MEOW: Metadata
Enhancement and
OAI Workshop
Metadata Enhancement,
Sharing and OAI-PMH
What does this record describe?
identifier:
http://name.university.edu/IC-FISH3ICX0802]1004_112
publisher:
Museum of Zoology, Fish Field Notes
format:
jpeg
rights:
These pages may be freely searched and
displayed. Permission must be received for subsequent
distribution in print or electronically.
type: image
subject:
1926-05-18; 1926; 0812; 18; Trib. to Sixteen Cr.
Trib. Pine River, Manistee R.; JAM26-460; 05; 1926/05/18;
R10W; S26; S27; T21N
language:
UND
source:
Michigan 1926 Metzelaar, 1926--1926;
description: Flora and Fauna of the Great Lakes Region
Creating descriptive metadata
• “Digital library” systems
– ContentDM
– ExLibris Digitool
– Greenstone
• Library catalogs
• Spreadsheets & databases
• XML
Some Emerging Trends in
Metadata Creation
•
•
•
•
“Schema-agnostic” metadata
Metadata that is both shareable and re-purposeable
Harvestable metadata (OAI-PMH)
“Non-exclusive”/”cross-cultural” metadata--i.e. it’s
okay to combine standards from different metadata
communities--e.g. MARC and CCO, DACS and
AACR, DACS and CCO, EAD and CDWA Lite, etc.
• Importance of authorities--and difficulties in “bringing
along” the power of authorities with shareable
metadata records
• The need for practical, economically feasible
approaches to metadata creation
Building “Good digital collections”
• Interoperable – with the
important goal of crosscollection searching
• Persistent – reliably accessible
• Re-usable – repositories of
digital objects that can be used
for multiple purposes
Issues
• Many significant digital collections lack
standardized metadata
• Such inconsistent metadata causes
access problems, especially when
collections are aggregated
• Enhancing metadata for existing
collections is often difficult to undertake
because of a lack of tools to automate the
process
Issues
• IMLS grant: “Enhancing and Remediating Legacy
Metadata for Effective Resource Sharing.”
• “Metadata today is likely to be created by people without
any metadata training … metadata records are also
created by automated means … unsurprisingly, the
metadata resulting form these processes varies strikingly
in quality and often does not play well together ..
Nevertheless, many metadata aggregators use this
metadata to build services for end users, thus
contributing to criticisms that metadata is of limited
value, can’t be trusted or that it’s demonstrably so
incomplete as to be worthless”--Diane Hillman.
Issues
• Useful services depend on good metadata, but
most metadata is not very good
• Human created metadata is expensive
• Automated crawling strategies are limited by:
– Accessibility barriers (rights issues, technical
issues)
– Variable results with crawling technologies for
non-text
• Best metadata does not rely solely on
information contained within the resource itself
– Ex.: Controlled vocabularies, descriptions, links
What is shareable metadata?
- Is quality metadata
- Promotes search interoperability
“the ability to perform a search over diverse sets of metadata
records and obtain meaningful results.”
- Is human understandable outside of its local
context
- Is useful outside of its local context
- Is machine processable
Shareable metadata defined
• Promotes search interoperability - “the ability
to perform a search over diverse sets of
metadata records and obtain meaningful
results” (Priscilla Caplan)
• Is human understandable outside of its local
context
• Is useful outside of its local context
• Preferably is machine processable
Why share metadata anyway?
