1
REUSE OF ONTOLOGY MAPPINGS
Anika Groß,
Database Group, Universität Leipzig
Canberra, March 2016
2
• Structured representation of knowledge
• Used for annotation as standardized semantic
description of object properties...
3
MeSH
GALEN
SNOMED CT
NCI
Thesaurus
Uberon
Mouse
Anatomy
FMA
• Overlapping ontologies → creation of mappings/alignments
•...
4
• Overlapping ontologies → creation of mappings/alignments
• Useful for data integration, analysis across sources …
• On...
5
• Ontologies are not static!
• Research, new knowledge  continuous changes
• Release of new versions
• Ontology changes...
6
REUSE EXISTING MAPPINGS TO …
→ create new ontology mappings
• “Indirect” matching: combine existing mappings to create
n...
7
ONTOLOGY MATCHING WORKFLOW
• Manual creation of mappings between very large
ontologies is too labor-intensive
• Semi-aut...
8
?
• Indirect composition-based matching
• Via intermediate ontology (IO):
important hub ontology,
synonym dictionary, …
...
9
• Use mappings to intermediate ontologies IO1, …, IOk
to indirectly match O1 and O2
• Reduce matching effort by reusing ...
10
• (Binary) compose operator
• Composes two mappings 𝑀 𝑂1,𝐼𝑂 and 𝑀𝐼𝑂,𝑂2 to create
a new mapping 𝑀 𝑂1,𝑂2:
COMPOSE OPERATOR
11
O1
IO1
O2
occ = 1: CMO1,O2 = {(a,a),(b,b),(c,c)}
occ = 2: CMO1,O2 = {(a,a)}
Input: Two ontologies O1 and O2, list of in...
12
EVALUATION SETUP
• Match problem
• Adult Mouse Anatomy (MA)
• NCI Thesaurus Anatomy part (NCIT)
Uberon
UMLS
MA NCIT
Rad...
13
EVALUATION SETUP
• Match problem
• Adult Mouse Anatomy (MA)
• NCI Thesaurus Anatomy part (NCIT)
Preprocessing
Normaliza...
14
• Direct match result compared to composeMatch via each IO
• Additional matching of unmatched parts (extendMatch)
RESUL...
15
• Combination of four composed mappings
• Correspondences have to occur in at least 1, …, 4 mappings
RESULTS
union(occ=...
16
• Combination of four composed mappings
• Correspondences have to occur in at least 1, …, 4 mappings
RESULTS
http://oae...
17
COMPOSITION VIA SEVERAL SOURCES
• Many “mapping path” alternatives…
Geo
Names
Linked
GeoData
PubMed
Wrong domain
Hartun...
18
COMPOSITION VIA SEVERAL SOURCES
• Many “mapping path” alternatives…
Geo
Names
Linked
GeoData
PubMedWorldFact
Book
Too s...
19
COMPOSITION VIA SEVERAL SOURCES
• Many “mapping path” alternatives…
Geo
Names
Linked
GeoData
PubMedWorldFact
Book
DBped...
20
COMPOSE OPERATOR
21
EFFECTIVENESS OF MAPPINGS FOR COMPOSITION
Source S Target TIntermediate IMS,I MI,T
domain(MS,I) range(MS,I) domain(MI,T...
22
Mapping-based
• Take all mapping paths between S and T
• Different path filtering methods
1) Effectiveness: k most effe...
23
Reuse of mappings and composition strategies
→ very useful to create new correspondences/links
EVALUATION
60
70
80
90
1...
24
REUSE EXISTING MAPPINGS TO …
→ create new ontology mappings
• “Indirect” matching: combine existing mappings to create
...
25
𝑶𝟏′
𝑶𝟐′
𝑶𝟏
𝑶𝟐
𝑂𝑀 𝑂1,𝑂2 𝑂𝑀 𝑂1′,𝑂2′ ?
Requirements
• High mapping quality
• Mapping consistency
• Include new concepts
• ...
