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Mapping Taxonomies,
Thesauri, and Ontologies
presented by
Heather Hedden
Hedden Information Management
1
▪ Taxonomy consultant
– Independent, through Hedden Information Management (since 2004)
– Employed, through Project Performance Corporation, and contract
▪ Former staff taxonomist
– At various companies: Gale/Cengage Learning, Viziant, First Wind
▪ Instructor of online and onsite taxonomy courses
– Independently through Hedden Information Management
– Previously at Simmons University - Library & Information Science School
▪ Author of The Accidental Taxonomist (2010, 2016, Information Today, Inc.)
▪ Former indexer of books and database content (articles, images, etc.)
About Heather Hedden
2
© 2019 Hedden Information Management
1. Introduction to mapping knowledge organization systems (KOS)
2. Situations for KOS mapping
3. Method of mapping
4. Mapping examples
5. Standards for mapping
6. Tools for mapping
7. Mapping case study
Outline
3
© 2019 Hedden Information Management
Knowledge organization systems (KOS):
▪ Taxonomies
▪ Thesauri
▪ Ontologies
▪ Other controlled vocabularies
➢ Usually created for a specific use (specific content and audience)
➢ Occasionally created for wider, shared use
➢ Often are enhanced, or extended or adapted for additionally uses
Introduction
4
© 2019 Hedden Information Management
Mapping knowledge organization systems (KOS)
▪ A form of linking knowledge organization systems together
▪ Linking individual concepts in one KOS to concepts in another.
▪ Retaining them each as a distinct KOS.
▪ A KOS continues to be used for its original purpose plus added use
through the mapped KOS.
The name “mapping” might
come from mathematical
set theory, whereby elements
in one set are mapped to
elements in another set.
Introduction
5
© 2019 Hedden Information Management
Mapping types
▪ Directional from one KOS to another with sufficiently equivalent links, so
that one KOS may be used for another.
▪ Directional from a term set to a KOS with equivalent and hierarchical links,
so that a KOS can be enriched with added concepts.
▪ Bidirectional, with equivalent links, so that content can be shared.
▪ Bidirectional, with associative links, so that users can navigate to new
content. (Might not call “mapping”)
Introduction
6
© 2019 Hedden Information Management
Crosswalk – a table of mappings between concepts in two or more structured
vocabularies.
▪ Depending on systems used, a designated crosswalk table may or may
not be created.
▪ A KOS managed in software with a mapping feature does not require a
crosswalk, but a crosswalk file can be generated/exported.
Introduction
7
© 2019 Hedden Information Management
An expanded set of content, tagged with a different KOS, will be retrieved by
users with their existing KOS.
➢ The organization continues
to provide only its KOS to its
users to retrieve both its own
content and added content.
▪ A content publisher with a KOS
partners with a specialized
information vendor, with its own
KOS, to expand its content
offering.
▪ An organization with a KOS tagged to its internal content licenses content
from an external source that is tagged with a different KOS.
Situations for KOS Mapping
8
UsersContent KOS
KOS mapping
Added content KOS 2
tagged
tagged
retrieved
© 2019 Hedden Information Management
A set of content will be retrieved by different audiences, each accessing their
own KOS.
➢ Identical content will be retrieved
by end-users with a new KOS.
Rather than re-index, the new
KOS (or more than one) will be
mapped to the existing KOS.
▪ Selected content with an enterprise taxonomy is
made available on a public web site with a
different public-facing taxonomy.
▪ A provider of scientific/technical/medical content
with a technical thesaurus creates a simpler taxonomy aimed at laypeople.
▪ Content will be made available in a different language region (locale), and a
comparable KOS already exists in that other language.
Situations for KOS Mapping
9
User groupContent KOS
tagged retrieved
User group 2KOS 2
KOS mapping
retrieved
© 2019 Hedden Information Management
A front-end KOS will be used to retrieve various content sets, each tagged
with its own KOS.
➢ A vastly expanded set of content
can be accurately retrieved.
▪ A knowledge graph is built to
aggregate data from multiple
repositories or data silos,
each with its own KOS.
▪ An enterprise search is based
on “federated search.”
▪ A search engine product
taxonomy is mapped to, in
order to increase SEO.
Situations for KOS Mapping
10
Users
Content KOS 1
tagged
retrievedKOS 2
tagged
KOS 3
tagged
KOS mapping
front-end
KOS
© 2019 Hedden Information Management
A term list is mapped to a KOS to enrich the KOS.