• Benefits to users
– Single search of a variety of digital resources
– Aggregation of subject-specific resources
– Higher quality resources
• Benefits to institutions
– Increased user access to collection by allowing
metadata to appear in other places
– Exposure to broader audience, new users
– Surfacing rare, unknown, or scattered collections
Different shapes and sizes…
• Range of different aggregations:
– focused subject area v. comprehensive
– specialized audience v. general audience
• Range of different displays
Metadata as a view of the
resource
• There is no monolithic, one-size-fits-all
metadata record
• Metadata for the same thing is different
depending on use and audience
• Affected by format, content, and context
• Descriptive vs. administrative vs.
technical, etc. data
Metadata is a view of a
resource
• No monolithic, one-size-fits-all metadata
record
• The view might be different depending on
use and audience as well as format,
content, and context
• Content standard is a view
• Metadata standard is a view
• Vocabulary used is a view
Choice of metadata format(s) as
a view
• Many factors affect choice of metadata
formats
• Many different formats may all be
appropriate for a single item
• High-quality metadata in a format not
common in your community of practice is
not shareable
Focus of description as a view
• Link between records for analog and
digital
• Hierarchical record with all versions
• Physical with link to digital
• All versions in flat record
• Content but not carrier
Finding the right balance
• Metadata providers know the materials
– Document encoding schemes and controlled
vocabularies
– Document practices
– Ensure record validity
• Aggregators have the processing power
–
–
–
–
Format conversion
Reconcile known vocabularies
Normalize data
Batch metadata enhancement
6 Cs and lots of Ss of shareable
metadata
Content
Consistency
Coherence
Context
Communication
Conformance
Metadata standards
Vocabulary and encoding standards
Descriptive content standards
Technical standards
Content
•
•
•
•
•
•
Choose appropriate vocabularies
Choose appropriate granularity
Make it obvious what to display
Make it obvious what to index
Exclude unnecessary “filler”
Make it clear what links point to
Consistency
• Records in a set should all reflect the
same practice
– Fields used
– Vocabularies
– Syntax encoding schemes
• Allows aggregators to apply same
enhancement logic to an entire group of
records
Coherence
• Metadata format chosen makes sense for
materials and managing institution
– Not just Dublin Core!
• Record should be self-explanatory
• Values must appear in appropriate elements
• Repeat fields instead of “packing” to explicitly
indicate where one value ends and another
begins
Context
• Include information not used locally
• Exclude information only used locally
• Current safe assumptions
– Users discover material through shared
record
– User then delivered to your environment for
full context
• Context driven by intended use
Communication
• Method for creating shared records
• Vocabularies and content standards used
in shared records
• Record updating practices and schedules
• Accrual practices and schedules
• Existence of analytical or supplementary
materials
• Provenance of materials
Conformance to Standards
• Metadata standards (and not just DC)
• Vocabulary and encoding standards
• Descriptive content standards (AACR2,
CCO, DACS)
• Technical standards (XML, Character
encoding, etc)
Before you share…
• Check your metadata
– Appropriate view?
– Consistent?
– Context provided?
– Does the aggregator have what they need?
– Documented?
Can a stranger tell you what the record
describes?
The reality of sharing metadata
• We can no longer afford to only think
about our local users
• Creating shareable metadata will require
more work on your part
• Creating shareable metadata will require
our vendors to support (more) standards
• Creating shareable metadata is no longer
an option, it’s a requirement
So where does RDA fit in?
• RDA is a content standard
• MODS is a metadata standard
• RDA is closely aligned with MARC and MODS
• Useful to have a RDA – MODS examples particularly as MODS is
shifting away from MARC
• Although because of its origins in the library world RDA
presupposes MARC as a vehicle for cataloging records, this
emergent cataloging code could also be used with MODS, Dublin
Core, or other metadata schemas.
• Lack of engagement with the MODS community beyond the Library
of Congress
DLF Aquifer Guidelines
• Does NOT recommend any one content
standard over another
“Choice and format of titles should be governed by a content standard
such as the Anglo-American Cataloging Rules, 2nd edition
(AACR2), Cataloguing Cultural Objects (CCO), or Describing
Archives: A Content Standard (DACS). Details such as
capitalization, choosing among the forms of titles presented on an
item, and use of abbreviations should be determined based on the
rules in a content standard. One standard should be chosen and
used consistently for all records in an OAI set.”