26
ADAPTATION APPROACHES
𝑶𝑴 𝑶𝟏, 𝑶𝟏′
𝑶𝟏
𝑶𝟐
𝑂𝑀 𝑂1,𝑂2
compose
𝒅𝒊𝒇𝒇 𝑶𝟏, 𝑶𝟏′
𝑶𝟏
𝑶𝟐
DiffAdapt
𝑂𝑀 𝑂1,𝑂2
Composition-based
Adaptat...
27
ADAPTATION APPROACHES
𝑶𝑴 𝑶𝟏, 𝑶𝟏′
𝑶𝑴 𝑶𝟐,𝑶𝟐′
𝑶𝟏
𝑶𝟐
𝑂𝑀 𝑂1,𝑂2
𝒅𝒊𝒇𝒇 𝑶𝟏, 𝑶𝟏′
𝒅𝒊𝒇𝒇 𝑶𝟐,𝑶𝟐′
𝑶𝟏
𝑶𝟐
𝑂𝑀 𝑂1,𝑂2
Composition-based
Ada...
28
ADAPTATION APPROACHES
compose
𝑶𝑴 𝑶𝟏, 𝑶𝟏′
𝑶𝑴 𝑶𝟐,𝑶𝟐′
𝑶𝟏
𝑶𝟐
𝑂𝑀 𝑂1,𝑂2 𝑶𝑴 𝑶𝟏′
,𝑶𝟐′
𝒅𝒊𝒇𝒇 𝑶𝟏, 𝑶𝟏′
𝒅𝒊𝒇𝒇 𝑶𝟐,𝑶𝟐′
𝑶𝟏
𝑶𝟐
𝑶𝑴 𝑶𝟏′
,𝑶𝟐...
29
• COnto-Diff: Diff Evolution Mapping 𝑑𝑖𝑓𝑓(𝑂 𝑜𝑙𝑑, 𝑂 𝑛𝑒𝑤)
• Based on match mapping between two ontology versions 𝑂 𝑜𝑙𝑑 an...
30
• Combine ‘old‘ ontology mapping with ontology evolution mapping
(between old and new version): compose-Operator
• Reus...
31
𝑶𝑴 𝑶𝟏,𝑶𝟐′
• Combine ‘old‘ ontology mapping with ontology evolution mapping
(between old and new version): compose-Opera...
32
<
<neck head and neckhead
compose
ℎ𝑎𝑛𝑑𝑙𝑒𝑑
head
neckneck
head and neckhead
𝑶𝟏 𝑶𝟐 𝑶𝟐‘
COMBINATION OF SEMANTIC CORRESPONDE...
33
• Modular, flexible adaptation approach
• Individual migration for different change operations
using Change Handler 𝐶𝐻
...
34
DIFF-BASED ADAPTATION OF ONTOLOGY MAPPINGS
tail
head
neck
limbs
lower extremities limb segments
limbs
upper extremities...
35
DIFF-BASED ADAPTATION OF ONTOLOGY MAPPINGS
tail
head
neck
limbs
lower extremities limb segments
limbs
upper extremities...
36
DIFF-BASED ADAPTATION OF ONTOLOGY MAPPINGS
DiffAdapt 𝑶𝑴 𝑶𝟐,𝑶𝟏, 𝒅𝒊𝒇𝒇 𝑶𝟐,𝑶𝟐′, 𝑶𝟐, 𝑶𝟐′, 𝑶𝟏, 𝑪𝑯
1. Determination of affecte...
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𝑚𝑒𝑟𝑔𝑒 𝒉𝒆𝒂𝒅, 𝑛𝑒𝑐𝑘 , 𝒉𝒆𝒂𝒅 𝒂𝒏𝒅 𝒏𝒆𝒄𝒌
EXAMPLES
MergeHandler
= <neckneck
head and neck= headhead
𝑶𝟏 𝑶𝟐 𝑶𝟐‘
upper extremities ...
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• UMLS Mapping versions: „silver standard“
• Adaptation of 2009 version, reference mapping: 2012 version
EVALUATION
Ont...
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70
75
80
85
90
95
100
Unaff CA CA+m DA DA+m
MAPPING QUALITY SCT-NCIT
• Unaffected correspondences only (Unaff ): good r...
