Bibliographic metadata (including citation) Tuesday 7 th July 2009 AMG 2 nd workshop, University of Leicester , Leicester www.bath.ac.uk UKOLN is supported by: Alexey Strelnikov Research Officer UKOLN Contributions from Emma Tonkin

Agenda Introduction What and why Use cases Key points Issues Recommendations

Introduction

What and why

Use cases

Key points

Issues

Recommendations

Introduction Metadata extraction is the process of describing extrinsic and intrinsic qualities of a resource

Metadata extraction is the process of describing extrinsic and intrinsic qualities of a resource

Bibliographic metadata Bibliographic metadata is a particular case of metadata extraction. For example: Title Authors Emails Citations

Bibliographic metadata is a particular case of metadata extraction.

For example:

Title

Authors

Emails

Citations

What and why General metadata extraction – tends to involve machine learning Citation and reference analysis – usually involves regular expressions Might involve visual structure analysis and text mining

General metadata extraction – tends to involve machine learning

Citation and reference analysis – usually involves regular expressions

Might involve visual structure analysis and text mining

What and why (2) In order to improve long/boring manual operations with metadata: Generation metadata on document deposit Revision of metadata Comparison and aggregation <Put your own operation here>

In order to improve long/boring manual operations with metadata:

Generation metadata on document deposit

Revision of metadata

Comparison and aggregation

<Put your own operation here>

What and why (3) Automatic extraction can make a system more robust (in addition to existing approaches) It is not a drop-in replacement for manual creation, but semi-automated feature extraction can make for better metadata quality overall

Automatic extraction can make a system more robust (in addition to existing approaches)

It is not a drop-in replacement for manual creation, but semi-automated feature extraction can make for better metadata quality overall

Use case (1) Dominik – is a researcher, publishing his new paper Instead of fully manual deposit (typing in all values) he makes use of system suggestions, which make the process faster and simpler

Dominik – is a researcher, publishing his new paper

Instead of fully manual deposit (typing in all values) he makes use of system suggestions, which make the process faster and simpler

Use case (2) Fiona – is a researcher, assessing impact made by her paper How many citations of my work? Network of citations (existing system: Google scholar, citeseer.net...)

Fiona – is a researcher, assessing impact made by her paper

How many citations of my work?

Network of citations (existing system: Google scholar, citeseer.net...)

Use case (3) Bob – is a repository manager, checking inconsistency in the repository's metadata Make use of system recommendations, and a generated value confidence level Easier to find invalid or obsolete metadata values

Bob – is a repository manager, checking inconsistency in the repository's metadata

Make use of system recommendations, and a generated value confidence level

Easier to find invalid or obsolete metadata values

Use case (4) Edward – is an application profile/standard curator, checking inter-repository metadata Have application profile, but no feedback on how it is followed Consistent errors: Not filled Systematically wrong value (might be related to research field, environment) Comparison & aggregation report

Edward – is an application profile/standard curator, checking inter-repository metadata

Have application profile, but no feedback on how it is followed

Consistent errors:

Not filled

Systematically wrong value (might be related to research field, environment)

Comparison & aggregation report

Summary for use cases All approaches have a manual analogue Automated metadata extraction would be an improvement, but not replacement Service is invisible , it just makes suggestions: for example – 'the metadata field “title” should be “Some name”'

All approaches have a manual analogue

Automated metadata extraction would be an improvement, but not replacement

Service is invisible , it just makes suggestions: for example – 'the metadata field “title” should be “Some name”'

Key points Standards - involved in the workflow make a big impact “The nice thing about standards is that there are so many of them to choose from” Andrew S. Tanenbaum Tools – existing applications to extract metadata

Standards - involved in the workflow make a big impact

“The nice thing about standards is that there are so many of them to choose from” Andrew S. Tanenbaum

Tools – existing applications to extract metadata

Standards Should consider a number of standards for representation, format, as well as languages and locales Document encoding Metadata encoding Locale specifics Citation formats

Should consider a number of standards for representation, format, as well as languages and locales

Document encoding

Metadata encoding

Locale specifics

Citation formats

Document encoding Important because this may impact correct reading of a resource Document formats: PDF, Doc, PPT, etc. Font encoding: UTF, locale specific

Document encoding

Important because this may impact correct reading of a resource

Document formats:

PDF, Doc, PPT, etc.

Font encoding:

UTF, locale specific

Metadata encoding This has a direct impact on the result's usability in a given context Examples of metadata standards: OAI-DC SWAP LOM OAI-ORE MARC

Metadata encoding

This has a direct impact on the result's usability in a given context

Examples of metadata standards:

OAI-DC

SWAP

LOM

OAI-ORE

MARC

Locale specifics Country and culture specific formats of text elements For example: Right-to-left languages Date format: dd/mm/yyyy mm/dd/yyyy

Locale specifics

Country and culture specific formats of text elements

For example:

Right-to-left languages

Date format:

dd/mm/yyyy

mm/dd/yyyy

Citation and reference formats There exist many citation/reference formats, different standards exist for most research fields For example: APA – social sciences MLA – literature and the arts AMA - biology Turabian – multi-field Chicago standard – publications Harvard, Numerical, MHRA - multi-field

Citation and reference formats

There exist many citation/reference formats, different standards exist for most research fields

For example:

APA – social sciences

MLA – literature and the arts

AMA - biology

Turabian – multi-field

Chicago standard – publications

Harvard, Numerical, MHRA - multi-field

Tools Automated metadata extraction is a workflow, which involves several interconnected software systems Helps to overcome standards heterogeneity

Automated metadata extraction is a workflow, which involves several interconnected software systems

Helps to overcome standards heterogeneity

Examples of Tools Examples of existing tools: DC-dot (variety of doc/web formats -> DC metadata) DepositPlait (var. format metadata -> metadata repository) DataFountains (var. format->metadata) paperBase (prototype concentrating on eprint documents)

Examples of existing tools:

DC-dot (variety of doc/web formats -> DC metadata)

DepositPlait (var. format metadata -> metadata repository)

DataFountains (var. format->metadata)

paperBase (prototype concentrating on eprint documents)

Issues Full-text resource availability Readability of the text Legal issues Engineering constraints - machine suggestions might be imperfect Language & localization - need to retrain system for the other locale

Full-text resource availability

Readability of the text

Legal issues

Engineering constraints - machine suggestions might be imperfect

Language & localization - need to retrain system for the other locale

Recommendations A robust system that is easy to retrain, customizable input & outputs plugins A potential gain: Simplify (re)extraction of metadata, faster repository operations, validation Making use of confidence level assigned to the metadata field A potential gain: Identifying possibly incorrect metadata records

A robust system that is easy to retrain, customizable input & outputs plugins

A potential gain:

Simplify (re)extraction of metadata, faster repository operations, validation

Making use of confidence level assigned to the metadata field

A potential gain:

Identifying possibly incorrect metadata records

Recommendations (2) Make full-text document available to the system A potential gain: Periodical re-exploration of the resource and updating the metadata Investigate the problem of analysing citation A potential gain: Assess level of similarity between papers Classify paper nature

Make full-text document available to the system

A potential gain:

Periodical re-exploration of the resource and updating the metadata

Investigate the problem of analysing citation

A potential gain:

Assess level of similarity between papers

Classify paper nature

Q&A Thank you for your attention

Thank you for your attention