Metadata aggregators
• CIC Metadata Portal
http://cicharvest.grainger.uiuc.edu/
– Records and digital resources shared by consortium of
institutions, provided for
– Educators, researchers, and general public
Benefits:
Single comprehensive search of
multiple collections and a variety of disciplines
Metadata aggregators
• National Science Digital Library
http://nsdl.org
- Online resources and records pertaining to science & math
education and research, vetted for inclusion,
provided for
- Educators, researchers, policy makers, and the general public
Benefits:
Single portal serving a range of resources on a
specialized topic to a diverse audience
OAI-PMH
• Open Archives Initiative-Protocol for Metadata
Harvesting
• OAI-PMH defines a mechanism for harvesting
records containing metadata from repositories.
• The OAI-PMH gives a simple technical option for
data providers to make their metadata available to
services, based on the open standards HTTP
(Hypertext Transport Protocol) and XML (Extensible
Markup Language).
• The metadata that is harvested may be in any format
that is agreed by a community (or by any discrete set
of data and service providers), although unqualified
Dublin Core is specified to provide a basic level of
interoperability
OAI-PMH
• Metadata from many sources can be gathered
together in one database, and services can be
provided based on this centrally harvested, or
"aggregated" data.
• Data Provider: a Data Provider maintains one or
more repositories (web servers) that support the
OAI-PMH as a means of exposing metadata.
• Service Provider: a Service Provider issues OAIPMH requests to data providers and uses the
metadata as a basis for building value-added
services. A Service Provider in this manner is
"harvesting" the metadata exposed by Data
Providers
OAI-PMH Structure
• Intentionally designed to be simple
• Data providers
– Have metadata they want to share
– “Expose” their metadata to be harvested
• Service providers
– Harvest metadata from data providers
– Provide searching of harvested metadata
from multiple sources
– Can also provide other value-added services
Data Providers
• Set up a server that responds to
harvesting requests
• Required to expose metadata in simple
Dublin Core (DC) format
• Can also expose metadata in any other
format expressible with an XML schema
Service Providers
• Harvest and store metadata
• Generally provide search/browse access
to this metadata
• Can be general or domain-specific
• Can choose to collect metadata in formats
other than DC
• Generally link out to holding institutions for
access to digital content
• OAIster is a good example
Pop Quiz
What is the OAI-PMH?
(Select one answer)
(a) The OAI-PMH is a protocol for sharing
metadata.
(b) The OAI-PMH is a low-barrier protocol for
searching across repositories and retrieving
resources from them.
Multiple Service Providers can harvest from
multiple Data Providers.
Basic functioning of OAI-PMH
Finding the right balance
• Metadata providers know the materials
– Document encoding schemes and controlled
vocabularies
– Document practices
– Ensure record validity
• Aggregators have the processing power
–
–
–
–
Format conversion
Reconcile known vocabularies
Normalize data
Batch metadata enhancement
Why share metadata?
• Benefits to users
– One-stop searching
– Aggregation of subject-specific resources
• Benefits to institutions
– Increased exposure for collections
– Broader user base
– Bringing together of distributed collections
Don’t expect users will know about your collection
and remember to visit it.
Why share metadata with OAI?
• “Low barrier” protocol
• Shares metadata only, not content,
simplifying rights issues
• Same effort on your part to share with one
or a hundred service providers (basically)
• Wide adoption in the cultural heritage
sector
• Quickly eclipsed older methods such as
Z39.50
Common Problems with Metadata in
Aggregation
• Consistency
• Sufficiency
• Compatibility
Consistency problems
•
•
•
•
Appearance of data
Application of format
Granularity of records
Vocabulary usage
Result:
Service Provider must normalize data
(if can determine what “normal” is)
Sufficiency problems
• Too little info for understanding what
resource is, especially outside of local
context
Result:
Users don’t know whether a resource is
relevant or not
Compatibility problems
Information in records is
– Erroneous
– Unnecessary
– Incompatible
Result:
Interferes with harvesting and indexing
Common content mistakes
• No indication of vocabulary used
– Names
• LCNAF: Michelangelo Buonarroti, 1475-1564
• ULAN: Buonarroti, Michelangelo
– Places
• LCSH: Jakarta (Indonesia)
• TGN: Jakarta
– Subjects
• LCSH: Neo-impressionism (Art)
• AAT: Pointillism
• Shared record for a single page in a book
• Link goes to search interface rather than item being
described
• “Unknown” or “N/A” in metadata record
Common context mistakes
• Leaving out information that applies to an
entire collection (“On a horse”)
• Location information lacking parent
institution
• Geographic information lacking higherlevel jurisdiction
• Inclusion of administrative metadata
1. Duplication problem
Different
collections
describing
same
source:
Duplication problems
• Duplicates
– can be identical records,
– can describe the same source, but with
different metadata, and
– can describe the same source but with links to
different or slightly different location identifiers
(e.g. index page vs. splash page).