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Adaptation Strategy
1) Automatic detection of consistent mappings
w.r.t. new ontology version
2) Recommendations for ne...
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• Ontology matching and entity linking
• Integration of larger sets of heterogeneous sources:
holistic matching and reu...
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Reuse of Ontology Mappings

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Ontologies are used in numerous research disciplines and commercial applications to uniformly and semantically annotate real-world objects. Often there are multiple interrelated ontologies in a domain, and repositories such as BioPortal already provide mappings (links) between these ontologies. Especially manually verified mappings can be reused 1) to create new mappings between so far unconnected sources, and 2) to avoid an expensive re-identification, e.g. when the underlying ontologies change.

New ontology mappings can be determined by reusing and composing previously determined mappings that involve intermediate ontologies. The composition of mappings is very efficient and can achieve mappings of very high quality especially for valuable intermediate ontologies. Moreover, due to a rapid development of application domains, ontologies are frequently changed to include up-to-date knowledge. These changes dramatically influence dependent data as well as applications like ontology mappings and ontology-based annotations. Thus existing mappings may become invalid and need to be migrated to the most recent ontology versions, such that users and dependent applications can consume up-to-date mappings.

In this talk, I will give a brief introduction to ontology mappings and provide an overview on reuse-based approaches for mapping creation and maintenance, currently studied at the Database Group at Leipzig University.

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Reuse of Ontology Mappings

  1. 1. 1 REUSE OF ONTOLOGY MAPPINGS Anika Groß, Database Group, Universität Leipzig Canberra, March 2016
  2. 2. 2 • Structured representation of knowledge • Used for annotation as standardized semantic description of object properties • Very large ontologies in the life sciences ONTOLOGIES Anatomy Molecular biology ChemistryMedicine Tissue Anatomic Structure, System, or Substance Organ Lung SkinKidney … …
  3. 3. 3 MeSH GALEN SNOMED CT NCI Thesaurus Uberon Mouse Anatomy FMA • Overlapping ontologies → creation of mappings/alignments • Useful for data integration, analysis across sources … • Ontology mapping: set of semantic correspondences (links) between concepts of different ontologies ONTOLOGY MAPPINGS
  4. 4. 4 • Overlapping ontologies → creation of mappings/alignments • Useful for data integration, analysis across sources … • Ontology mapping: set of semantic correspondences (links) between concepts of different ontologies ONTOLOGY MAPPINGS 𝑶𝟐 tail head neck limbs limb segments body 𝑶𝟏 head lower extremities limbs upper extremities body neck trunk tail = = = = < < = 𝑶𝑴 𝑶𝟏,𝑶𝟐 • Manual or semi- automatic identification (matching)
  5. 5. 5 • Ontologies are not static! • Research, new knowledge  continuous changes • Release of new versions • Ontology changes → Impact on dependent mappings and applications? EVOLUTION OF ONTOLOGIES AND MAPPINGS 𝑶𝟏 0 𝑶𝟐 𝑶𝑴 𝑶𝟏,𝑶𝟐
  6. 6. 6 REUSE EXISTING MAPPINGS TO … → create new ontology mappings • “Indirect” matching: combine existing mappings to create new mappings between so far unconnected sources → create up-to-date ontology mappings • Migration of outdated mappings to currently valid ontology versions  Ontologies, ontology mappings, ontology evolution 2) Composition-based ontology matching 3) Adaptation of ontology mappings 4) Outlook
