The document discusses metadata mapping and crosswalks, which facilitate the transfer of data between different metadata schemes used by cultural institutions. It describes various challenges and approaches related to interoperability, as well as processes for metadata ingestion, validation, transformation, and publishing. The emphasis is on the importance of flexibility and intelligence in achieving successful metadata crosswalks.

1. Metadata Mapping
&
Metadata Crosswalks
Nikos Palavitsinis, PhD
Alternative Title
”the story of combining
Ariadne’s thread with the Gordian Knot”

2. What are crosswalks?
• Crosswalks show people where to put the data
from one scheme into a different scheme. They
are often used by libraries, archives, museums,
and other cultural institutions to translate data
to or from MARC, Dublin Core, TEI, and
other metadata schemes.
source

3. One-way only
The process of translating from one schema to another is called
metadata mapping or field mapping [source]
Crosswalk from MARC to DC Crosswalk from DC to MARC

4. Mapping Problems
• Element A in Scheme A contains X values that
need to be split up into Element 1 and
Element 2 of Scheme B
• Element A in Scheme A can take more that
one values (multiplicity of n) whereas the
equivalent Element 2 in Scheme B, takes all
these values in a single field

5. Mapping Problems
• Different data formats across schemas (use of
names, other conventions, etc.)
• Element A in Scheme A is indexed but the equivalent
element in the other scheme is not
• Scheme A uses a different controlled vocabulary for
the same Element than Scheme B

6. “The more metadata experience we have, the more
it becomes clear that metadata perfection is not
attainable, and anyone who attempts it will be
sorely disappointed.
When metadata is crosswalked between two or
more unrelated sources, there will be data elements
that cannot be reconciled in an ideal manner. The
key to a successful metadata crosswalk is intelligent
flexibility. It is essential to focus on the important
goals and be willing to compromise in order to
reach a practical conclusion…“
"Metadata in Practice" Diane I. Hillmann and Elaine L. Westbrooks, eds.,
American Library Association, Chicago, 2004, p. 91.

7. Automated?
• Metadata Crosswalks can be automated, but
due to the complexity of metadata standards
and the extent of customization taking place,
only few general purpose automated
processes exist for crosswalks

8. Mapping between formats
• Excellent resource by Michael Day of UKOLN
– http://www.ukoln.ac.uk/metadata/interoperability/
Source

9. Metadata Element Set
• Two key components
– Semantics: Definitions of the meanings of the
elements
– Content: Declarations or instructions (or rules) of
what and how values should be assigned to
elements

10. Why map metadata?
• “Interoperability is the ability of multiple
systems with different hardware and software
platforms, data structures, and interfaces to
exchange data with minimal loss of content
and functionality”
NISO (National Information Standards Organization). (2004). Understanding metadata. Bethesda, MD: NISO
Press. Available: <http://www.niso.org/standards/resources/UnderstandingMetadata.pdf>.

11. Interoperability
…on a schema level
focusing on the elements of the schemas, being independent of
any applications. Derived element sets, encoded schemas,
crosswalks, application profiles, and element registries
…on a record level
focusing on integrating metadata records through the mapping
of the elements according to the semantic meanings of these
elements. Converted records and new records resulting from
combining values of existing records

12. Interoperability
…on a repository level
focusing on mapping value strings associated with particular
elements (terms associated with subject or format elements).
The results enable cross-collection searching
Source: http://www.dlib.org/dlib/june06/chan/06chan.html

13. Interoperability on the schema level
• This is achieved through:
– Derivation
• Using elements from existing schemas or standards, as
they are
– Application Profiling
• Localizing and optimizing schemata for specific contexts
– Metadata Crosswalks
• mapping elements, semantics, and syntax from one
metadata scheme to those of another

14. Interoperability on the schema level
• This is achieved through:
– Switching Across
• When trying to crosswalk among more schemas, using a
central one as a switch and crosswalking all to this one, is
easier
– Metadata Framework
• Either developing it based on existing schemas, or
establishing it before the development of schemas and
application profiles
– Metadata Registry
• Offering a centralized access point to existing schemas, to
facilitate the development of new ones and “foster”
interoperability

15. Crosswalking Approaches
• Absolute crosswalking
– You only match the elements that are 100%
equivalent and you ignore the rest
• Useful when mapping from a simpler to a more
complex schema
• Relative crosswalking
– You map all elements in a source schema to at
least one element of a target schema
• Useful when mapping from a complex to a simpler
schema

