Transcript The PowerPoint Slides

Harvesting Metadata Using
OAI-PMH
Roy Tennant
California Digital Library
Outline
The Open Archives Initiative
OAI-PMH
The Harvesting Process
Harvesting Problems
Steps to a Fruitful Harvest
A Harvesting Service Model
The OAI Future
Open Archives Initiative
Aimed at making the large and growing number of
repositories of freely available digital content
interoperable
Protocol for Metadata Harvesting (OAI-PMH)
specifies how repositories can expose their metadata
for others to harvest
Over 800 repositories world-wide support the protocol
OAIster.org has indexed nearly 6 million items from
over 500 of those repositories
www.oaforum.org/tutorial/
OAI-PMH
Data providers (DP) — those with the stuff
Service providers (SP) — those who harvest
metadata and provide aggregation and search
services
Software for both DPs and SPs readily available
OAI-PMH verbs:
Identify
ListIdentifiers
ListMetadataFormats
ListSets
ListRecords
GetRecord
OAI Architecture
Source: Open Archives Forum Tutorial
Identify
Provides basic information about a
repository
ListMetadataFormats
Lists available metadata formats
ListIdentifiers
Lists all identifiers (or only those of the
optionally specified set)
Must include metadataPrefix attribute
ListSets
Lists available sets
Library of Congress ListSets response
ListRecords
Lists all records (or only those of the
optionally specified set)
Must include metadataPrefix attribute
GetRecord
Retrieves a specific record
Must include metadataPrefix and identifier attributes
The Harvesting Process
Identifying Sources
Selecting Sets
Harvesting
Metadata Processing
Indexing
Interface
A Harvesting Service Model
gita.grainger.uiuc.edu/registry/
errol.oclc.org
Selecting Sets
Review the response to the ListSets
verb
May be instructive to search the
collection in the native interface, if
possible
Look for descriptive pages on the site
being harvested
Harvesting
Many harvesting applications are available, I
will focus on:
Public Knowledge Project (PKP) Harvester
Virginia Tech Perl Harvester
Library software vendors increasingly offer
harvesting products (e.g., ExLibris’
MetaIndex)
Virginia Tech Perl Harvester
+-----------------------------------------+
| Harvester Sample Configurator
|
+-----------------------------------------+
| Version 1.1 :: July 2002
|
| Hussein Suleman <[email protected]>
|
| Digital Library Research Laboratory
|
| www.dlib.vt.edu :: Virginia Tech
|
------------------------------------------+
Defaults/previous values are in brackets - press <enter> to accept those
enter "&delete" to erase a default value
enter "&continue" to skip further questions and use all defaults
press <ctrl>-c to escape at any time (new values will be lost)
Press <enter> to continue
[ARCHIVES]
Add all the archives that should be harvested
Current list of archives:
No archives currently defined !
Select from: [A]dd
[D]one
Enter your choice [D] : a{return}
[ARCHIVE IDENTIFIER]
You need a unique name by which to refer to the archive you
will harvest metadata from
Examples: nsdl-380602, VTETD
Archive identifier [] : nsdl-380602{return}
Let’s Harvest!
Indexing
Pick your favorite database/indexing
software:
MySQL
SWISH-E
Whatever is lying around…
May need to specifically set up a method to
search across the entire record
May need different fields for indexing than for
display
Will need to deal with element collision
Interface
Software interface (API) for other
applications:
SRU/SRW?
MXG?
Arbitrary Web Services schema?
User interface:
What functions do you want your users to be able
to perform?
What kinds of displays do you want?
Harvesting Problems
Sets
Metadata Formats
Metadata Artifacts
Granularity
Metadata Variances
Sets
Records are harvested in clumps,
called “sets” created by DPs
No guidelines exist for defining sets
Examples:
Collection
Organizational structure
Format (but is a page image an image?
See example)
Metadata Formats
Only required format is simple Dublin
Core, although any format can be made
available in addition
Few DPs surface richer metadata
Simple DC is simply too simple!
Example (artifact vs. surrogate dates)
Metadata Artifacts
“unintended, unwanted aberrations”
Sample causes:
Idiosyncratic local practices
Anachronisms
HTML code
Examples:
Circa = string of dates for searching purposes
[electronic resource]
Granularity
Record Granularity: what is an “object”?
A book, or each individual page?
Examples: CDL, Univ. of Michigan
Metadata Granularity:
Multiple values in one field
Example: Univ. of Washington
Metadata Variances
Subject terminology differences
Disparities in recording the same
metadata
Example: date variances
Mapping oddities or mistakes
Examples: 1) format into description, 2)
description into subject
Steps to a Fruitful Harvest
Needs Assessment (it’s the user, stupid)
DP Identification and Communication
Metadata Capture
Metadata Analysis
Metadata Subsetting
Metadata Normalization
Metadata Enrichment
Indexing & Display
Interface (it’s still the user, stupid)
Needs Assessment
What are you trying to accomplish?
What will your users want to be able to do?
What metadata will you need, and what
procedures will you need to set up to enable
these activities?
Which repositories have what you want?
Is what they have (e.g., sets, metadata)
usable as is, or ?
DP Identification & Communication
Identification:
Use UIUC directory of DPs to identify potential
sources
Communication:
Not required to tell them you are harvesting, but
may help establish a good relationship
May want to request that they surface a richer
metadata format and/or provide a different set
Metadata Capture
Sample questions to answer:
Individual sets, or all?
Richer metadata formats available?
How frequently to reharvest?
Start from scratch each time or update?
Many software options
Metadata Analysis
Finding out what you have (and don’t
have)
Encoding practices
Gap analysis (e.g., missing fields, etc.)
Mistakes (e.g., mapping errors)
Software can help
Commercial software like Spotfire
In-house or open source software tools
Five elements are used 71% of the time
Source: 2002 Master’s Thesis, Jewel Hope Ward, UNC Chapel Hill
Metadata Subsetting
DP sets are unlikely to serve all SP
uses well
SPs will need the ability to subset
harvested metadata
Metadata Normalization
Normalizing: to reduce to a standard or
normal state
Prototype date normalization service
screen
Metadata Enrichment
Adding fields and/or qualifiers may be useful
or required, for example:
Metadata provider information
Geographic coverage
Subject terms mapped to a different thesaurus
Authority control record
The enrichment process may be the same
tool as the subsetting tool (i.e., find a cluster
of records and perform an action)
Indexing & Display
Selected fields may need to be mapped to
specific indexing and display elements
Particularly required if harvesting different
metadata formats
But also needs to be done with multiple,
conflicting fields:
<date>1863.</date>
<date>[2001 or 2002.]</date>
<identifier>SHS 1,679</identifier>
<identifier>http://content.lib.washington.edu/cgi-bin/htmlview.exe?CISOROOT=/loc&CIS
<identifier>http://content.lib.washington.edu/loc/image/1679.jpg</identifier>
A Harvesting Service Model
The OAI Future
Further protocol development
Services layered on top of OAI-PMH
Shared software tools
Best practices for both DPs and SPs
oai-best.comm.nsdl.org