  7. 7. 7 ONTOLOGY MATCHING WORKFLOW • Manual creation of mappings between very large ontologies is too labor-intensive • Semi-automatic generation of semantic correspondences: linguistic, structural, instance-based matching techniques Matching Mapping sim(O1.a, O2.b) = 0.8 sim(O1.a, O2.c) = 0.5 sim(O1.c, O2.c) = 1.0 further input, e.g. instances, dictionary … O1 O2 Pre- processing Post- processing
  8. 8. 8 ? • Indirect composition-based matching • Via intermediate ontology (IO): important hub ontology, synonym dictionary, … MAPPING COMPOSITION MA_0001421 UBERON:0001092 NCI_C32239 Synonym: Atlas Name: atlas Name: C1 VertebraName: cervical vertebra 1 Synonym: cervical vertebra 1 Synonym: C1 vertebra • Find new correspondences via composition • Reuse existing mappings to • Increase match quality & save computation time IO O1 O2 Groß, Hartung, Kirsten, Rahm: Mapping Composition for Matching Large Life Science Ontologies. 2nd International Conference on Biomedical Ontology (ICBO), 2011
  9. 9. 9 • Use mappings to intermediate ontologies IO1, …, IOk to indirectly match O1 and O2 • Reduce matching effort by reusing mappings to IO → very fast composition INDIRECT MATCHING ... IO1 IO2 IOk O1 O2 ... O1 O2 On HOOnew → IO should have a significant overlap with O1 and O2 → IO1, …, IOk may complement each other → Centralized hub HO → many mappings to other ontologies → Onew aligned with any Oi via HO
  10. 10. 10 • (Binary) compose operator • Composes two mappings 𝑀 𝑂1,𝐼𝑂 and 𝑀𝐼𝑂,𝑂2 to create a new mapping 𝑀 𝑂1,𝑂2: COMPOSE OPERATOR
  11. 11. 11 O1 IO1 O2 occ = 1: CMO1,O2 = {(a,a),(b,b),(c,c)} occ = 2: CMO1,O2 = {(a,a)} Input: Two ontologies O1 and O2, list of intermediate ontologies IO1… IOk, occurrence count occ Output: Composed mapping CMO1,O2 COMPOSEMATCH a b c d e a b g h a b c d f a i c IO2 MapList  empty for each IOi  IO do MO1,IOi getMapping(O1, IOi) return 𝑚𝑒𝑟𝑔𝑒(MapList, occ) MapList.add(𝑐𝑜𝑚𝑝𝑜𝑠𝑒(MO1,IOi, MIOi,O2)) MIOi,O2 getMapping(IOi, O2) end for MapList (c,c ), (a,a) (a,a), (b,b)
  12. 12. 12 EVALUATION SETUP • Match problem • Adult Mouse Anatomy (MA) • NCI Thesaurus Anatomy part (NCIT) Uberon UMLS MA NCIT RadLex FMA • Gold standard ~1500 correspondences • Precompute mappings using a match strategy ~5000 ~88,000 ~30,800 ~81,000 ~2,700 ~3,300 #concepts
  13. 13. 13 EVALUATION SETUP • Match problem • Adult Mouse Anatomy (MA) • NCI Thesaurus Anatomy part (NCIT) Preprocessing Normalization Linguistic Matcher (Name, synonyms, Trigram t = 0.8) Selection & Postprocessing Uberon UMLS MA NCIT RadLex FMA • Gold standard ~1500 correspondences ~5000 ~88,000 ~30,800 ~81,000 ~2,700 ~3,300 #concepts
  14. 14. 14 • Direct match result compared to composeMatch via each IO • Additional matching of unmatched parts (extendMatch) RESULTS 88.2% 86% • Uberon & UMLS → best evaluated intermediate ontologies Intermediate Ontology IO
  15. 15. 15 • Combination of four composed mappings • Correspondences have to occur in at least 1, …, 4 mappings RESULTS union(occ=1) F-Measure 90.2 Precision 92.7 Recall 87.8 Higher occurrence → Recall ↓ extendMatch → Recall ↑
  16. 16. 16 • Combination of four composed mappings • Correspondences have to occur in at least 1, …, 4 mappings RESULTS http://oaei.ontologymatching.org/[year]/anatomy Top Results OAEI Other systems later adopted similar techniques to make use of domain specific background knowledge (e.g. including Uberon, UMLS)