➢ A vastly expanded set of content
can be accurately retrieved.
▪ Terms from search engine logs
are mapped to a KOS to add
alternative labels.
▪ Terms from an open source or licensed
vocabulary are mapped to a KOS.
Situations for KOS Mapping
11
UsersContent KOS
tagged retrieved
Term list
KOS mapping
© 2019 Hedden Information Management
© 2019 Hedden Information Management
Mapping methodology/theory
▪ Mapping direction: from a tagged taxonomy (source) to the retrieval/user-
interface taxonomy (target)
▪ Consider the tagged-taxonomy/source terms as variants (alternative labels)
for the retrieval taxonomy/target terms.
‒ Equivalent meaning is for the context.
‒ Narrower-to-broader matches are OK: a narrower concept in a tagging
taxonomy may be mapped to a broader concept in the retrieval taxonomy, if
no equivalent exists in the retrieval taxonomy.
‒ Many-to-one mappings are OK.
Method for Mapping
12Retrieval KOS
mapped retrievedtagged
Tagged KOS
© 2019 Hedden Information Management
Mapping methodology/theory
▪ Focus on the meaning of concepts.
▪ Relationships between concepts within a KOS generally do not matter.
▪ Can map between term lists, taxonomies, thesauri, ontologies
▪ The type of KOS does not impact the direction of mapping,
although the usual case is from simpler to more complex KOS.
Method for Mapping
13
Term list Taxonomy Taxonomy
Ontology
© 2019 Hedden Information Management
Directional mapping is easier when:
▪ The scope of both is identical.
▪ The retrieval KOS has fewer terms than the tagged KOS.
▪ The tagged KOS is more specific/granular than the retrieval KOS.
Directional mapping is more complex when:
▪ Mapping from a hierarchical taxonomy to a faceted taxonomy
▪ There is inconsistency, and one KOS is more detailed (with more
specific/granular concepts) in some areas, and the other KOS is more
detailed in other areas.
Directional mapping does not work when:
▪ From a faceted taxonomy to a hierarchical taxonomy, thesaurus, or ontology
Method for Mapping
14
© 2019 Hedden Information Management
Mapping technique/steps
▪ Identify which KOS is the tagged/mapped-from taxonomy, and which KOS is
the retrieval/mapped-to taxonomy.
▪ Use a software tool or scripts to compare both, to obtain exact matches and
close matches.
▪ Human review confirms and approves automatically proposed close
matches.
▪ Human review attempts to identify mappings for unmatched concepts,
but some will remain unmapped and cannot be utilized.
▪ If all tagged content is required for inclusion, then new concepts need to be
added to the retrieval KOS.
Method for Mapping
15
© 2019 Hedden Information Management
Automatic mappings, without requiring review, comprises:
▪ Exact match concepts, ignoring only capitalization and diacritics
▪ Concept in tagged/source KOS is an exact match to a synonym (alternative
label) of a concept in the retrieval/target KOS
Automatic suggested mappings for human review, comprises:
▪ Keyword matches – all the same words, but can be in any order
▪ Stemmed keyword matches – same words, any order, but also includes
plural/singular and certain grammatical variants
▪ Concept label phrase within another concept label – if the retrieval KOS
concept is within the tagged KOS concept label, it’s usually a good match.
(The latter is longer and likely qualified, and thus more specific.)
▪ Combinations of above
Method for Mapping
16
© 2019 Hedden Information Management
Method for Mapping
17
Match Type Tagged KOS Concept Retrieval KOS Concept
Auto-match, needs no review
Exact match Information technology Information Technology
Exact synonym match Banknotes Currency altLabel Banknotes
Auto-match + Review
Keyword match - yes Financing debt Debt financing
Keyword match - no Industry news News industry
Stemmed keyword match - yes Data security Secure data
Stemmed keyword match - no Fair trading Trade fairs
Phrase within phrase - yes Geothermal power plants Power plants
Phrase within phrase - no Computer hardware & software Computer hardware
Multiple words within - yes Danish language books Danish books
Multiple words within - no Public health education Public higher education
© 2019 Hedden Information Management
Mapping Examples
18
Colum A:
Tagged taxonomy
(from)
Column B:
Retrieval taxonomy
(to)
Column C:
Human review notes:
“ok” is equivalent,
“b” second term is
broader so also ok,
“n” is narrower or
otherwise not
acceptable.
