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1
EVOLUTION OF
ONTOLOGY-BASED MAPPINGS
Anika Groß, Database Group
ALIGNED project meeting, Dec 3rd 2015
2
• Introduction
• GOMMA
• COnto-Diff
• Evolution of
• Ontology mappings
• Annotation mappings
• ELISA project
AGENDA
3
• Structured representation
of knowledge
• Very large ontologies
in biomedical domain
ONTOLOGIES
Anatomy Molecular
biology
ChemistryMedicine
Tissue
Anatomic Structure,
System, or Substance
Organ
Lung SkinKidney …
…
4
P10646
(TFPI1_HUMAN)
GO:0007596
(blood coagulation)
is involved in
• Standardized semantic description of object properties
ONTOLOGY-BASED ANNOTATIONS
Genes, proteins, … PublicationsElectronic health
records
5
P10646
(TFPI1_HUMAN)
GO:0007596
(blood coagulation)
• Standardized semantic description of object properties
ONTOLOGY-BASED ANNOTATIONS
Gene
Ontology
Ensembl
Annotation Mapping
Ensembl ID GO ID
ENSP00000344151 GO:0015808 (L-alanine transport)
ENSP00000230480 GO:0005615 (extracellular space)
ENSP00000352999 GO:0006915 (apoptosis)
Genes, proteins, … PublicationsElectronic health
records
• Applications:
• Semantic search, navigation …
• Functional analysis: identification of significant characteristics
of specific gene/proteins groups
6
• Overlapping ontologies → creation of mappings/alignments
• Useful for data integration, analysis across sources …
• Ontology mapping: set of semantic correspondences 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)
7
• Ontologies are not static!
• Research, new knowledge  continuous changes
• Release of new versions
• Ontology changes
EVOLUTION OF ONTOLOGY-BASED MAPPINGS
𝑶𝟏
0
𝑶𝟐
𝑶𝑴 𝑶𝟏,𝑶𝟐
8
How can I determine
changes between
ontology versions?
Does evolution impact
annotations and analysis
results?
How can I migrate
existing mappings to
currently valid ontology
versions?
Impact of ontology evolution on dependent
mappings and applications
How does ontology
evolution influence
ontology mappings?
9
• Introduction
• GOMMA
• COnto-Diff
• Evolution of
• Ontology mappings
• Annotation mappings
• ELISA project
AGENDA
10
• GENERIC ONTOLOGY MATCHING AND MAPPING MANAGEMENT
• Generic infrastructure to manage and analyze evolution of
ontologies and mappings
• CODEX (Complex Ontology Diff Explorer)
• www.izbi.de/codex
• REX (Region Evolution Explorer)
• http://www.izbi.de/rex
GOMMA
11
• Basic changes (add, del, update) are often not sufficient
• Large ontologies → need compact diff
• Different modeling of changes (e.g. obsolete)
• Aim: determine an expressive, complete, invertible diff
evolution mapping between given versions of an ontology
• Rule-based approach
• Input: match mapping between two ontology versions 𝑂 𝑜𝑙𝑑, 𝑂 𝑛𝑒𝑤
• Output: Diff Evolution Mapping 𝑑𝑖𝑓𝑓(𝑂 𝑜𝑙𝑑, 𝑂 𝑛𝑒𝑤)
• Set of basic and complex change operations for concepts and
properties (relationships + concept attributes)
addC, addR, …
delC, delR, toObsolete, …
split, merge, substitute, …
CONTODIFF (COMPLEX ONTOLOGY DIFF)
Hartung, Groß, Rahm: COnto-Diff: Generation of Complex Evolution Mappings for Life Science
Ontologies. Journal of Biomedical Informatics 46 (1): 15-32, 2013.
12
CONTO-DIFF OVERVIEW
Match
Ontology
version
Oold
Ontology
version
Onew
Working
repository
Match System
• Single Matchers
• Match Workflows
• Set Operators
diffcompact
(Oold,Onew)
COG (Change Operation Generating)
Rule library
Rules (b-COG, c-COG, a-COG
Background
Knowledge
BK
match
(Oold,Onew) Basic
Change
Detection
Complex
Change
Detection
Aggregation
Ruled-based
Change Detection
diffbasic
(Oold,Onew)
13
• Input: Evolution mapping diffbasic(Oold, Onew), rule set Rc-COG
• Output: diff(Oold,Onew)
• Example: merge-rule
COMPLEX CHANGE DETECTION - EXAMPLE
e
d
c
a
b
a,bOold  cOnew  ab  mapC(a,c)  mapC(b,c)
 ∄ d(dOnew  mapC(a,d)  cd)  ∄ e(eOnew  mapC(b,e)  ce)
→ create[merge({a},c), merge({b},c)], eliminate[mapC(a,c), mapC(b,c)]
• Apply further rules to recursively aggregate and create
complex change operations
• merge({a,b},c)
14
• Introduction
• GOMMA
• COnto-Diff
• Evolution of
• Ontology mappings
• Annotation mappings
• ELISA project
AGENDA
15
• Mappings can become invalid → need to be updated
• Reuse existing mappings (avoid full re-determination)
MAPPING ADAPTATION
𝑶𝟏′
𝑶𝟐′
𝑶𝟏
𝑶𝟐
𝑂𝑀 𝑂1,𝑂2 𝑂𝑀 𝑂1′
,𝑂2′
?
