Mikael Nilsson < [email_address] > et al. Making MLR semantic?

History Contribution in Jeju from a number of experts: “Requirements for ISO MLR interoperability”(WG4 N0238) Suggested basing MLR on semantic technologies The group was tasked to present a proposal for an MLR standard based on semantic technologies Outline of such a proposal was submitted (WG4 N0278) - presented here

Contribution in Jeju from a number of experts:

“Requirements for ISO MLR interoperability”(WG4 N0238)

Suggested basing MLR on semantic technologies

The group was tasked to present a proposal for an MLR standard based on semantic technologies

Outline of such a proposal was submitted (WG4 N0278) - presented here

Background Islands of metadata interoperability, for example The “LOM island” - IEEE LOM and LOM-based profiles The “MODS island” The “MPEG-7 island” The “Dublin Core & RDF island” Two approaches to Application Profiles Base standard – profiles customize the base (LOM, MODS, MPEG-7) Framework only, profiles combine terms arbitrarily (Dublin Core, RDF) <-- MLR wants to be here (?)

Islands of metadata interoperability, for example

The “LOM island” - IEEE LOM and LOM-based profiles

The “MODS island”

The “MPEG-7 island”

The “Dublin Core & RDF island”

Two approaches to Application Profiles

Base standard – profiles customize the base

(LOM, MODS, MPEG-7)

Framework only, profiles combine terms arbitrarily (Dublin Core, RDF) <-- MLR wants to be here (?)

Metadata interoperability now IEEE LOM NorLOM UK LOM Core ... RDF Dublin Core DC APs Semantic Web ... MARC21 MARC-XML METS METS MPEG-7

Metadata interoperability vision Education Government Libraries Multimedia Semantic Web

Levels of interoperability Human interoperability - words Use the same definition of words , regardless of technical framework Semantic interoperability – the cloud Machines apply the same processing to terms whereever they appear This is the purpose of RDF Profile interoperability – the domain Domain-specific interoperability based on shared profiles, vocabularies, etc. Quality control, syntax validation etc.

Human interoperability - words

Use the same definition of words , regardless of technical framework

Semantic interoperability – the cloud

Machines apply the same processing to terms whereever they appear

This is the purpose of RDF

Profile interoperability – the domain

Domain-specific interoperability based on shared profiles, vocabularies, etc.

Quality control, syntax validation etc.

Proposal for MLR DON'T create a new metadata island DON'T create a need for more crosswalks DO use a framework-based approach DO allow for application profiles combining terms from other sources DON'T reinvent the framework DON'T require others to redefine their terms for use in MLR DO base the framework on the RDF model

DON'T create a new metadata island

DON'T create a need for more crosswalks

DO use a framework-based approach

DO allow for application profiles combining terms from other sources

DON'T reinvent the framework

DON'T require others to redefine their terms for use in MLR

DO base the framework on the RDF model

Statement-based models

Statements as graphs title contribution date entity name My learning resource Contribution A Person B “ A book” “ John Smith” “ 2008-09-03”

An XML format defined from an application profile The format depends on the application profile Interpreting as RDF triples is straightforward if application profile is knowns Making an XML schema <LearningResource> <Title>A book</title> <Contribution> <Date>2008-09-03</Date> <Entity> <Name>John Smith</Name> </Entity> </Contribution> </LearningResource>

An XML format defined from an application profile

The format depends on the application profile

Interpreting as RDF triples is straightforward if application profile is knowns

Another example (FOAF-like) <Person uri=”http://example.com/persons#john”> <Name>John Smith</Name> <Email> [email_address] </Email> <Knows uri=”http://example.com/persons#gordon” /> </Person> foaf:name foaf:mbox foaf:knows http://example.com/persons#john “ John Smith” “ [email_address] ” http://example.com/persons#gordon

Walk-through <LearningResource grddl:transform=”http://yyy/mlr.xsl” > <Title>A book</title> <Contribution> <Date>2008-09-03</Date> <Entity> <Name>John Smith</Name> </Entity> </Contribution> </LearningResource> http://yyy/mlr.xsl foaf:name foaf:mbox foaf:knows title contribution date entity name foaf:mbox foaf:knows http://example.com/persons#john “ John Smith” “ [email_address] ” http://example.com/persons#gordon My learning resource Contribution A http://example.com/persons#john “ A book” “ John Smith” “ 2008-09-03” “ [email_address] ” http://example.com/persons#gordon My learning resource Contribution A Person B “ A book” “ John Smith” “ 2008-09-03” title contribution date entity name

“Follow your nose” http://xmlns.com/foaf/0.1/knows RDF Schema label comment range HTML http://example.com/persons#john http://example.com/persons#gordon FOAF specification http://xmlns.com/foaf/0.1/knows “ Knows” “ A person known by this person (indicating some level of reciprocated interaction between the parties)” foaf:Person

Linked Open Data More than 2 billion RDF triple

More than 2 billion RDF triple

Summary Semantic technologies allow for Large-scale interoperability (triples, AP-independent, follow-your-nose, linked data) Ontology support Reuse of existing standards Collaboration between standards bodies Reuse of existing tools Implementation in many environments From mobile or AJAX applications Through HTML (RDFa) and RSS To multi-billion-triples RDF stores

Semantic technologies allow for

Large-scale interoperability (triples, AP-independent, follow-your-nose, linked data)

Ontology support

Reuse of existing standards

Collaboration between standards bodies

Reuse of existing tools

Implementation in many environments

From mobile or AJAX applications

Through HTML (RDFa) and RSS

To multi-billion-triples RDF stores

Consequences for MLR drafts Use a “statement”-based model based on RDF Mature specification, large set of tools (parsers, reasoners, databases, etc) Ontology support (formal semantics) REMOVE structure attributes from MLR terms Provide new MLR templates for describing terms Properties, like “title”, “creator” Classes, like “Learning Resource”, “Event”, “Contribution”, “LangString”, “Classification” etc. REMOVE current application profile definition

Use a “statement”-based model based on RDF

Mature specification, large set of tools (parsers, reasoners, databases, etc)

Ontology support (formal semantics)

REMOVE structure attributes from MLR terms

Provide new MLR templates for describing terms

Properties, like “title”, “creator”

Classes, like “Learning Resource”, “Event”, “Contribution”, “LangString”, “Classification” etc.

REMOVE current application profile definition

Defining a property

Roadmap Part 1: Overview of MLR, how to make new parts, etc Part “B”: Basic RDF-based model, templates for properties, etc. Part “C”: Core elements. Allow reference to e.g. Dublin Core terms. Part “D”: Definition of Application Profiles, records, etc. Part “E”: MLR Core Application profile Part “F”: XML format for MLR application profiles

Part 1: Overview of MLR, how to make new parts, etc

Part “B”: Basic RDF-based model, templates for properties, etc.

Part “C”: Core elements. Allow reference to e.g. Dublin Core terms.

Part “D”: Definition of Application Profiles, records, etc.

Part “E”: MLR Core Application profile

Part “F”: XML format for MLR application profiles

Some issues Should MLR use RDF directly or add some constructs on top? Can properties from other specs be reused in MLR application profiles? (Dublin Core etc.) Should MLR parts reference external properties? Should MLR collaborate directly with e.g. DCMI in developing notion of application profile?

Should MLR use RDF directly or add some constructs on top?

Can properties from other specs be reused in MLR application profiles? (Dublin Core etc.)

Should MLR parts reference external properties?

Should MLR collaborate directly with e.g. DCMI in developing notion of application profile?