2. Record quality
could be different
No date.
No format, type,
grade level, language,
rights, etc.
Description might be
based on the then
table of contents.
No date.
Description might be
based on the then
cover page.
Description is no
longer relevant.
Format missed
images.
No grade level, rights.
No date.
Description is more
general and can last
longer. +
Recorded grade level,
format, type +
No language, rights.
3. Incomplete data causes low recall
Completeness Search &
Display
No
FORMAT
information
Completeness Search & Display (cont.)
•Collections which did not provide FORMAT information are
excluded from being searched at advanced searches
Completeness Search & Display (cont.)
• Collections did not use EDUCATIONLEVEL or AUDIENCE element would
not be in the pool when a user searches by Grade Level.
Expectations for discovery systems
are rising
• Growth of cutting-edge systems outside of
libraries affecting user expectations
• Higher user expectations are a good thing!
• Many expected functions will be easier
with robust structured metadata
– Genre access
– Faceted browsing
– Limiting scope by time, place, etc.
Libraries are having trouble
meeting those expectations
• Non-textual resources are more difficult to
search
• Legacy metadata isn’t always structured in ways
that allow high-level services
• Legacy metadata doesn’t always include enough
information to allow high-level services
• Creating new metadata needed to provide highlevel services is prohibitively expensive
Enter automated enhancement
methods
• Much research has been done
• Little of it has been put into production systems
in library metadata creation environments
– Still requires human intervention
– Fear of human skills becoming devalued
• Metadata aggregators, out of necessity, are
among the first implementers
• Automatic enhancement holds great promise for
standardizing and streamlining metadata
creation and aggregation activities
Why Enhance Metadata at All?
• Four categories of problems limit metadata
usefulness:
– Missing data: elements not present
– Incorrect data: values not conforming to
proper usage
– Confusing data: embedded html tags,
improper separation of multiple elements, etc.
– Insufficient data: no indication of controlled
vocabularies, formats, etc.
Solving the problems -Enriching and enhancing
harvested records
record
Aggregation
Metadata
Repository
record
Enriched
record
Aggregation
It is possible that these problems be
eliminated to certain level through a
process called ‘aggregation’ in a metadata
repository.
The notion behind this process is that a
metadata record, “a series of statements
about resources,” can be aggregated to
build a more complete profile of a
resource.
Provider A
OAI
Provider A
orig metadata
<dc:title>
<dc:identifier>
<dc:creator>
<dc:type>
ENC
Enhancement
Service
OAI
iVia
Enhancement
Service
OAI
NSDL Metadata Repository
ENC enhancements
<dct:audience>
<dct:educationLevel>
NSDL Safe
Transforms
Safe xform enhancements
<dc:identifier URI>
<dc:type DCMIType>
iVia enhancements
<dc:subject GEM>
<dc:subject LCSH>
<dc:subject LCC>
NSDL normalized/augmented
<dc:title source=A>
<dc:creator source=A>
<dc:identifier URI source=MR>
<dc:type DCMIType source=MR>
<dc:subject GEM source=iVia>
<dc:subject LCSH source=iVia>
<dc:subject LCC source=iVia>
<dct:audience source=ENC>
<dct:educationLevel source=ENC>
OAI
Incorrect element mapping
OPTIONS mapped to SUBJECT, missing all KEYWORDS
missed?!
missing keywords
AUTHOR mapped to DESCRIPTION, no CREATOR
Only physical description, no content description
Inappropriate mapping
CLASSIFICATION mapped to SUBJECT and missed all the KEYWORDs.