Groß, Dos Reis, Hartung, Pruski, Rahm: Semi-automatic adaptation of
mappings between life science ontologies. DILS, 2013.
Anforderungen:
• Hohe Mappingqualität
• Mappingkonsistenz
• Einbeziehen neuer Konzepte
• Reduzierung des manuellen Aufwands, Involvierung von Nutzern
• Unterstützung von semantischen Mappings
𝒅𝒊𝒇𝒇 𝑶𝟏, 𝑶𝟏′
𝒅𝒊𝒇𝒇 𝑶𝟐,𝑶𝟐′
𝑶𝟏
𝑶𝟐
𝑶𝑴 𝑶𝟏′
,𝑶𝟐′
DiffAdapt
DiffAdapt
𝑂𝑀 𝑂1,𝑂2
Diff-based
Adaptierung (DA)
𝑶𝟏′
𝑶𝟐′
16
• 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
17
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′
18
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′𝒅𝒊𝒇𝒇 𝑶𝟐,𝑶𝟐′
addC(trunk)
delC(tail)
split (limb segments, {lower limbs, upper limbs})
merge({head, neck}, head and neck)
19
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′𝒅𝒊𝒇𝒇 𝑶𝟐,𝑶𝟐′
𝑶𝑴𝒊𝒏𝒇𝒍
Unaffected
20
𝑚𝑒𝑟𝑔𝑒 𝒉𝒆𝒂𝒅, 𝑛𝑒𝑐𝑘 , 𝒉𝒆𝒂𝒅 𝒂𝒏𝒅 𝒏𝒆𝒄𝒌
EXAMPLE - MERGE
MergeHandler
= <neckneck
head and neck= headhead
𝑶𝟏 𝑶𝟐 𝑶𝟐‘
< head and neckhead 𝒉𝒂𝒏𝒅𝒍𝒆𝒅
<neck 𝒉𝒂𝒏𝒅𝒍𝒆𝒅head and neck
<
𝑚𝑒𝑟𝑔𝑒({ℎ𝑒𝑎𝑑, 𝒏𝒆𝒄𝒌}, 𝒉𝒆𝒂𝒅 𝒂𝒏𝒅 𝒏𝒆𝒄𝒌)
21
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
22
exp - Verified by experiment
auth - Author statement
auto - Automatically generated
Ensembl ID GO ID v48 v49 v50 v51 v52
ENSP…344151 GO:0015808 exp exp exp exp exp
Ensembl ID GO ID v48 v49 v50 v51 v52
ENSP…344151 GO:0015808 exp exp exp exp exp
ENSP…230480 GO:0005615 auth auth exp auth auto
ANNOTATION EVOLUTION ANALYSIS
Groß, Hartung, Kirsten, Rahm: Estimating the Quality of Ontology-Based
Annotations by Considering Evolutionary Changes. DILS, 2009.
Ensembl ID GO ID v48 v49 v50 v51 v52
ENSP…344151 GO:0015808 exp exp exp exp exp
ENSP…230480 GO:0005615 auth auth exp auth auto
ENSP…352999 GO:0006915 exp - - - exp
• 80% of additions in Ensembl: auto
• Instabilities for auto and auth
• Temporary deletions
• Changing provenance information
• Stable, manually verified, > 𝟏
𝟐 year?
• 13% of Ensembl and 76% Swiss-Prot annotations
• How do annotations change?
• Quality + reliability of annotations?