  17. 17. 17 COMPOSITION VIA SEVERAL SOURCES • Many “mapping path” alternatives… Geo Names Linked GeoData PubMed Wrong domain Hartung, Groß, Rahm: Composition Methods for Link Discovery. Proc. of 15. GI-Fachtagung für Datenbanksysteme in Business, Technologie und Web (BTW), 2013 • Which intermediate source(s) should be used? S T A B C S T A B C
  18. 18. 18 COMPOSITION VIA SEVERAL SOURCES • Many “mapping path” alternatives… Geo Names Linked GeoData PubMedWorldFact Book Too special Hartung, Groß, Rahm: Composition Methods for Link Discovery. Proc. of 15. GI-Fachtagung für Datenbanksysteme in Business, Technologie und Web (BTW), 2013 • Which intermediate source(s) should be used? S T A B C S T A B C
  19. 19. 19 COMPOSITION VIA SEVERAL SOURCES • Many “mapping path” alternatives… Geo Names Linked GeoData PubMedWorldFact Book DBpedia Ok, universal knowledge source Hartung, Groß, Rahm: Composition Methods for Link Discovery. Proc. of 15. GI-Fachtagung für Datenbanksysteme in Business, Technologie und Web (BTW), 2013 • Which intermediate source(s) should be used? S T A B C S T A B C
  20. 20. 20 COMPOSE OPERATOR
  21. 21. 21 EFFECTIVENESS OF MAPPINGS FOR COMPOSITION Source S Target TIntermediate IMS,I MI,T domain(MS,I) range(MS,I) domain(MI,T) range(MI,T) Binary: n-ary: 1. Mapping coverage in S and T should be high 2. Overlap of entities in I should be high
  22. 22. 22 Mapping-based • Take all mapping paths between S and T • Different path filtering methods 1) Effectiveness: k most effective mapping paths (selEff) 2) Complement: k best complementing mapping paths w.r.t. S and T (selComp) Link-based • Select best routes in a graph of links between entities/concepts (not on “mapping level”) • Graph-based approach • Transformation of S, T and mappings in M into a weighted, directed graph • Application of Shortest-Path algorithm to solve mapping composition problem DIFFERENT COMPOSITION STRATEGIES Hartung, Groß, Rahm: Composition Methods for Link Discovery. Proc. of 15. GI-Fachtagung für Datenbanksysteme in Business, Technologie und Web (BTW), 2013
  23. 23. 23 Reuse of mappings and composition strategies → very useful to create new correspondences/links EVALUATION 60 70 80 90 100 NYT-DBp NYT-FB NYT-GeoN MA-NCIT F-measure all selEff selCompl link • selEff, selComp, link always better than naïve (all) approach Geography (Instance Matching track) Anatomy track • Selection strategies better for Anatomy • Link strategy slightly better for Geography + Best Compose approach always better than direct match
  24. 24. 24 REUSE EXISTING MAPPINGS TO … → create new ontology mappings • “Indirect” matching: combine existing mappings to create new mappings between so far unconnected sources → create up-to-date ontology mappings • Migration of outdated mappings to currently valid ontology versions  Ontologies, ontology mappings, ontology evolution  Composition-based ontology matching 2) Adaptation of ontology mappings 3) Outlook
  25. 25. 25 𝑶𝟏′ 𝑶𝟐′ 𝑶𝟏 𝑶𝟐 𝑂𝑀 𝑂1,𝑂2 𝑂𝑀 𝑂1′,𝑂2′ ? Requirements • High mapping quality • Mapping consistency • Include new concepts • Reduction of manual effort, involve user feedback • Support of semantic mappings • Mappings can become invalid → need to be updated • Reuse existing mappings (avoid full re-determination) MAPPING ADAPTATION PROBLEM Groß: Evolution von ontologiebasierten Mappings in den Lebenswissenschaften, Dissertation, Universität Leipzig, 2014. Groß, Dos Reis, Hartung, Pruski, Rahm: Semi-automatic adaptation of mappings between life science ontologies. Proc. 9th Intl. Conference on Data Integration in the Life Sciences (DILS), 2013.
