Metadata for the Web From Discovery to Description

Cornell CS 502

Metadata for the WebFrom Discovery to Description

CS 502 – 20020224Carl Lagoze – Cornell University

Cornell CS 502

The fifteen Dublin Core Elements

Creator Title Subject

Contributor Date Description

Publisher Type Format

Coverage Rights Relation

Source Language I dentifi er

http://dublincore.org/usage/terms/dc/current-elements/http://dublincore.org

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A Pidgin for Digital Tourists

• Metadata is language• Dublin Core is a small and simple language -- a

pidgin -- for finding resources across domains.• Speakers of different languages naturally

"pidginize" to communicate– E.g., tourists using simple phrases to order beer

("zwei Bier bitte" "dva pivo" "biru o san bai"...)

• We are all "tourists" on the global Internet.

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What is the Dublin Core (1)

• A simple set of properties to support resource discovery on the web (fuzzy search buckets)?

DomainIndependent

view

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What is Dublin Core (2)?

• An extensible ontology for resource desciption?

Gre

ate

r Fun

ction

ality

&

Cost

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What is the Dublin Core (3)?

• A cross-domain switchboard for interoperable metadata?

Switchboard

DublinCore

MARC

INDECSIMS

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Dublin Core Qualifiers

• From fuzzy buckets to more specific description

• Model of “graceful degradation”– Support both simplicity and specificity– Intra-domain and inter-domain semantics

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Varieties of qualifiers: Element Refinements

• Make the meaning of an element narrower or more specific.

• Narrowing implies an is a relationship – a "date created“ is a "date“– an "is part of relation“ is a "relation“

• If your software does not understand the qualifier, you can safely ignore it.

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Varieties of Qualifiers: Value Encoding Schemes

• Says that the value is– a term from a controlled vocabulary (e.g., Library of

Congress Subject Headings)– a string formatted in a standard way (e.g., "2001-05-

02" means May 3, not February 5)

• Even if a scheme is not known by software, the value should be "appropriate" and usable for resource discovery.

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A Grammar of Dublin Core

• http://www.dlib.org/dlib/october00/baker/10baker.html

• By design not as subtle as mother tongues, but easy to learn and extremely useful in practice

• Pidgins: small vocabularies (Dublin Core: fifteen special nouns and lots of optional adjectives)

• Simple grammars: sentences (statements) follow a simple fixed pattern...

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Example Dublin Core statements

• Resource has Title 'Grammar of Dublin Core'.• Resource has Creator 'Tom Baker'.• Resource has Subject 'Metadata'.• Resource has Relation http://foo.org/file.htm.

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Resource has property

DC:CreatorDC:TitleDC:SubjectDC:Date...

X

implied subject

impliedverb

one of 15properties

property value(an appropriateliteral)

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Resource has property

DC:CreatorDC:TitleDC:SubjectDC:Date...

X

implied subject

impliedverb

one of 15properties

property value(an appropriateliteral)

[optional qualifier]

[optional qualifier]

qualifiers(adjectives)

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Resource has Date "2000-06-13"Revised

ISO8601

Resource has Subject "Languages -- Grammar"LCSH

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Dumb-Down Principle for Qualifiers

• The fifteen elements should be usable and understandable with or without the qualifiers

• Qualifiers refine meaning (but may be harder to understand)

• Nouns can stand on their own without adjectives

• If your software encounters an unfamiliar qualifier, look it up -- or just ignore it!

• "has a“ relations break the model– E.g., a creator has a hair color

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Resource has Date "2000-06-13"Revised

ISO8601

Resource has Subject "Languages -- Grammar"LCSH

Test for “good““ qualifiers:cover and ask: -- Does the statement still make sense? -- Is it still correct?

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Resource has subjectaudience

Resource has creatoraffiliation

“Incorrect” Qualification

“Cornell University”

“pre-schoolers”

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Open questions in this model

• Are uncontrolled and unconstrained values really useful for discovery?

• Is it possible for an organization (DCMI) to control the evolution of a language?

• How can "simple discovery metadata" be combined with complex descriptions? Is there a notion of graceful degradation?