Review example
© 2019 Hedden Information Management
Mapping Examples
19
Colum A:
Target/retrieval taxonomy (to)
Column B:
Source terms from search log
(from)
Column C:
Auto-suggested
Column D:
Human review approves as
“y” - yes
Review
example
© 2019 Hedden Information Management
Mapping Examples
20
Computer Hardware & Software N Computer Hardware 4
Computer Hardware & Software N Computer Software 4
Consumer Electronics & Appliances Stores Y Consumer Electronics 4
Electrical & Electronic Manufacturing Y Electrical/Electronic Manufacturing 4
Health Care Services & Hospitals Y Hospital & Health Care 4
Investment Banking & Asset Management Y Investment Banking 4
Investment Banking & Asset Management N Investment Management 4
Sporting Goods Stores Y Sporting Goods 4
Automotive Parts & Accessories Stories Y Automotive 5
Biotech & Pharmaceuticals N Pharmaceuticals 5
Cable N Internet 5
Casual Restaurants Y Restaurants 5
Financial Analytics & Research N Research 5
© 2019 Hedden Information Management
SKOS (Simple Knowledge Organization System)
Has a set of relation type properties for mapping:
▪ mappingRelation – the parent category relation-type property that includes the
others:
▪ exactMatch – exact match, bidirectional, in all circumstances
▪ closeMatch – close match, bidirectional, in some (sufficient) circumstances or in
a certain context
▪ broadMatch – has broader concept in the other KOS; inverse of narrowMatch
▪ narrowMatch – has narrower concept in the other KOS; inverse of broadMatch
▪ relatedMatch – has related concept in the other KOS, bidirectional
➢ For directional mapping from a tagged KOS to a retrieval KOS, could use the
generic mappingRelation or a combination of exactMatch and closeMatch.
Standards for Mapping
21
© 2019 Hedden Information Management
ISO 25964-2 Information and Documentation – Thesauri and
interoperability with other Vocabularies
Part 2: Interoperability with other vocabularies (2013)
▪ Inter-vocabulary mapping is the principal focus.
▪ Addresses the theory and method of various kinds of mappings.
▪ Addresses both one-way directional mapping, and multi-directional.
▪ Considers also mapping between thesauri and other kinds of vocabularies:
synonym rings, classification schemes, subject heading schemes,
taxonomies, terminologies, name authority lists, and ontologies.
Standards for Mapping
22
© 2019 Hedden Information Management
Scripting languages (e.g. Perl), or advanced features of Excel
▪ Used if KOS management software does not have batch/auto-mapping
or to enhance software mapping with additional, less-close matches
KOS management software feature (PoolParty, Synaptica, Semaphore)
▪ SKOS-based KOS management software supports mapping
relationships between concepts in different vocabularies
▪ KOS management software may also have batch/auto-mapping
feature for exact and close matches.
▪ Maintaining mapping relations in a KOS management software
supports ongoing maintenance, in case changes occur with concepts.
Tools for Mapping
23
© 2019 Hedden Information Management
Beyond Mapping: Other KOS Linking
24
Example in PoolParty
taxonomy, thesaurus, and
ontology management
software:
The mapping of one KOS
on industries to another
KOS on industries, using
the Project Linking feature.
© 2019 Hedden Information Management
Beyond Mapping: Other KOS Linking
25
Batch linking results, matching
preferred labels to each other,
or
alternative-to-preferred labels,
for manual approval or editing.
© 2019 Hedden Information Management
Beyond Mapping: Other KOS Linking
26
Concept details
Advanced SKOS view
displays the various
SKOS mapping types.
© 2019 Hedden Information Management
Regulatory information database vendor Wolters Kluwer Financial Services
wanted to map its new Regulatory Change taxonomy to the internal taxonomy
of a leading bank client of theirs, so that the client could retrieve both its internal
content and the subscribed regulatory change content with a single taxonomy.
Mapping Case Study
27
© 2019 Hedden Information Management
Issue: Initial mapping was done before the new Wolters Kluwer regulatory
change taxonomy was completed, since it was desired to have mapping also
serve to enrich the taxonomy with new terms.
Problems:
▪ Concepts and their labels were not yet finalized in the retrieval taxonomy, so
mapping would be postponed or might have to be redone.
▪ A change in a label is OK, but a change in the meaning of a concept
impacts mapping.
Solution:
▪ Using a KOS management software tool that automates mappings saves
time in doing mappings, so doing mapping twice at different stages in the
project is OK.
