©CLRC/BITD/Keith G JefferyCERIF in CRISs & Scientific Repositories 20020823 1 Comparative Study of Metadata for Scientific Information: The Place of CERIF

©CLRC/BITD/Keith G Jeffery

CERIF in CRISs & Scientific Repositories 20020823 1

Comparative Study of Metadata for Scientific

Information: The Place of CERIF in CRISs

& Scientific RepositoriesKeith G Jeffery, Director, IT CLRC [email protected]

Andrei Lopatenko, Manchester University [email protected]

Anne Asserson, University of Bergen [email protected]



Series of Presentations

• CERIF: Past, Present and Future: An OverviewAnne Asserson, Andrei Lopatenko, Keith Jeffery

• CERIF - Information Retrieval of Research Information in a Distributed Heterogeneous EnvironmentAndrei Lopatenko, Keith Jeffery, Anne Asserson

• Comparative Study of Metadata for Scientific Information: The place of CERIF in CRISs and Scientific RepositoriesKeith Jeffery, Andrei Lopatenko, Anne Asserson



STRUCTURE

• DATA, INFORMATION & KNOWLEDGE

• DATA DELUGE, INFORMATION EXPLOSION AND METADATA

• USAGE OF METADATA IN CRISs• METADATA AND CERIF• CONCLUSIONS AND

RECOMMENDATIONS



DATA, INFORMATION & KNOWLEDGE

Data

• DATA : 06032002 – representation of observation of real

world– A lexical string of characters or

symbols



• INFORMATION : 06-03-2002– USA: 3rd June 2002, – UK: 6th March 2002

• Instead use:– Data : 20020603 – Metadata:

• yyyymmdd : a ‘format template’• Date : a type

– Structured data in context


Information



• KNOWLEDGE– Theories or hypotheses– Representation of:

• Facts (i.e. information)• Rules (when a, if b, then x, else y)

– Processing of them by inference:• Deduction, induction, abduction

– Commonly accepted justified belief


Knowledge



Start-Time Departureairport Flight Arrivalairport End-Time

0800 LHR BA123 FRA 1000

0900 LHR BA125 FRA 1100

1000 LHR BA127 FRA 1200

1100 LHR BA129 FRA 1300

Etc etc

1800 LHR BA137 FRA 2000


Knowledge: Facts



Start-Time Departureairport Flight Arrivalairport End-Time

0800 LHR BA123 FRA 1000

0900 LHR BA125 FRA 1100

1000 LHR BA127 FRA 1200

1100 LHR BA129 FRA 1300

Etc etc

1800 LHR BA137 FRA 2000

between 0800 and 1800 every hour, on the hour a BA flight leaves LHR for FRA

INDUCTION

(data mining)


Knowledge: Induction



Collecting Observed Facts

DATA


Putting it together


CERIF in CRISs & Scientific Repositories 20020823

10

Structuring in Context

DATA

INFORMATION


Putting it together



11

Inducing commonly accepted belief

DATA

INFORMATION

KNOWLEDGE


Putting it together



12

Value-Adding for Business Needs

DATA

INFORMATION

KNOWLEDGE

INSIGHT


Putting it together



13

STRUCTURE




RECOMMENDATIONS



14

DATA DELUGE, INFORMATION EXPLOSION AND METADATA

Technology Capacity

• Communications– 2.4Kb/s 20Gb/s in 30 years 2 * 106

• Online Storage– 1.2 Mb 40 Gb in 30 years 4 * 104

• Processor Speed increased even more

With acknowledgements to



15

• e-Science– Petabytes per year– Particle Physics– Space Science– Genomics

• e-Information– Terabytes per year– Eprints– Hyperlinked data– Hypermedia

• e-Learning• e-Business With acknowledgements to CLRC/BITD/PS


Application Areas



16

WWW(1989)


Technology Takeup



17

• Much of this data is inaccessible • Need to be able to

– Find relevant data as information– Understand it : syntax, semantics

– Understand any restrictions on its use

datarequired

METADATA


Data & Metadata



18

• Metadata is data about data

• Metadata to one application is data to another

Application1 Application2


Data & Metadata



19

data (document)

SCHEMA NAVIGATIONAL ASSOCIATIVE

how to

get it

constrain it

view to users


Three Kinds of Metadata



20

• intensional description of extensional instances– database:

• name • size• security authorisations

– attributes: • name• type • constraints

• formal logic relationship to data instances


Metadata Kinds: Schema



21

data (document)

SCHEMA NAVIGATIONAL ASSOCIATIVE

how to

get it

constrain it

view to users





22

• How to get to information resource direct– filename– DB name + navigational algorithm– DB name + predicate (query)– URL– URL + predicate (query)

• or any of the above via– web indexing system (eg AltaVista, ExCite…)– local indexing system bookmarks or proxy

server)


Metadata Kinds: Navigational



23

data (document)

NAVIGATIONAL

how to

get it

SCHEMA

constrain it

ASSOCIATIVE

view to users





24

• information for application assistance– catalog record (e.g. Dublin Core) - descriptive– content rating (e.g. PICS) - restrictive– security, privacy (cryptography, digital signatures)

- restrictive– information from dictionaries, thesauri,

hyperglossaries, domain ontologies - supportive

• no formal logic relationship to data instances


Metadata Kinds: Associative



25

STRUCTURE




RECOMMENDATIONS



26

• Data quality• Access• Understanding answers• Improving Queries• Interoperability with other CRISs• Interoperability with other Systems e.g.