Solving the problems -Correcting the errors
• Checking and testing crosswalks!!!
• Re-harvesting
• Training how to use OAI tools
Value space that should follow standardized rules:
Examples from values associated with DATE element
•
•
•
•
•
•
•
•
1979
2000-03
2000-03-01
2001-0102T21:48.00Z
200003
C1999, 2000
January, 1919
May, 1919
•
•
•
•
•
•
•
1987, c2000
?1999
1952 (issued)
(1982)
1930?]
1823-1845
Between 1680 and
1896?
dc.date: Recommended best practice for
encoding the date value
is defined in a profile of ISO 8601
[W3CDTF] and includes
(among others) dates of the form YYYY-
Value space that should apply standard controlled
vocabularies: Examples from values associated with
LANGUAGE element
•
•
•
•
•
en
eng
en-GB
en-US
English
•
•
•
•
•
engfre
new
Korean
Deutsch
German
LOCLANGUAGE::
German
dc.language: Recommended best practice is to use RFC 3066
[RFC3066] which, in conjunction with ISO639 [ISO639]),
defines two- and three-letter primary language tags with
optional subtags.
Two efforts to promote shareable
metadata
• Best Practices for OAI Data Provider
Implementations and Shareable Metadata
– http://webservices.itcs.umich.edu/mediawiki/oaibp/?T
ableOfContents
• Digital Library Federation / Aquifer
Implementation Guidelines for Shareable MODS
Records
– http://www.diglib.org/aquifer/dlfmodsimplementationg
uidelines_finalnov2006.pdf
Everyone could use better metadata!
?
Metadata Enhancement
•
•
•
•
Clustering and classification
Automated name authority control
Date normalization
Thumbnail generation and creating
actionable URLs
Clustering and classification
• UC-Irvine and Michigan
• Evaluate topic/subject-based metadata
enhancement
• Clustering: “learning the topics” (preprocess)
• Classification: using the learned topics to
determine topics in records and records in
topics
Topic Model
• State-of-the-art statistical algorithm
• Learns a set of topics or subjects covered
by a collection of text records
• Works by finding patterns of co-occurring
words
• Determines the mix of topics associated
with each record
Clustering and classification
• Mix of scientific repositories
• Average of 75 words per record
• Used words from <title>, <description>,
<subject> for clustering
• Only kept words that occurred in more
than 10 records
• Result: a final vocabulary of 90,000 words
• Cluster words into topics: ended up with
500 topics
Clustering and classification
• 500 topics too many to look at
• Needed to organize topics under broad
topical categories
– Cluster the clusters (automatic)
– Use pre-defined categories
• Classify group of keywords (manual + automatic)
• Create hierarchy by hand (manual)
clustering is
learning the
topics
vocabulary
Cluster
OAI
records
preprocess
topic
model
(cluster/learn)
topics
vocab
-ulary
Classify
preprocess
topic
model
(classify)
1. topics in records
2. records in topics
OAI
records
classification
is using the
learned topics
Preprocessing Example
•
<ID=oai:CiteSeerPSU:44072>
•
<title>Reinforcement Learning: A Survey
•
<description>This paper surveys the field
of reinforcement learning from a computerscience perspective. It is written to be
accessible to researchers familiar with
machine learning. Both the historical basis
of the field and a broad selection of current
work are summarized. Reinforcement
learning is the problem faced by an agent
that learns behavior through trial-and-error
interactions with a dynamic environment.