Mapping Case Study
28
© 2019 Hedden Information Management
Issue: It was desired to have the mapping go in both directions.
Problems:
▪ Only exact matches would work in both directions, but many mappings are
not exact, but slightly narrower-to-broader.
▪ Mappings could be done twice, once in each direction, but that's more work.
Solutions:
▪ Using SKOS designated broadMatch and narrowMatch, in addition to
exactMatch, preserves narrower-to-broader distinctions, and the mappings
function in both directions.
▪ Using a KOS management software tool that automates mappings of exact
matches and close matches saves time in doing mappings, so a mapping in
the other direction can also be done to check quality of initial mapping.
Mapping Case Study
29
© 2019 Hedden Information Management
Issue: A very low number of automated matches were initially achieved.
Problems:
▪ Scope of taxonomies do not match.
▪ Terms that could be mapped were not because they were not similar enough.
▪ Synonyms/alternative labels were very few in one taxonomy and not
complete in the other.
Solution:
▪ Adding more alternative labels to concepts in both vocabularies support
automated matching, and the automated matching is run again.
Examples that did not automatically match, but should have:
Commercial Accounts <-> Business Deposit Accounts
(The latter had more specific types only, as examples, for alternative labels.)
Mapping Case Study
30
© 2019 Hedden Information Management
Other cross-taxonomy relationships with other functions
▪ Relationships across taxonomies, that are “related term” types of
relationships, not equivalence type
▪ No automatic way to create them, done term-by-term
▪ Could use SKOS relatedMatch relationship
▪ When a user selects a concept, it does not retrieve content tagged to both
concepts in both taxonomies.
▪ Relationships (directly or indirectly) must display to the end user.
▪ Relationships can be generic “related term” or customized/semantic.
▪ Example: Products Taxonomy concepts related to Interests Taxonomy
concepts
Beyond Equivalency Mapping: Other KOS Linking
31
© 2019 Hedden Information Management
Questions/Contact
32
Heather Hedden
Taxonomy Consultant
Hedden Information Management
Carlisle, MA USA
+1 978-467-5195
www.hedden-information.com
accidental-taxonomist.blogspot.com
www.linkedin.com/in/hedden
Twitter: @hhedden

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Mapping Taxonomies, Thesauri, and Ontologies

  • 1. Mapping Taxonomies, Thesauri, and Ontologies presented by Heather Hedden Hedden Information Management 1
  • 2. ▪ Taxonomy consultant – Independent, through Hedden Information Management (since 2004) – Employed, through Project Performance Corporation, and contract ▪ Former staff taxonomist – At various companies: Gale/Cengage Learning, Viziant, First Wind ▪ Instructor of online and onsite taxonomy courses – Independently through Hedden Information Management – Previously at Simmons University - Library & Information Science School ▪ Author of The Accidental Taxonomist (2010, 2016, Information Today, Inc.) ▪ Former indexer of books and database content (articles, images, etc.) About Heather Hedden 2 © 2019 Hedden Information Management
  • 3. 1. Introduction to mapping knowledge organization systems (KOS) 2. Situations for KOS mapping 3. Method of mapping 4. Mapping examples 5. Standards for mapping 6. Tools for mapping 7. Mapping case study Outline 3 © 2019 Hedden Information Management
  • 4. Knowledge organization systems (KOS): ▪ Taxonomies ▪ Thesauri ▪ Ontologies ▪ Other controlled vocabularies ➢ Usually created for a specific use (specific content and audience) ➢ Occasionally created for wider, shared use ➢ Often are enhanced, or extended or adapted for additionally uses Introduction 4 © 2019 Hedden Information Management
  • 5. Mapping knowledge organization systems (KOS) ▪ A form of linking knowledge organization systems together ▪ Linking individual concepts in one KOS to concepts in another. ▪ Retaining them each as a distinct KOS. ▪ A KOS continues to be used for its original purpose plus added use through the mapped KOS. The name “mapping” might come from mathematical set theory, whereby elements in one set are mapped to elements in another set. Introduction 5 © 2019 Hedden Information Management
  • 6. Mapping types ▪ Directional from one KOS to another with sufficiently equivalent links, so that one KOS may be used for another. ▪ Directional from a term set to a KOS with equivalent and hierarchical links, so that a KOS can be enriched with added concepts. ▪ Bidirectional, with equivalent links, so that content can be shared. ▪ Bidirectional, with associative links, so that users can navigate to new content. (Might not call “mapping”) Introduction 6 © 2019 Hedden Information Management
  • 7. Crosswalk – a table of mappings between concepts in two or more structured vocabularies. ▪ Depending on systems used, a designated crosswalk table may or may not be created. ▪ A KOS managed in software with a mapping feature does not require a crosswalk, but a crosswalk file can be generated/exported. Introduction 7 © 2019 Hedden Information Management