• Use provenance and stability information
23
ANNOTATIONSEVOLUTION
0
40000
80000
120000
160000
200000
240000
25 27 29 31 33 35 37 39 41 43 45 47 49 51
#annotations
version
man
auto
• 78% out of 265,000 annotations → automatically generated
• growthauto = 4.6
• v40 – v42 many deletions
Ensembl 2004-2008
24
• Introduction
• GOMMA
• COnto-Diff
• Evolution of
• Ontology mappings
• Annotation mappings
• ELISA project
AGENDA
25
• Collaboration
• Luxembourg Institute of Science and Technology (LIST)
• University of Paris-Sud
• Database Group, Universität Leipzig
• Granted by German Research Foundation (DFG) and
National Research Fund Luxembourg (FNR)
• Motivation
• Medical domain is highly dynamic
• 50% of knowledge is renewed every 10 years
• Content of ontologies follows the evolution of the domain
• Modifications in ontologies must be propagated to
ontology-based semantic annotations
ELISA - EVOLUTION OF SEMANTIC ANNOTATIONS
http://dbs.uni-leipzig.de/research/projects/evolution_of_ontologies_and_mappings/elisa
26
Objectives
• Understand the quality of annotations through manual and automatic
annotation processes
• Identify and characterize ontology evolution
• Exploit this information to define maintenance/migration algorithms
for semantic annotations
PROJECT OVERVIEW
27
• New annotation methods
• Christen, Groß, Varghese, Dugas, Rahm:
Annotating Medical Forms using UMLS. DILS 2015
• Use of COntoDiff+ further development
• Development of new maintenance algorithms
• Two real case applications
• Annotations that serve to enrich patient data in the
Luxembourgish national health platform
• Annotation of case report forms (CRFs) used in
clinical trial research
… help companies and research projects in managing the ever-
increasing quantity of their data
PROJECT OVERVIEW (2)
28
Adaptation of semantic mappings
• Semantic enrichment of mappings and Diff (is-a, part-of, …)
• Interactive tools for verification of correspondences and
annotations
Annotation Quality
• Other studies* confirm instability of annotations and their impact
• Sophisticated methods to assess quality
→Can be used by algorithms / applications
Evaluation
of these methods in other domains, e.g. social sciences
OUTLOOK
* Groß, Hartung, Prüfer, Kelso, Rahm: Impact of Ontology Evolution on Functional
Analyses. Bioinformatics, 2012.
Gillis, Pavlidis: Assessing identity, redundancy and confounds in gene ontology
annotation over time. Bioinformatics, 2013.
Clarke, Loguercio, Good, Su: A task-based approach for Gene Ontology evaluation.
Journal of Biomedical Semantics, 2013.
29
0
2
4
6
8
10
Cumulativefrequency
year
Name change
„Leipzig“
800 1015 1165 1220 1232 1402 1459 1494 1507

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EVOLUTION OF ONTOLOGY-BASED MAPPINGS

  • 1. 1 EVOLUTION OF ONTOLOGY-BASED MAPPINGS Anika Groß, Database Group ALIGNED project meeting, Dec 3rd 2015
  • 2. 2 • Introduction • GOMMA • COnto-Diff • Evolution of • Ontology mappings • Annotation mappings • ELISA project AGENDA
  • 3. 3 • Structured representation of knowledge • Very large ontologies in biomedical domain ONTOLOGIES Anatomy Molecular biology ChemistryMedicine Tissue Anatomic Structure, System, or Substance Organ Lung SkinKidney … …
  • 4. 4 P10646 (TFPI1_HUMAN) GO:0007596 (blood coagulation) is involved in • Standardized semantic description of object properties ONTOLOGY-BASED ANNOTATIONS Genes, proteins, … PublicationsElectronic health records
  • 5. 5 P10646 (TFPI1_HUMAN) GO:0007596 (blood coagulation) • Standardized semantic description of object properties ONTOLOGY-BASED ANNOTATIONS Gene Ontology Ensembl Annotation Mapping Ensembl ID GO ID ENSP00000344151 GO:0015808 (L-alanine transport) ENSP00000230480 GO:0005615 (extracellular space) ENSP00000352999 GO:0006915 (apoptosis) Genes, proteins, … PublicationsElectronic health records • Applications: • Semantic search, navigation … • Functional analysis: identification of significant characteristics of specific gene/proteins groups
  • 6. 6 • Overlapping ontologies → creation of mappings/alignments • Useful for data integration, analysis across sources … • Ontology mapping: set of semantic correspondences 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)
  • 7. 7 • Ontologies are not static! • Research, new knowledge  continuous changes • Release of new versions • Ontology changes EVOLUTION OF ONTOLOGY-BASED MAPPINGS 𝑶𝟏 0 𝑶𝟐 𝑶𝑴 𝑶𝟏,𝑶𝟐
  • 8. 8 How can I determine changes between ontology versions? Does evolution impact annotations and analysis results? How can I migrate existing mappings to currently valid ontology versions? Impact of ontology evolution on dependent mappings and applications How does ontology evolution influence ontology mappings?