The work described here has a
resemblance to work in psychology, but
differs considerably in the details and in the
use of the word "reinforcement." …
•
<subject>Leslie Pack Kaelbling, Michael
Littman, Andrew Moore. Reinforcement
Learning: A Survey
vocab
-ulary
<ID=oai:CiteSeerPSU:44072>
reinforcement learning survey
preprocess
survey field reinforcement learning
computer science perspective written
accessible researcher familiar machine
learning historical basis field broad
selection current summarized
reinforcement learning faced agent
learn behavior trial error interaction
dynamic environment resemblance
psychology differ considerably detail
word reinforcement …
leslie pack kaelbling littman andrew
moore reinforcement learning survey
Example Topics (1)
Words in Topic
Topic Label
gene sequence genes sequences cdna region amino_acid clones
encoding cloned coding dna genomic cloning clone
gene sequencing
social cultural political culture conflict identity society economic
context gender contemporary politic world examines tradition
sociology institution ethic discourse
cultural identity
general_relativity gravity gravitational solution black_hole tensor
einstein horizon spacetime equation field metric vacuum scalar
matter energy relativity
relativity
house garden houses dwelling housing homes terrace estate
home building architecture residence homestead residences road
cottage domestic fences lawn historic
domestic
architecture
Example Topics (2)
Words in Topic
Usefulness
large small size larger smaller sizes scale sized
largest
Reasonable but unusable
foi para pacientes por foram dos doen resultados
grupo das tratamento entre
Topic about patient treatment, in
Spanish
building street visible santa_ana view avenue
public_library front orange corner
Not usable: mix of concept words and
specific geographic location words
Topics Assigned to a Record
Metadata Record
Topic Labels
(% words assigned)
Aggregating sets of judgments: two impossibility
results compared.
(C. List and P. Pettit)
game theory (21%)
argument (12%)
criteria (7%)
May's celebrated theorem (1952) shows that, if a
group of individuals wants to make a choice
between two alternatives (say x and y), then
majority voting is the unique decision procedure
satisfying a set of attractive minimal conditions ...
Clustering and classification
• Selected useful topics
– [ t482 ] labor worker employment wage
market labour job unemployment wages
earning panel find evidence individual
participation skill
– [ t372 ] firm investment capital
productivity innovation industry sector
economic industrial foreign industries
corporate ownership technological
companies evidence
Clustering and classification
• Selected less useful topics
– [ t255 ] journal author chapter vol notes
editor publication issue special bibliography
reader references appendix literature
submitted topic
– [ t013 ] university department mail edu
institute science california email usa
computer york fax college press center
address
Broad Topical Categories (BTCs)
• By clustering the clusters
– Worked well
– Can choose desired number of BTCs
• By classifying groups of keywords
– Worked well too
• Then review and manually edit
– Include or exclude any subtopic
Clustering and classification:
Further evaluation
• Need to test non-English and cultural
heritage repositories
• Need usability testing
• “On the horse” problem more prevalent
• When to re-cluster?
Automated Name Authority Control
(ANAC)
• Johns Hopkins University: research only;
never implemented
• 29,000 Levy sheet music records
• 13,764 unique names
ANAC
•
•
•
•
The evidence used to determine the probability of a match between a name
to an LC record is a set of Boolean tests involving the name, the Levy
metadata associated with that name, and the LC record.
The following fields were used by ANAC:
Levy record:
– Given name: often abbreviated
– Middle names: often abbreviated
– Family name
– Modifiers: titles and suffixes
– Date: publication year
– Location: publication location (city)
LC record:
– Given name: includes abbreviations
– Middle names: includes abbreviations
– Family name
– Modifiers: titles and suffixes
– Birth: year of birth
– Death: year of death
– Context: miscellaneous data
ANAC
• The tests used are: first name equality and
consistency, middle name equality and
consistency, music terms present in LC
record context, name modifier consistency,
Levy sheet music publication consistent
with LC author birth and death, and Levy
record publication location in LC record
context
ANAC
• In order to train the system, the Cataloging Department at the
Sheridan Libraries generated ground truth data.