  • 8. An expanded set of content, tagged with a different KOS, will be retrieved by users with their existing KOS. ➢ The organization continues to provide only its KOS to its users to retrieve both its own content and added content. ▪ A content publisher with a KOS partners with a specialized information vendor, with its own KOS, to expand its content offering. ▪ An organization with a KOS tagged to its internal content licenses content from an external source that is tagged with a different KOS. Situations for KOS Mapping 8 UsersContent KOS KOS mapping Added content KOS 2 tagged tagged retrieved © 2019 Hedden Information Management
  • 9. A set of content will be retrieved by different audiences, each accessing their own KOS. ➢ Identical content will be retrieved by end-users with a new KOS. Rather than re-index, the new KOS (or more than one) will be mapped to the existing KOS. ▪ Selected content with an enterprise taxonomy is made available on a public web site with a different public-facing taxonomy. ▪ A provider of scientific/technical/medical content with a technical thesaurus creates a simpler taxonomy aimed at laypeople. ▪ Content will be made available in a different language region (locale), and a comparable KOS already exists in that other language. Situations for KOS Mapping 9 User groupContent KOS tagged retrieved User group 2KOS 2 KOS mapping retrieved © 2019 Hedden Information Management
  • 10. A front-end KOS will be used to retrieve various content sets, each tagged with its own KOS. ➢ A vastly expanded set of content can be accurately retrieved. ▪ A knowledge graph is built to aggregate data from multiple repositories or data silos, each with its own KOS. ▪ An enterprise search is based on “federated search.” ▪ A search engine product taxonomy is mapped to, in order to increase SEO. Situations for KOS Mapping 10 Users Content KOS 1 tagged retrievedKOS 2 tagged KOS 3 tagged KOS mapping front-end KOS © 2019 Hedden Information Management
  • 11. A term list is mapped to a KOS to enrich the KOS. ➢ A vastly expanded set of content can be accurately retrieved. ▪ Terms from search engine logs are mapped to a KOS to add alternative labels. ▪ Terms from an open source or licensed vocabulary are mapped to a KOS. Situations for KOS Mapping 11 UsersContent KOS tagged retrieved Term list KOS mapping © 2019 Hedden Information Management
  • 12. © 2019 Hedden Information Management Mapping methodology/theory ▪ Mapping direction: from a tagged taxonomy (source) to the retrieval/user- interface taxonomy (target) ▪ Consider the tagged-taxonomy/source terms as variants (alternative labels) for the retrieval taxonomy/target terms. ‒ Equivalent meaning is for the context. ‒ Narrower-to-broader matches are OK: a narrower concept in a tagging taxonomy may be mapped to a broader concept in the retrieval taxonomy, if no equivalent exists in the retrieval taxonomy. ‒ Many-to-one mappings are OK. Method for Mapping 12Retrieval KOS mapped retrievedtagged Tagged KOS
  • 13. © 2019 Hedden Information Management Mapping methodology/theory ▪ Focus on the meaning of concepts. ▪ Relationships between concepts within a KOS generally do not matter. ▪ Can map between term lists, taxonomies, thesauri, ontologies ▪ The type of KOS does not impact the direction of mapping, although the usual case is from simpler to more complex KOS. Method for Mapping 13 Term list Taxonomy Taxonomy Ontology
  • 14. © 2019 Hedden Information Management Directional mapping is easier when: ▪ The scope of both is identical. ▪ The retrieval KOS has fewer terms than the tagged KOS. ▪ The tagged KOS is more specific/granular than the retrieval KOS. Directional mapping is more complex when: ▪ Mapping from a hierarchical taxonomy to a faceted taxonomy ▪ There is inconsistency, and one KOS is more detailed (with more specific/granular concepts) in some areas, and the other KOS is more detailed in other areas. Directional mapping does not work when: ▪ From a faceted taxonomy to a hierarchical taxonomy, thesaurus, or ontology Method for Mapping 14
  • 15. © 2019 Hedden Information Management Mapping technique/steps ▪ Identify which KOS is the tagged/mapped-from taxonomy, and which KOS is the retrieval/mapped-to taxonomy. ▪ Use a software tool or scripts to compare both, to obtain exact matches and close matches. ▪ Human review confirms and approves automatically proposed close matches. ▪ Human review attempts to identify mappings for unmatched concepts, but some will remain unmapped and cannot be utilized. ▪ If all tagged content is required for inclusion, then new concepts need to be added to the retrieval KOS. Method for Mapping 15