  26. 26. 26 ADAPTATION APPROACHES 𝑶𝑴 𝑶𝟏, 𝑶𝟏′ 𝑶𝟏 𝑶𝟐 𝑂𝑀 𝑂1,𝑂2 compose 𝒅𝒊𝒇𝒇 𝑶𝟏, 𝑶𝟏′ 𝑶𝟏 𝑶𝟐 DiffAdapt 𝑂𝑀 𝑂1,𝑂2 Composition-based Adaptation (CA) Diff-based Adaptation (DA) 𝑶𝟏’𝑶𝟏’
  27. 27. 27 ADAPTATION APPROACHES 𝑶𝑴 𝑶𝟏, 𝑶𝟏′ 𝑶𝑴 𝑶𝟐,𝑶𝟐′ 𝑶𝟏 𝑶𝟐 𝑂𝑀 𝑂1,𝑂2 𝒅𝒊𝒇𝒇 𝑶𝟏, 𝑶𝟏′ 𝒅𝒊𝒇𝒇 𝑶𝟐,𝑶𝟐′ 𝑶𝟏 𝑶𝟐 𝑂𝑀 𝑂1,𝑂2 Composition-based Adaptation (CA) Diff-based Adaptation (DA) 𝑶𝟏’ 𝑶𝟐’𝑶𝟐’ 𝑶𝟏’
  28. 28. 28 ADAPTATION APPROACHES compose 𝑶𝑴 𝑶𝟏, 𝑶𝟏′ 𝑶𝑴 𝑶𝟐,𝑶𝟐′ 𝑶𝟏 𝑶𝟐 𝑂𝑀 𝑂1,𝑂2 𝑶𝑴 𝑶𝟏′ ,𝑶𝟐′ 𝒅𝒊𝒇𝒇 𝑶𝟏, 𝑶𝟏′ 𝒅𝒊𝒇𝒇 𝑶𝟐,𝑶𝟐′ 𝑶𝟏 𝑶𝟐 𝑶𝑴 𝑶𝟏′ ,𝑶𝟐′ DiffAdapt 𝑂𝑀 𝑂1,𝑂2 Composition-based Adaptation (CA) Diff-based Adaptation (DA) 𝑶𝟏’ 𝑶𝟐’𝑶𝟐’ 𝑶𝟏’
  29. 29. 29 • COnto-Diff: Diff Evolution Mapping 𝑑𝑖𝑓𝑓(𝑂 𝑜𝑙𝑑, 𝑂 𝑛𝑒𝑤) • Based on match mapping between two ontology versions 𝑂 𝑜𝑙𝑑 and 𝑂 𝑛𝑒𝑤 • Set of basic and complex change operations addC, addR, … delC, delR, toObsolete, … split, merge, substitute, … • GENERIC ONTOLOGY MATCHING AND MAPPING MANAGEMENT • Generic infrastructure to manage and analyze evolution of ontologies and mappings GOMMA
  30. 30. 30 • Combine ‘old‘ ontology mapping with ontology evolution mapping (between old and new version): compose-Operator • Reuse and adapt existing correspondences COMPOSITION-BASED ADAPTATION • Semantic correspondence types? + Matching added concepts (𝑂1’𝑂1, 𝑂2’ 𝑂2) tail head neck limbs lower extremities limb segments limbs upper extremities body neck body 𝑶𝟏 𝑶𝟐 trunk limbs head and neck body 𝑶𝟐‘ lower limbs upper limbs == = = = = = < < > > < < tail head 𝑶𝑴 𝑶𝟏,𝑶𝟐 𝑶𝑴 𝑶𝟐,𝑶𝟐′ trunk semType: = equivalent < less general > more general
  31. 31. 31 𝑶𝑴 𝑶𝟏,𝑶𝟐′ • Combine ‘old‘ ontology mapping with ontology evolution mapping (between old and new version): compose-Operator • Reuse and adapt existing correspondences COMPOSITION-BASED ADAPTATION • Semantic correspondence types? + Matching added concepts (𝑂1’𝑂1, 𝑂2’ 𝑂2) lower extremities limbs upper extremities body neck 𝑶𝟏 trunk limbs head and neck body 𝑶𝟐‘ lower limbs upper limbs tail head trunk semType: = equivalent < less general > more general ?
