPropertyOf Domain Range Dcterms subPropertyOf Domain Range

contributor dc:contributor rdfs:Resource dcterms:Agent conformsTo dc:relation, dcterms:relation rdfs:Resource dcterms:Standard

hasFormat dc:relation, dcterms:relation rdfs:Resource rdfs:Resource
dc:creator,
creator rdfs:Resource dcterms:Agent
dcterms:contributor hasPart dc:relation, dcterms:relation rdfs:Resource rdfs:Resource
dcterms:LocationPeriodOr
coverage dc:coverage rdfs:Resource hasVersion dc:relation, dcterms:relation rdfs:Resource rdfs:Resource
Jurisdiction
dc:coverage, isFormatOf dc:relation, dcterms:relation rdfs:Resource rdfs:Resource
spatial rdfs:Resource dcterms:Location
dcterms:coverage
isPartOf dc:relation, dcterms:relation rdfs:Resource rdfs:Resource
dc:coverage,
Temporal rdfs:Resource dcterms:PeriodOfTime
dcterms:coverage isReferencedBy dc:relation, dcterms:relation rdfs:Resource rdfs:Resource
Date dc:date rdfs:Resource rdfs:Literal
isReplacedBy dc:relation, dcterms:relation rdfs:Resource rdfs:Resource
Available dc:date, dcterms:date rdfs:Resource rdfs:Literal

Created dc:date, dcterms:date rdfs:Resource rdfs:Literal isRequiredBy dc:relation, dcterms:relation rdfs:Resource rdfs:Resource

dateAccepted dc:date, dcterms:date rdfs:Resource rdfs:Literal isVersionOf dc:relation, dcterms:relation rdfs:Resource rdfs:Resource

dateCopyrighted dc:date, dcterms:date rdfs:Resource rdfs:Literal References dc:relation, dcterms:relation rdfs:Resource rdfs:Resource

dateSubmitted dc:date, dcterms:date rdfs:Resource rdfs:Literal Replaces dc:relation, dcterms:relation rdfs:Resource rdfs:Resource

Issued dc:date, dcterms:date rdfs:Resource rdfs:Literal Requires dc:relation, dcterms:relation rdfs:Resource rdfs:Resource
Modified dc:date, dcterms:date rdfs:Resource rdfs:Literal Rights dc:rights rdfs:Resource dcterms:RightsStatement
accessRights dc:rights, dcterms:rights rdfs:Resource dcterms:RightsStatement
Valid dc:date, dcterms:date rdfs:Resource rdfs:Literal
License dc:rights, dcterms:rights rdfs:Resource dcterms:LicenseDocument
description dc:description rdfs:Resource rdfs:Resource Subject dc:subject rdfs:Resource rdfs:Resource
dc:description, title dc:title rdfs:Resource rdfs:Resourcerdfs:Literal
Abstract rdfs:Resource rdfs:Resource
dcterms:description alternative dc:title, dcterms:title rdfs:Resource rdfs:Resourcerdfs:Literal
dc:description, type dc:type rdfs:Resource rdfs:Class
tableOfContents rdfs:Resource rdfs:Resource
dcterms:description audience rdfs:Resource dcterms:AgentClass
dcterms:MediaTypeOrExte educationLevel dcterms:audience rdfs:Resource dcterms:AgentClass
format dc:format rdfs:Resource mediator dcterms:audience rdfs:Resource dcterms:AgentClass
nt
dcmitype:Collec
extent dc:format, dcterms:format rdfs:Resource dcterms:SizeOrDuration accrualMethod dcterms:MethodOfAccrual
tion
dcterms:PhysicalR dcmitype:Collec
Medium dc:format, dcterms:format dcterms:PhysicalMedium accrualPeriodicity dcterms:Frequency
esource tion
Identifier dc:identifier rdfs:Resource rdfs:Literal dcmitype:Collec
accrualPolicy dcterms:Policy
bibliographicCitat dc:identifier, dcterms:Bibliograp tion
rdfs:Literal
ion dcterms:identifier hicResource instructionalMethod rdfs:Resource dcterms:MethodOfInstructio
provenance rdfs:Resource dcterms:ProvenanceStatem
Language dc:language rdfs:Resource dcterms:LinguisticSystem
rightsHolder rdfs:Resource dcterms:Agent
Publisher dc:publisher rdfs:Resource dcterms:Agent
Relation dc:relation rdfs:Resource rdfs:Resource http://dublincore.org/documents/dcmi-terms/
source dc:source, dcterms:relation rdfs:Resource rdfs:Resource Hideaki Takeda / National Institute of Informatics
http://www.kanzaki.com/docs/sw/dc-domain-range.html

SKOS (Simple Knowledge Organization
System)
• Metadata for taxonomy
– Hierarchical structure of concepts
• Invented to represent taxonomy such as subject
heading
• =/= subclass relationship among classes
• W3C Recommendation 18 August 2009


System)
• SKOS Core (hierarchical concept structure)
– skos:semanticRelation
– skos:broaderTransitive
subPropertyOf
– skos:narrowerTransitive
– skos:broader
– skos:narrower
– skos:related
– skos:preflabel
– skos:altlabel
– skos:hiddenlabel Hideaki Takeda / National Institute of Informatics

System)
• SKOS Mapping
– skos:mappingRelation
– skos:closeMatch
subPropertyOf
– skos:exactMatch
– skos:broadMatch
– skos:narrowMatch
– skos:relatedMatch


Linked Open Vocabulary (LOV)
• A technical platform for search and quality
assessment among the vocabularies
ecosystem
– Register schemata
– Search schemata
• http://labs.mondeca.com/dataset/lov/


X


More Info.
• http://www.w3.org/2005/Incubator/lld/wiki/V
ocabulary_and_Dataset


Summary for schema
• Some major schemata
– DC, DC terms, FOAF, SKOS …
• More domain-specific schemata
– CIDOC CRM
– PRISM
–…
• Re-using is highly recommended
– LOV


Summary
• Three layers
– Ontology/Thesaurus/Taxonomy
– Schema
– Identification
• Not just top-down, rather bottom-up
• Each layer has own role
• Not pursue the value of each layer, rather
make a good combination of them


Schema and Identity for Linked Data

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