  • 9. 9 • Introduction • GOMMA • COnto-Diff • Evolution of • Ontology mappings • Annotation mappings • ELISA project AGENDA
  • 10. 10 • GENERIC ONTOLOGY MATCHING AND MAPPING MANAGEMENT • Generic infrastructure to manage and analyze evolution of ontologies and mappings • CODEX (Complex Ontology Diff Explorer) • www.izbi.de/codex • REX (Region Evolution Explorer) • http://www.izbi.de/rex GOMMA
  • 11. 11 • Basic changes (add, del, update) are often not sufficient • Large ontologies → need compact diff • Different modeling of changes (e.g. obsolete) • Aim: determine an expressive, complete, invertible diff evolution mapping between given versions of an ontology • Rule-based approach • Input: match mapping between two ontology versions 𝑂 𝑜𝑙𝑑, 𝑂 𝑛𝑒𝑤 • Output: Diff Evolution Mapping 𝑑𝑖𝑓𝑓(𝑂 𝑜𝑙𝑑, 𝑂 𝑛𝑒𝑤) • Set of basic and complex change operations for concepts and properties (relationships + concept attributes) addC, addR, … delC, delR, toObsolete, … split, merge, substitute, … CONTODIFF (COMPLEX ONTOLOGY DIFF) Hartung, Groß, Rahm: COnto-Diff: Generation of Complex Evolution Mappings for Life Science Ontologies. Journal of Biomedical Informatics 46 (1): 15-32, 2013.
  • 12. 12 CONTO-DIFF OVERVIEW Match Ontology version Oold Ontology version Onew Working repository Match System • Single Matchers • Match Workflows • Set Operators diffcompact (Oold,Onew) COG (Change Operation Generating) Rule library Rules (b-COG, c-COG, a-COG Background Knowledge BK match (Oold,Onew) Basic Change Detection Complex Change Detection Aggregation Ruled-based Change Detection diffbasic (Oold,Onew)
  • 13. 13 • Input: Evolution mapping diffbasic(Oold, Onew), rule set Rc-COG • Output: diff(Oold,Onew) • Example: merge-rule COMPLEX CHANGE DETECTION - EXAMPLE e d c a b a,bOold  cOnew  ab  mapC(a,c)  mapC(b,c)  ∄ d(dOnew  mapC(a,d)  cd)  ∄ e(eOnew  mapC(b,e)  ce) → create[merge({a},c), merge({b},c)], eliminate[mapC(a,c), mapC(b,c)] • Apply further rules to recursively aggregate and create complex change operations • merge({a,b},c)
  • 14. 14 • Introduction • GOMMA • COnto-Diff • Evolution of • Ontology mappings • Annotation mappings • ELISA project AGENDA
  • 15. 15 • Mappings can become invalid → need to be updated • Reuse existing mappings (avoid full re-determination) MAPPING ADAPTATION 𝑶𝟏′ 𝑶𝟐′ 𝑶𝟏 𝑶𝟐 𝑂𝑀 𝑂1,𝑂2 𝑂𝑀 𝑂1′ ,𝑂2′ ? Groß, Dos Reis, Hartung, Pruski, Rahm: Semi-automatic adaptation of mappings between life science ontologies. DILS, 2013. Anforderungen: • Hohe Mappingqualität • Mappingkonsistenz • Einbeziehen neuer Konzepte • Reduzierung des manuellen Aufwands, Involvierung von Nutzern • Unterstützung von semantischen Mappings 𝒅𝒊𝒇𝒇 𝑶𝟏, 𝑶𝟏′ 𝒅𝒊𝒇𝒇 𝑶𝟐,𝑶𝟐′ 𝑶𝟏 𝑶𝟐 𝑶𝑴 𝑶𝟏′ ,𝑶𝟐′ DiffAdapt DiffAdapt 𝑂𝑀 𝑂1,𝑂2 Diff-based Adaptierung (DA) 𝑶𝟏′ 𝑶𝟐′
  • 16. 16 • 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
  • 17. 17 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′
  • 18. 18 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′𝒅𝒊𝒇𝒇 𝑶𝟐,𝑶𝟐′ addC(trunk) delC(tail) split (limb segments, {lower limbs, upper limbs}) merge({head, neck}, head and neck)
  • 19. 19 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′𝒅𝒊𝒇𝒇 𝑶𝟐,𝑶𝟐′ 𝑶𝑴𝒊𝒏𝒇𝒍 Unaffected
  • 20. 20 𝑚𝑒𝑟𝑔𝑒 𝒉𝒆𝒂𝒅, 𝑛𝑒𝑐𝑘 , 𝒉𝒆𝒂𝒅 𝒂𝒏𝒅 𝒏𝒆𝒄𝒌 EXAMPLE - MERGE MergeHandler = <neckneck head and neck= headhead 𝑶𝟏 𝑶𝟐 𝑶𝟐‘ < head and neckhead 𝒉𝒂𝒏𝒅𝒍𝒆𝒅 <neck 𝒉𝒂𝒏𝒅𝒍𝒆𝒅head and neck < 𝑚𝑒𝑟𝑔𝑒({ℎ𝑒𝑎𝑑, 𝒏𝒆𝒄𝒌}, 𝒉𝒆𝒂𝒅 𝒂𝒏𝒅 𝒏𝒆𝒄𝒌)
  • 21. 21 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