• For each name in 2,000 randomly selected Levy metadata records,
catalogers recorded the authorized form of the name when a
matching authority record was available.
• The entire process required 311 hours (approximately seven
minutes per name).
• The human catalogers used much the same type of evidence as
ANAC in establishing matches. Catalogers examined name
similarity; compared publication dates from the Levy records to birth
and death dates in the authority records; and examined authority
record note fields for musical terms.
• In addition, the catalogers often searched for bibliographic records
of other editions of a particular title to determine the authoritative
name assigned to the subject.
ANAC
• Overall, ANAC was successful 58% of the time. When a
name had an LC record, ANAC was successful 77% of
the time, but when an LC record did not exist for a name
ANAC was successful only 12% of them time. The
reason for this discrepancy is that ANAC cannot learn
whether or not a name has been added to the LC
authority file.
• It took ANAC five hours and forty-five minutes to classify
the 2,673 (2,841 minus 168) names, or about eight
seconds per name. The database-bound process of
retrieving the candidate set of MARC records given a
family name consumed most of this time.
ANAC
• Matching very dependent on contextual data
• Machine matching much faster than manual (8 sec. vs. 7
min.)
• Performance reasonable even with dirty metadata.
• Machine matching could enhance manual work
• Combination of machine processing and human
intervention produced best results
• Approach could be tweaked by comparing names to
multiple authority files or domain specific databases
• ANAC not a generalizable tool, but there are others
Date Normalization
• How to make “ca. 1880” a machine-readable
date but not a 19-2 baseball score
• California Digital Library
• Created for American West project, so
sidestepped issue of B.C.E. date normalization
• Uses <date> element
• If no <date>, searches for date-like strings in
<title>, then <description>
• Currently normalizes to YYYY only, not MM or
DD (will add later)
<date>: Encoding Variances
•
•
•
•
•
•
•
•
•
•
•
1991-10-01
ca. 1920.
(ca). 1920)
2001.06.08 by CAD
Unknown
ca. June 19, 1901.
(ca). June 19, 1901)
[2001 or 2002.]
1853.
c1875.
c1908 November 19
•
•
•
•
•
•
•
•
•
c1905
1929 June 6
[between 1904 and 1908]
[ca. 1967]
1918 ?
[1919 ?]
191-?
1870 December, c1871
1920, 1921, 1922, 1923,
1924, 1925, 1926, 1927,
1928, 1929
Normalization Process
• Extract dates
• Standardize approximate dates, e.g. ca.
1902 = 1902~
– CDL uses +/- 5 years e.g. 1902~ = 1897-1907
• Normalize Dates
• Populate date.found or date.guess
• Create era, decade and year tokens
Recognizing Unknowns
• Recognizes wide range of expression of
date unknownness in <date> element, e.g.
unknown, unkn, unavail, n.d., nd, undated,
no date, not indicated
• Looks for date-like strings to normalize in
<title>, then <description> when <date>
element contains one of these expressions
• If no date, look for Civil War, Renaissance,
dates of reigns of sovereigns, etc.
Known Issues
• Distinguishing “c” for circa from “c” for
copyright (currently interprets as later)
• Getting tripped up by baseball scores in
non-<date> elements
• Getting tripped up by 4-digit item
identifiers
Thumbnail generation and creating
actionable URLs
• Thumbnail: “A miniature representation of
a page or image that is used to identify a
file by its content”—PC Magazine
• Need coordination between metadata
harvesting and thumbnail grabbing
• Digital libraries need digital objects
• <identifier> element in DC is a problem:
hard to identify link to actual object
Thumbnail generation and creating
actionable URLs
• “Users should be able to download, manipulate,
morph, annotate, cross-search, and repurpose
digital library content”
• Find best possible link, find best possible image,
build thumbnail: registry of links; data providers
retain the responsibility of maintaining the
authoritative version of their resource
• Need to find a way to express intellectual
property rights related to manipulation of objects
• Try to get providers to supply better metadata,
but in mean time use what we’ve got
Characteristics of quality metadata:
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Completeness. -- choosing an element set allowing the resources in
question to be described as completely as is economically feasible, and -applying that element set as completely as possible.