16. Three Meanings of Interoperability
• Semantic
– Semantic mapping is the process of analyzing the
definitions of the elements or fields to determine
whether they have the same or similar meanings
• Cultural
– presence of data models or wrappers that specify the
semantic schema being used
• Syntactic (technical)
– the ability to communicate, transport, store, and
represent metadata and other types of information
between and among different systems and schemas
Source

17. Examples of Metadata Ingestion

18. Bitter Harvest: Problems &
Suggested Solutions for
OAI-PMH Data & Service
Providers

19. Fill Partner Request Form
Process Partner Request Form and
decide on viable aggregation route
Send Data
Exchange
Agreement (DEA)
Inform
aggregator and
liaise with
potential data
provider
Sign DEA and send to Europeana (data
providers or aggregators have to sign
with aggregator)
Send Data Contribution Form
Fill Data Contribution Form and send to
Europeana
Process Data Contribution Form to
enable first delivery of data
Delivery of data via OAI-PMH or FTP
sample or full datasets
(new data providers)
Feedback on metadata structure,
mandatory elements, rights statements
Delivery of ingest ready data: full
datasets (all data providers)
Feedback taken
into account
Check data
Feedback on
metadata
structure,
mandatory
elements, rights
statements
Ingestion of
datasets fully
compliant to
publication
policy
Publication of the submitted datasets in
Europeana
Action for data
provider or
aggregator
Action for
Europeana
Before 5th
of a month
Before 15th of a
month
Before 21st
of a month
Between 21st
and 30th
of a month
Between 10th
and 20th of
following month
Source: Europeana_Sounds

20. Metadata Operations
• Metadata Harvesting
– The process of collecting metadata descriptions of records
in an archive so that services can be built using metadata
from many archives [source]
• Metadata Validation
– The process of checking the structure of a metadata record
to define whether or not the record complies to a
predefined set of criteria
• Metadata Ingestion
– The process of bringing metadata records (and/or
content), into your system [source]
– i.e. You ingest metadata through harvesting [source]

21. Metadata Operations
• Metadata Transformation
– Converting a set of metadata values from the format of a source
system into the format of a destination system [source]
• Metadata Enrichment
– The process of adding metadata to an existing metadata record,
thus creating a new record, with added-value operations
• Metadata Publishing
– The process of making metadata data elements available to
external users, both people and machines using a formal review
process and a commitment to change control processes [source]

22. Step 1
Harvesting
You harvest the metadata
through OAI-PMH in an
“intermediate” system

23. Step 2
Harvesting
Ingestion
The metadata are ingested into
the target repository or any
other intermediate system

24. Step 3
Harvesting
Ingestion
Metadata elements are mapped
to the metadata schema of the
receiving repository
Mapping

25. Step 4
Harvesting
Ingestion
Mapping
Validation
You pass the metadata through
a mechanism that checks their
integrity in reference to a pre-
defined standard/schema

26. Step 5
Harvesting
Ingestion
Mapping
Validation
Transformation
Metadata are subjected to the
necessary transformations
identified by the validation step

27. Step 6
Harvesting
Ingestion
Mapping
Validation
Transformation
Enrichment
If necessary, metadata may be
enriched further, adding value or
changing them altogether
Transformation
& Enrichment

28. Step 7… … …Step 1.223.124
Harvesting
Ingestion
Mapping
Validation
Transformation
Enrichment
Publishing
Metadata are published on the
target repository and are offered
also through an OAI-PMH target
And round it goes!

29. Reading Material
Other Sources/Projects/Initiatives:
• http://www.slideshare.net/RoldanBasilio/metadata-mapping-61747115
• http://pro.carare.eu/doku.php?id=support:metadata-mapping
• http://old.carare.eu/eng/Support/About-metadata-mapping
• https://en.wikipedia.org/wiki/Data_mapping
• http://www.oclc.org/research/themes/data-science/schematrans.html
• https://indico.cern.ch/event/103325/contributions/1300399/attachments/11668/17064/OAI7_UNSW.pdf
• http://www.slideshare.net/locloud/the-mint-mapping-tool-and-the-more-aggregator
• http://www.slideshare.net/Europeana_Sounds/aggregation-workflow

30. Metadata Mapping
&
Metadata Crosswalks
Nikos Palavitsinis, PhD
Alternative Title
”the story of combining
Ariadne’s thread with the Gordian Knot”