– Local management information systems– Bibliographic systems– Scientific data systems

USAGE OF METADATA IN CRISs

Benefits



27

• All CRISs based on– DB SYSTEM– IR SYSTEM

• Have schema metadata• It may not be sufficient

– To ensure integrity– To provide rich enough

program interface– To ensue integrity in

foreign key - primary key linkage to associated CRISs or other systems

SCHEMA

constrain it


Schema Metadata



28

NAVIGATIONAL

how to

get it

• ‘Base CRISs’ may have navigational metadata– If provide raw

information only: no– If provide URLs to e.g.

publications, scientific datasets: yes

• ‘Meta-CRISs’ which act as catalogues or indexes to other CRISs do have navigational metadata


Navigational Metadata



29

• AdM– Associative

descriptive

• ArM– Associative

restrictive

• AsM– Associative

supportive

ASSOCIATIVE

view to users


Associative Metadata



30

• CRISs have AdM if– Provide summary record of >= 1

{<project> | <person> | <orgunit>} and point to detailed records

– The AdM provides machine-readable (syntax) and machine-understandable (semantics) information

ASSOCIATIVE

view to users


Associative descriptive Metadata



31

• CRISs have ArM if– Provide separate metadata record

with information on access rights, copyright, IPR, 3rd party liability disclaimer, pricing

– The ArM provides machine-readable (syntax) and machine-understandable (semantics) information

ASSOCIATIVE

view to users


Associative restrictive Metadata



32

• CRISs have AsM if– Provide >= 1 {dictionary |

hyperglossary | thesaurus | domain ontology}

– The AsM provides machine-readable (syntax) and machine-understandable (semantics) information and / or knowledge

ASSOCIATIVE

view to users


Associative supportive Metadata



33

USAGE OF METADATA IN CRISs Typical CRIS and

Metadata

Metadata for whole collection of base CRIS data records

Metadata for data record in base CRIS

Metadata within base CRIS

Data

schema navigational

associative

associative

Other data system



34

STRUCTURE




RECOMMENDATIONS



35

METADATA & CERIF

The CERIF Models

Metadata Model

Export Model

Full CRIS Model

CRIS C

CRIS B

CRIS A



36

• Metadata Model Designed in from the start– Schema and navigational metadata defined– AdM : e.g. to be used as catalog in ERGO– AsM : e.g. controlled lists of terms

• Also designed to assist evolution of further linkages– Flexible ‘articulated’ structure– Links (metadata within records) to e.g.

bibliographic, scientific datasets

METADATA & CERIF

The CERIF Metadata Model



37

• AdM : provide crosswalks from CERIF Metadata Standard to:– Dublin Core (at least formalised

version)– Other relevant standards as they

emerge

– Using RDF, XML-Schema– Coded in XML

METADATA & CERIF CERIF: What Next? :

Associative descriptive Metadata



38

• ArM: define metadata standard taking into account existing ones– Access rights– IPR, copyright, right to use– 3rd party liability disclaimer– Charges

• XrML ? (PARC, Palo Alto)


Associative restrictive Metadata



39

• AsM : take current work on formal domain ontologies and push further– Using pre-existing ontologies when

relevant– Providing crosswalks to related

ontologies

• DAML/OIL


Associative supportive Metadata



40

STRUCTURE




RECOMMENDATIONS



41

CONCLUSIONS & RECOMMENDATIONS

CERIF: Good Basis

• CERIF already provides a good metadata standard– Formally defined– Proper subset of export and full CRIS

models– Recognised by EU



42

• CERIF Metadata already provides to a large extent the facility for:– Data quality– Access– Understanding answers– Improving Queries– Interoperability with other CRISs– Interoperability with other Systems e.g.

• Local management information systems• Bibliographic systems• Scientific data systems

CONCLUSIONS &

RECOMMENDATIONS CERIF Metadata Provides



43

• But can do more– Improvements proposed earlier– Plenty of scope for ideas, enthusiasm

• euroCRIS CERIF Task Group• www.eurocris.org/cerif

CONCLUSIONS &

RECOMMENDATIONS More can be done



44

CONCLUSIONS &

RECOMMENDATIONS CERIF Metadata: Use It!

hieroglyphics

demotic

greek

CERIF METADATA

Is developed to assist:

Quality

Understanding (answers)

Precision (queries)

Interoperability

Of CRISsWith acknowledgements to the British Museum

Documents

©CLRC/BITD/Keith G JefferyCERIF in CRISs & Scientific Repositories 20020823 1 Comparative Study of Metadata for Scientific Information: The Place of CERIF