  • 16. © 2019 Hedden Information Management Automatic mappings, without requiring review, comprises: ▪ Exact match concepts, ignoring only capitalization and diacritics ▪ Concept in tagged/source KOS is an exact match to a synonym (alternative label) of a concept in the retrieval/target KOS Automatic suggested mappings for human review, comprises: ▪ Keyword matches – all the same words, but can be in any order ▪ Stemmed keyword matches – same words, any order, but also includes plural/singular and certain grammatical variants ▪ Concept label phrase within another concept label – if the retrieval KOS concept is within the tagged KOS concept label, it’s usually a good match. (The latter is longer and likely qualified, and thus more specific.) ▪ Combinations of above Method for Mapping 16
  • 17. © 2019 Hedden Information Management Method for Mapping 17 Match Type Tagged KOS Concept Retrieval KOS Concept Auto-match, needs no review Exact match Information technology Information Technology Exact synonym match Banknotes Currency altLabel Banknotes Auto-match + Review Keyword match - yes Financing debt Debt financing Keyword match - no Industry news News industry Stemmed keyword match - yes Data security Secure data Stemmed keyword match - no Fair trading Trade fairs Phrase within phrase - yes Geothermal power plants Power plants Phrase within phrase - no Computer hardware & software Computer hardware Multiple words within - yes Danish language books Danish books Multiple words within - no Public health education Public higher education
  • 18. © 2019 Hedden Information Management Mapping Examples 18 Colum A: Tagged taxonomy (from) Column B: Retrieval taxonomy (to) Column C: Human review notes: “ok” is equivalent, “b” second term is broader so also ok, “n” is narrower or otherwise not acceptable. Review example
  • 19. © 2019 Hedden Information Management Mapping Examples 19 Colum A: Target/retrieval taxonomy (to) Column B: Source terms from search log (from) Column C: Auto-suggested Column D: Human review approves as “y” - yes Review example
  • 20. © 2019 Hedden Information Management Mapping Examples 20 Computer Hardware & Software N Computer Hardware 4 Computer Hardware & Software N Computer Software 4 Consumer Electronics & Appliances Stores Y Consumer Electronics 4 Electrical & Electronic Manufacturing Y Electrical/Electronic Manufacturing 4 Health Care Services & Hospitals Y Hospital & Health Care 4 Investment Banking & Asset Management Y Investment Banking 4 Investment Banking & Asset Management N Investment Management 4 Sporting Goods Stores Y Sporting Goods 4 Automotive Parts & Accessories Stories Y Automotive 5 Biotech & Pharmaceuticals N Pharmaceuticals 5 Cable N Internet 5 Casual Restaurants Y Restaurants 5 Financial Analytics & Research N Research 5
  • 21. © 2019 Hedden Information Management SKOS (Simple Knowledge Organization System) Has a set of relation type properties for mapping: ▪ mappingRelation – the parent category relation-type property that includes the others: ▪ exactMatch – exact match, bidirectional, in all circumstances ▪ closeMatch – close match, bidirectional, in some (sufficient) circumstances or in a certain context ▪ broadMatch – has broader concept in the other KOS; inverse of narrowMatch ▪ narrowMatch – has narrower concept in the other KOS; inverse of broadMatch ▪ relatedMatch – has related concept in the other KOS, bidirectional ➢ For directional mapping from a tagged KOS to a retrieval KOS, could use the generic mappingRelation or a combination of exactMatch and closeMatch. Standards for Mapping 21
  • 22. © 2019 Hedden Information Management ISO 25964-2 Information and Documentation – Thesauri and interoperability with other Vocabularies Part 2: Interoperability with other vocabularies (2013) ▪ Inter-vocabulary mapping is the principal focus. ▪ Addresses the theory and method of various kinds of mappings. ▪ Addresses both one-way directional mapping, and multi-directional. ▪ Considers also mapping between thesauri and other kinds of vocabularies: synonym rings, classification schemes, subject heading schemes, taxonomies, terminologies, name authority lists, and ontologies. Standards for Mapping 22
  • 23. © 2019 Hedden Information Management Scripting languages (e.g. Perl), or advanced features of Excel ▪ Used if KOS management software does not have batch/auto-mapping or to enhance software mapping with additional, less-close matches KOS management software feature (PoolParty, Synaptica, Semaphore) ▪ SKOS-based KOS management software supports mapping relationships between concepts in different vocabularies ▪ KOS management software may also have batch/auto-mapping feature for exact and close matches. ▪ Maintaining mapping relations in a KOS management software supports ongoing maintenance, in case changes occur with concepts. Tools for Mapping 23
  • 24. © 2019 Hedden Information Management Beyond Mapping: Other KOS Linking 24 Example in PoolParty taxonomy, thesaurus, and ontology management software: The mapping of one KOS on industries to another KOS on industries, using the Project Linking feature.