  32. 32. 32 < <neck head and neckhead compose ℎ𝑎𝑛𝑑𝑙𝑒𝑑 head neckneck head and neckhead 𝑶𝟏 𝑶𝟐 𝑶𝟐‘ COMBINATION OF SEMANTIC CORRESPONDENCES • Correspondence (𝑐1, 𝑐2), 𝑐1 ∈ 𝑂1, 𝑐2 ∈ 𝑂2 • 𝑠𝑒𝑚𝑇𝑦𝑝𝑒 ∈ =, <, >, ≈ • 𝑠𝑡𝑎𝑡𝑢𝑠 ∈ ℎ𝑎𝑛𝑑𝑙𝑒𝑑, 𝑡𝑜𝑉𝑒𝑟𝑖𝑓𝑦 = < > ≈ = = < > ≈ < < < ≈ ≈ > > ≈ > ≈ ≈ ≈ ≈ ≈ ≈ semType1 semType2 = = < < semType1 semType2 • Semantic type: ≈ • Status: 𝑡𝑜𝑉𝑒𝑟𝑖𝑓𝑦 • compose → 4 correspondences  lower extremities limb segments upper extremities lower limbs upper limbs > > < < 𝑶𝟏 𝑶𝟐 𝑶𝟐‘
  33. 33. 33 • Modular, flexible adaptation approach • Individual migration for different change operations using Change Handler 𝐶𝐻 • Reuse and adaptation of existing correspondences DIFF-BASED ADAPTATION OF ONTOLOGY MAPPINGS
  34. 34. 34 DIFF-BASED ADAPTATION OF ONTOLOGY MAPPINGS tail head neck limbs lower extremities limb segments limbs upper extremities body neck body 𝑶𝟏 𝑶𝟐 trunk limbs head and neck body 𝑶𝟐‘ lower limbs upper limbs trunk = > = = = = = = < < > < < tail head 𝑶𝑴 𝑶𝟏,𝑶𝟐 𝑶𝑴 𝑶𝟐,𝑶𝟐′
  35. 35. 35 DIFF-BASED ADAPTATION OF ONTOLOGY MAPPINGS tail head neck limbs lower extremities limb segments limbs upper extremities body neck body 𝑶𝟏 𝑶𝟐 trunk limbs head and neck body 𝑶𝟐‘ lower limbs upper limbs trunk = > = = = = = = < < > < < tail head 𝑶𝑴 𝑶𝟏,𝑶𝟐 𝑶𝑴 𝑶𝟐,𝑶𝟐′ merge({head, neck}, head and neck) addC(trunk) delC(tail) 𝒅𝒊𝒇𝒇 𝑶𝟐,𝑶𝟐′ split (limb segments, {lower limbs, upper limbs})
  36. 36. 36 DIFF-BASED ADAPTATION OF ONTOLOGY MAPPINGS DiffAdapt 𝑶𝑴 𝑶𝟐,𝑶𝟏, 𝒅𝒊𝒇𝒇 𝑶𝟐,𝑶𝟐′, 𝑶𝟐, 𝑶𝟐′, 𝑶𝟏, 𝑪𝑯 1. Determination of affected correspondences 𝑶𝑴𝒊𝒏𝒇𝒍 using 𝒅𝒊𝒇𝒇 𝑶𝟐,𝑶𝟐′ 2. Reuse of unaffected mapping part: 𝑂𝑀 𝑂2′,𝑂1← 𝑂𝑀 𝑂2,𝑂1 𝑂𝑀𝑖𝑛𝑓𝑙 3. For each 𝑐ℎ ∈ 𝐶𝐻 • Adaptation of 𝑂𝑀𝑖𝑛𝑓𝑙 using a change hander strategy (𝒅𝒊𝒇𝒇 𝑶𝟐,𝑶𝟐′, 𝑶𝟐, 𝑶𝟐′ , 𝑶𝟏) 4. Union of 𝑂𝑀𝑖𝑛𝑓𝑙 with unaffected mapping part: 𝑂𝑀 𝑂2′,𝑂1← 𝑂𝑀 𝑂2′,𝑂1 ∪ 𝑂𝑀𝑖𝑛𝑓𝑙 tail head neck limbs lower extremities limb segments limbs upper extremities body neck body 𝑶𝟏 𝑶𝟐 trunk limbs head and neck body 𝑶𝟐‘ lower limbs upper limbs trunk = > = = = = = = < < > < < tail head 𝑶𝑴 𝑶𝟏,𝑶𝟐 𝑶𝑴 𝑶𝟐,𝑶𝟐′ 𝑶𝑴𝒊𝒏𝒇𝒍 Unaffected
