Granularity in Library Linked Open Data

Gordon DunsireKeynote presentation to Code4Lib 2013,

12-14 Feb 2013, Chicago, USA

Overview

Fractals

Self-similar at all levels of granularity

Cannot determine level: all levels are equal!

Multi-faceted granularity

What is described by a bibliographic record?Or a single statement?

What is the level of description?How complete is it?

How detailed is the schema used?How dumb?

Semantic constraints?Unconstrained?

AAA! OWA! Rumsfeld and the white light!

This resource has intended audience JuvenileTriple:

has Granularity?

Subject Predicate Object

Coarse-grained systems consist of fewer, larger components than fine-grained

systems [Wikipedia]

Resource Description Framework – Linked data

Subject: what is the statement about?

Journal title

Issue

Article

JournalsLibrary collection

ParagraphWord

Consortium collection

Graphics

Subjects AccessDigital collection

RDF map

Section PageMarkup

RDF/XMLURI Node

Component

Super-Aggregate

Sub-Component

Aggregate

Focus

coarser

finer

Journal index

Festschrift

Resource Work

Predicate: what is the aspect described?

Access to resource

Access to content

Suitability rating

Membership category

AudienceAudience of audio-visual material

Audience and usage Component

Super-Aggregate

Sub-Component

Aggregate

Focus

coarser

finer

isbd: International Standard Bibliographic Description

dct: Dublin Core terms

schema: Schema.org

rda: Resource Description and Access

m21: marc21rdf.info

frbrer: Functional Requirements for Bibliographic Records, entity-relationship model

unc: unconstrained version

Possible Audience map (partial)

rdfs:subPropertyOf

unc:“has note on

use or audience”

isbd:“has note on

use or audience”

unc:“Intended audience”

rda:“Intended audience”

m21:“Target

audience” frbrer:“has intended

audience”

dct:“audience”

rdfs:subPropertyOf

  

  

m21:“Target

audience of …”

rdfs:subPropertyOf

schema:“audience”

What is the aspect described?

Manifestation record

Title and s.o.r

Title statement

Resource record

Title wordFirst word of title

Title of manifestationComponent

Super-Aggregate

Sub-Component

Aggregate

Focus

coarser

finer

dct:“Title”

dc:“Title” rdfs:

“Literal”

sP

sP

sPr

rdagrp1:“Title proper

(Manifestation)”

rdafrbr:“Manifestation”

rdaopen:“Title proper”

rdaopen:“Title”

rdagrp1:“Title

(Manifestation)”

d

dsP

sP

sP

sP

isbd:“has title proper”

isbd:“has title”

isbd:“Resource”

dd

sP

sP

eP

Possible Title semantic map(partial)

sP: rdfs:subPropertyOfd: rdfs:domain

r: rdfs:range

Semantic reasoning: the sub-property ladder

isbd:“has title proper”

dct:title

rdfs:subPropertyOf

Semantic rule:If property1 sub-property of property2;Then data triple: Resource property1 “string”Implies data triple: Resource property2 “string”

isbd:”Resource” “Physics”

isbd:“has title proper”

Resource “Physics”

dct:“has title”

machineentailment

coarser

finer

dumb-up

“For children aged 7-”ex:3

rda:”Intended audience (Work)”

“For ages 5-9”ex:2

isbd:”has note on use or audience”

“Primary school”ex:1

frbrer:”has intended audience”

“Juvenile”

ex:4

m21:”Target audience” m21terms:

commonaud#j

skos:prefLabel

Data triples from multiple schema

“For ages 5-9”ex:2unc:”has note on use or audience”

Data triples entailed from sub-property map

“Primary school”ex:1unc:”has note on use or audience”

“For children aged 7-”ex:3unc:”has note on use or audience”

“Juvenile”ex:4unc:”has note on use or audience”

ex:1 frbrer:”Work””is a”

ex:3 rda:”Work””is a”

ex:2 isbd:”Resource””is a”

Data triples entailed from property domains

What is the aspect described?

CreatorAuthor

Screenwriter

Children’s cartoon screenwriterAnimation screenwriter Component

Super-Aggregate

Sub-Component

Aggregate

Focus

coarser

finer

drda:”Work”

rdaroles:”Creator”

[rda:”Agent”]rdaroles:”Author (Work)”

rdaroles:”Screenwriter (Work)”

d

d

r

r

r

s

s

dct:”Creator”

dc:”Creator”

dct:”Agent”r

s

marcrel:”Author”

marcrel:”Authorof screenplay, etc.”

dc:”Contributor”

s

lcsh:”Screenwriters”

?

?

?

s: rdfs:subPropertyOfd: rdfs:domain

r: rdfs:range

?

Machine-generated granularity

Full-text indexing: down to word level

A very large multilingual ontology with 5.5 millions of concepts • A wide-coverage "encyclopedic dictionary" • Obtained from the automatic integration of WordNet and Wikipedia • Enriched with automatic translations of its concepts • Connected to the Linguistic Linked Open Data cloud!

User-generated granularity

“OK for my kids (7 and 9)”

“Too childish for me (age 14)”

“Ideal for the child of ambitious parents”

“This sucks – for kids only”

“Great! Has cool stuff”

KISS

Keep it simple, stupid

Keep it simple and stupid?

The data model is very simple: triples!

The (meta)data content is complex

The Mandelbrot Set:“an example of a complex structure arising from

the application of simple rules” - Wikipedia

Resource discovery is complex

AAA

Anyone can say anything about any thing

Someone will say something about every thing

In every conceivable way

Linguistically

OWA

Will all the gaps get filled?

“There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns. There are things we don't know we don't know.”- Donald Rumsfeld

Open World Assumption: the absence of a statement is not a statement of non-existence

!