  • 25. © 2019 Hedden Information Management Beyond Mapping: Other KOS Linking 25 Batch linking results, matching preferred labels to each other, or alternative-to-preferred labels, for manual approval or editing.
  • 26. © 2019 Hedden Information Management Beyond Mapping: Other KOS Linking 26 Concept details Advanced SKOS view displays the various SKOS mapping types.
  • 27. © 2019 Hedden Information Management Regulatory information database vendor Wolters Kluwer Financial Services wanted to map its new Regulatory Change taxonomy to the internal taxonomy of a leading bank client of theirs, so that the client could retrieve both its internal content and the subscribed regulatory change content with a single taxonomy. Mapping Case Study 27
  • 28. © 2019 Hedden Information Management Issue: Initial mapping was done before the new Wolters Kluwer regulatory change taxonomy was completed, since it was desired to have mapping also serve to enrich the taxonomy with new terms. Problems: ▪ Concepts and their labels were not yet finalized in the retrieval taxonomy, so mapping would be postponed or might have to be redone. ▪ A change in a label is OK, but a change in the meaning of a concept impacts mapping. Solution: ▪ Using a KOS management software tool that automates mappings saves time in doing mappings, so doing mapping twice at different stages in the project is OK. Mapping Case Study 28
  • 29. © 2019 Hedden Information Management Issue: It was desired to have the mapping go in both directions. Problems: ▪ Only exact matches would work in both directions, but many mappings are not exact, but slightly narrower-to-broader. ▪ Mappings could be done twice, once in each direction, but that's more work. Solutions: ▪ Using SKOS designated broadMatch and narrowMatch, in addition to exactMatch, preserves narrower-to-broader distinctions, and the mappings function in both directions. ▪ Using a KOS management software tool that automates mappings of exact matches and close matches saves time in doing mappings, so a mapping in the other direction can also be done to check quality of initial mapping. Mapping Case Study 29
  • 30. © 2019 Hedden Information Management Issue: A very low number of automated matches were initially achieved. Problems: ▪ Scope of taxonomies do not match. ▪ Terms that could be mapped were not because they were not similar enough. ▪ Synonyms/alternative labels were very few in one taxonomy and not complete in the other. Solution: ▪ Adding more alternative labels to concepts in both vocabularies support automated matching, and the automated matching is run again. Examples that did not automatically match, but should have: Commercial Accounts <-> Business Deposit Accounts (The latter had more specific types only, as examples, for alternative labels.) Mapping Case Study 30
  • 31. © 2019 Hedden Information Management Other cross-taxonomy relationships with other functions ▪ Relationships across taxonomies, that are “related term” types of relationships, not equivalence type ▪ No automatic way to create them, done term-by-term ▪ Could use SKOS relatedMatch relationship ▪ When a user selects a concept, it does not retrieve content tagged to both concepts in both taxonomies. ▪ Relationships (directly or indirectly) must display to the end user. ▪ Relationships can be generic “related term” or customized/semantic. ▪ Example: Products Taxonomy concepts related to Interests Taxonomy concepts Beyond Equivalency Mapping: Other KOS Linking 31
  • 32. © 2019 Hedden Information Management Questions/Contact 32 Heather Hedden Taxonomy Consultant Hedden Information Management Carlisle, MA USA +1 978-467-5195 www.hedden-information.com accidental-taxonomist.blogspot.com www.linkedin.com/in/hedden Twitter: @hhedden