Accuracy. -- the metadata being correct and factual, and conforming to
syntax of the element set in use.
Provenance. Here provenance refers to the provision of information about
the expertise of the person(s) creating the original metadata, and its
transformation history.
Conformance to expectations. Metadata elements, use of controlled
vocabularies, and robustness should match the expectations of a particular
community.
Logical consistency and coherence. -- element usage matching standard
definitions, and consistent application of these elements.
Timeliness. Currency--metadata keeping up with changes to the resource it
describes. Lag -- a resource’s availability preceding the availability of its
metadata.
Accessibility. Proper association of metadata with the resource it
describes and readability by target users contribute to this characteristic.
Additional characteristics that make quality
metadata more useful in a shared environment:
• Proper context. … each record contain the context necessary for
understanding the resource the record describes, without relying on
outside information.
• Content coherence. … need to contain enough information such
that the record makes sense standing on its own, yet exclude
information that only makes sense in a local environment.
• Use of standard vocabularies. The use of standard vocabularies
enables the better integration of metadata records from one source
with records from other sources.
• Consistency. All decisions made about application of elements,
syntax of metadata values, and usage of controlled vocabularies,
should be consistent within an identifiable set of metadata records
so those using this metadata can apply any necessary
transformation steps without having to process inconsistencies
within such a set.
• Technical conformance. Metadata should conform to the specified
XML schemas and should be properly encoded.
How Can We Ensure A Better Quality?
• Make Policies on:
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minimum quality requirements,
quality measurement instruments,
quality enforcement policies,
quality enhancement actions, and
the training of metadata creators.
• Training!
– A 2-hour training may eliminate hundreds of errors
• IT team should talk with content team
– A test of crosswalk for OAI harvest may prevent thousands of mis-matched
or missed values
• Use Tools:
– Provide instructions on best practices
– Use template for inputting records, with suggested syntax, vocabularies,
and build-in values
– Use validators
– Implement duplicate checking algorithm
Final Thoughts
• Creating shareable metadata requires
thinking outside of our local box
• Creating shareable metadata will require
more work on our part
• Creating shareable metadata will require
our vendors to support (more) standards
• Creating shareable metadata is no longer
an option, it’s a requirement
Before we share…
• Check our metadata
– Appropriate view?
– Consistent?
– Context provided?
– Does the aggregator have what they need?
– Documented?
Can a stranger tell you what the record
describes?
More thoughts
• Automated metadata enhancement techniques promise
to play an essential role in building and aggregating
digital library collections
• But they are not a “magic bullet” – must be used together
with other techniques
– User-contributed metadata
– Content-based retrieval
– Item-level attention by specialists
• Many collections could benefit
– Legacy collections described in MARC
– Special collections largely undescribed, especially at the item
level
• Should technical services expand metadata activities
• Catalogers and their skills essential to this process
The Way Forward?
• Service providers should be more demanding
(i.e. require that data providers adhere to certain
standards and use certain vocabularies, require
“pre-washed” metadata.
• Data providers should consistently use
appropriate standard schemas in their local
systems.
• Service providers should consider “adding
value” via services like vocabulary mapping,
query expansion, vocabulary-assisted
searching, user-added metadata, post-harvest
subsetting, metadata enhancement, etc
Lessons Learned
• Metadata (descriptive, technical, rights,
administrative, preservation) is one of your
biggest investments.
• Do it once, do it right (consistent schemas,
controlled vocabularies), and you can repurpose metadata in a wide variety of ways.
• Good descriptive metadata records can be
core—records don’t need to be “full” to be
“good.”
• Creation of consistent, standards-based
descriptive metadata (a.k.a. cataloging!) is timeand labor-intensive, but it’s worth it.