  37. 37. 37 𝑚𝑒𝑟𝑔𝑒 𝒉𝒆𝒂𝒅, 𝑛𝑒𝑐𝑘 , 𝒉𝒆𝒂𝒅 𝒂𝒏𝒅 𝒏𝒆𝒄𝒌 EXAMPLES MergeHandler = <neckneck head and neck= headhead 𝑶𝟏 𝑶𝟐 𝑶𝟐‘ upper extremities < lower limbs upper limbs lower extremities limb segments < 𝑠𝑝𝑙𝑖𝑡(𝒍𝒊𝒎𝒃 𝒔𝒆𝒈𝒎𝒆𝒏𝒕𝒔, {𝒍𝒐𝒘𝒆𝒓 𝒍𝒊𝒎𝒃𝒔, 𝒖𝒑𝒑𝒆𝒓 𝒍𝒊𝒎𝒃𝒔}) SplitHandler - “take best” ≈lower extremities lower limbs 𝒕𝒐𝑽𝒆𝒓𝒊𝒇𝒚 upper extremities upper limbs≈ 𝒕𝒐𝑽𝒆𝒓𝒊𝒇𝒚 < head and neckhead 𝒉𝒂𝒏𝒅𝒍𝒆𝒅 <neck 𝒉𝒂𝒏𝒅𝒍𝒆𝒅head and neck < > > 𝑚𝑒𝑟𝑔𝑒({ℎ𝑒𝑎𝑑, 𝒏𝒆𝒄𝒌}, 𝒉𝒆𝒂𝒅 𝒂𝒏𝒅 𝒏𝒆𝒄𝒌)
  38. 38. 38 • UMLS Mapping versions: „silver standard“ • Adaptation of 2009 version, reference mapping: 2012 version EVALUATION Ontology size Mapping size 1 10 100 1.000 10.000 100.000 #changes NCIT SCT FMA NCIT SCT #Concepts2009 62,285 63,655 310,121 #Concepts2012 62,285 84,132 318,502 SCT-NCIT #Corr2009 19,971 #Corr2012 22,732 • merge, split, … • Many concept additions and toObsolete changes • Mapping changes • 8% delCorr • 19% addCorr Ontology changes
  39. 39. 39 70 75 80 85 90 95 100 Unaff CA CA+m DA DA+m MAPPING QUALITY SCT-NCIT • Unaffected correspondences only (Unaff ): good results • CA: Precision ↓ • CA+m: Recall ↑ , F-Measure ≈ 90% • Diff-based approaches: increased quality, especially Precision ↑ • DA+m: best quality, F-Measure ≈ 94% RecallUnaff F-MeasureUnaff Precision Recall F-Measure Composition Diff
  40. 40. 40 Adaptation Strategy 1) Automatic detection of consistent mappings w.r.t. new ontology version 2) Recommendations for new correspondences → Aim: complete mapping 3) Expert validation of correspondence (𝑡𝑜𝑉𝑒𝑟𝑖𝑓𝑦 status) SEMI-AUTOMATIC MAPPING ADAPTATION  High mapping quality  Consistent mapping  New correspondences for new concepts  Reduction of manual effort  Consider mapping semantics
  41. 41. 41 • Ontology matching and entity linking • Integration of larger sets of heterogeneous sources: holistic matching and reuse of clustered entities • Semantic enrichment with concepts of ontologies • Interactive tools for link verification • Mapping semantics • Use of semantic relationships (is-a, part-of, …) in mappings and Diff • Evolution and adaptation of ontology-based annotations OUTLOOK

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