  • 22. 22 exp - Verified by experiment auth - Author statement auto - Automatically generated Ensembl ID GO ID v48 v49 v50 v51 v52 ENSP…344151 GO:0015808 exp exp exp exp exp Ensembl ID GO ID v48 v49 v50 v51 v52 ENSP…344151 GO:0015808 exp exp exp exp exp ENSP…230480 GO:0005615 auth auth exp auth auto ANNOTATION EVOLUTION ANALYSIS Groß, Hartung, Kirsten, Rahm: Estimating the Quality of Ontology-Based Annotations by Considering Evolutionary Changes. DILS, 2009. Ensembl ID GO ID v48 v49 v50 v51 v52 ENSP…344151 GO:0015808 exp exp exp exp exp ENSP…230480 GO:0005615 auth auth exp auth auto ENSP…352999 GO:0006915 exp - - - exp • 80% of additions in Ensembl: auto • Instabilities for auto and auth • Temporary deletions • Changing provenance information • Stable, manually verified, > 𝟏 𝟐 year? • 13% of Ensembl and 76% Swiss-Prot annotations • How do annotations change? • Quality + reliability of annotations? • Use provenance and stability information
  • 23. 23 ANNOTATIONSEVOLUTION 0 40000 80000 120000 160000 200000 240000 25 27 29 31 33 35 37 39 41 43 45 47 49 51 #annotations version man auto • 78% out of 265,000 annotations → automatically generated • growthauto = 4.6 • v40 – v42 many deletions Ensembl 2004-2008
  • 24. 24 • Introduction • GOMMA • COnto-Diff • Evolution of • Ontology mappings • Annotation mappings • ELISA project AGENDA
  • 25. 25 • Collaboration • Luxembourg Institute of Science and Technology (LIST) • University of Paris-Sud • Database Group, Universität Leipzig • Granted by German Research Foundation (DFG) and National Research Fund Luxembourg (FNR) • Motivation • Medical domain is highly dynamic • 50% of knowledge is renewed every 10 years • Content of ontologies follows the evolution of the domain • Modifications in ontologies must be propagated to ontology-based semantic annotations ELISA - EVOLUTION OF SEMANTIC ANNOTATIONS http://dbs.uni-leipzig.de/research/projects/evolution_of_ontologies_and_mappings/elisa
  • 26. 26 Objectives • Understand the quality of annotations through manual and automatic annotation processes • Identify and characterize ontology evolution • Exploit this information to define maintenance/migration algorithms for semantic annotations PROJECT OVERVIEW
  • 27. 27 • New annotation methods • Christen, Groß, Varghese, Dugas, Rahm: Annotating Medical Forms using UMLS. DILS 2015 • Use of COntoDiff+ further development • Development of new maintenance algorithms • Two real case applications • Annotations that serve to enrich patient data in the Luxembourgish national health platform • Annotation of case report forms (CRFs) used in clinical trial research … help companies and research projects in managing the ever- increasing quantity of their data PROJECT OVERVIEW (2)
  • 28. 28 Adaptation of semantic mappings • Semantic enrichment of mappings and Diff (is-a, part-of, …) • Interactive tools for verification of correspondences and annotations Annotation Quality • Other studies* confirm instability of annotations and their impact • Sophisticated methods to assess quality →Can be used by algorithms / applications Evaluation of these methods in other domains, e.g. social sciences OUTLOOK * Groß, Hartung, Prüfer, Kelso, Rahm: Impact of Ontology Evolution on Functional Analyses. Bioinformatics, 2012. Gillis, Pavlidis: Assessing identity, redundancy and confounds in gene ontology annotation over time. Bioinformatics, 2013. Clarke, Loguercio, Good, Su: A task-based approach for Gene Ontology evaluation. Journal of Biomedical Semantics, 2013.