• Can DC serve as a lingua franca (mapping template) among more complex models

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Models for Deploying Metadata

• Embedded in the resource– low deployment threshold– Limited flexibility, limited model

• Linked to from resource– Using xlink– Is there only one source of metadata?

• Independent resource referencing resource– Model of accessing the object through its surrogate– Resource doesn’t ‘have’ metadata, metadata is just

one resource annotating another

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Syntax Alternatives:HTML

• Advantages:– Simple Mechanism – META tags embedded in content– Widely deployed tools and knowledge

• Disadvantages– Limited structural richness (won’t support

hierarchical,tree-structured data or entity distinctions).

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Dublin Core in HTML

• http://www.dublincore.org/documents/2000/08/15/dcq-html/

• HTML constructs– <link> to establish pseudo-namespace– <meta> for metadata statements

• name attribute for DC element (DC.element.ER)

• content attribute for element value

• scheme attribute for encoding scheme or controlled vocabulary

• lang attribute for language of element value

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Dublin Core in HTML example

<link rel="schema.DC" href="http://purl.org/dc/elements/1.1"> <meta name="DC.Title" content="Business Unusual”><meta name=“DC.Title” lang=“es” content=“negocio inusual”> <meta name="DC.Creator" content="Carl Lagoze"> <meta name="DC.Subject" content="bibliographic control web cataloging "> <meta name="DC.Date.Created" scheme="W3CDTF"

content="2000-10-23"> <meta name="DC.Format" content="text/html"> <meta name="DC.Identifier" content="http://lcweb.loc.gov/lagoze_paper.html">

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Unqualified Dublin Core in XML

http://dublincore.org/documents/2002/09/09/dc-xml-guidelines/

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Multi-entity nature of object description

Photographer

Camera type Software

Computer artist

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Attribute/Value approaches to metadata…

Hamlet has a creator Shakespeare

subject implied verb metadata noun literal

Play

wrig

ht

metadata adjective

The playwright of Hamlet was Shakespeare

R1

“Shakespeare”

“Hamlet”

dc:creator.playwright

dc:title

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…run into problems for richer descriptions…

Hamlet has a creator Stratford

birt

hpla

ce

The playwright of Hamlet was Shakespeare,who was born in Stratford

“Stratford”R1

“Shakespeare”dc:creator.playwright

dc:creator.birthplace

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…because of their failure to model entity distinctions

R1

“Stratford”

creatorR2

name “Shakespeare”

birthplacetitle

“Hamlet”

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Applying a Model-Centric Approach

• Formally define common entities and relationships underlying multiple metadata vocabularies

• Describe them (and their inter-relationships) in a simple logical model

• Provide the framework for extending these common semantics to domain and application-specific metadata vocabularies.

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Events are key to understanding resource complexity?

• Events are implicit in most metadata formats (e.g., ‘date published’, ‘translator’)

• Modeling implied events as first-class objects provides attachment points for common entities – e.g., agents, contexts (times & places), roles.

• Clarifying attachment points facilitates understanding and querying “who was responsible for what when”.

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ABC/Harmony Event-aware metadata ontology• Recognizing inherent lifecycle aspects of

description (esp. of digital content)• Modeling incorporates time (events and

situations) as first-class objects– Supplies clear attachment points for agents, roles,

existential properties

• Resource description as a “story-telling” activity

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Resource-centric Metadata

Title Anna Karenina

Author Leo Tolstoy

Illustrator Orest Vereisky

Translator Margaret Wettlin

Date Created 1877

Date Translated 1978

Description Adultery & Depression

Birthplace Moscow

Birthdate 1828

?

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“translator”

“Margaret Wettlin”“Orest Vereisky”

“illustrator”

“Anna Karenina”

“Tragic adultery andthe search for meaningfullove”

“English”

“author”

“creation”

“1877”“1978”

“translation”

“Russian”

“Leo Tolstoy”"Moscow"

“1828”

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Breaking the metadata bottleneck Human vs. machine generation

• Simple text scraping – HTML tags as hint– Other structural methods

• Natural language methods and machine learning

• Contextual methods– Google (text and image search)

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Putting metadata in its place

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Query engine architecture space

Queries

Structured Unstructured

Data

Structured

Unstructured

1

3

2

4

(Relational)DatabaseSystems

InformationRetrievalSystems