ALINA PETROVA

EMCL WORKSHOP 1 8.02.2014

Learning formal definitions for biomedical concepts

Examples of reasoning over structured biomedical knowledge

2

1) Covert et al. 2012: Whole-cell simulation •  computational model of all processes in a bacterium •  2 years, >1000 articles

2) King et al. 2009: Automation of science •  Adam the Robot Scientist •  generate functional genomic hypothesis

about a yeast •  used knowledge bases and ontologies

for hypothesis generation and analysis •  experimental validation

The growth of biomedical scientific literature 3

Tsatsaronis et al. 2013

Existing biomedical ontologies 4

# concept year research/production

Definitions

UMLS 1,000,000 1986 R,P textual, triples

SNOMED CT 300,000 1965 P formal

FMA 75,000 1995 P triples

GO 42,000 1998 P textual

GALEN 29,000 1991 R formal

MeSH 25,000 1963 P textual

Great need to convert textual definitions to formal representation!

Formalizing biomedical knowledge 5

Atelectasis (Lung collapse) example:

Absence of air in the entire or part of a lung, such as an incompletely inflated neonate lung or a collapsed adult lung. Pulmonary atelectasis can be caused by airway obstruction, lung compression, fibrotic contraction, or other factors.

vs.

Atelectasis = Disorder of lung ⊓ ∃has_associate_morphology(Collapse) ⊓ ∃has_finding_site(Lung structure) ⊓ ∃has_episodicity(Episodicities) ⊓ ∃has_clinical_course(Courses)

…

An example of a MeSH definition 6

Arthritis is a form of joint disorder that results from joint inflammation. When bone surfaces become less well protected by cartilage, bone may be exposed and damaged.

Is it easy to formalize a definition? 7

Arthritis is a form of joint disorder that results from joint inflammation. Arthritis = Joint_Disorder ⊓ ∃results_from.Joint_Inflammation

YES!

Is it easy to formalize a definition? 8

When bone surfaces become less well protected by cartilage, bone may be exposed and damaged.

Temporal logic?

Modal logic?

Situation calculus?

???

NO!

DL? which one?

Sources of problems 9

�  Conceptual modeling ¡  Joint_Inflammation or Inflammation – related_to – Joints ?

�  Expressive modeling ¡  what exactly do we want to model? to what degree of

sophistication? using which formalism?

�  Text mining ¡  how to establish the dependencies between words in a

definition?

The Goal 10

A is a B that has property C.

A ≣ B ⊓ ∃property.C

How to extract formal definitions? 11

CONCEPT ANNOTATION

RELATION EXTRACTION

RELATION CLASSIFICATION

Example 12

Abdominal Wall: the outer margins of the abdomen, extending from the osteocartilaginous thoracic cage to the pelvis.

Step 1: Concept annotation 13

Abdominal Wall: the outer margins of the abdomen, extending from the osteocartilaginous thoracic cage to the pelvis. the abdomen -> ‘Abdomen’ the osteocartilaginous thoracic cage -> ‘Thorax’ the pelvis -> ‘Pelvis’

Step 2: Relation extraction 14

Abdominal Wall: the outer margins of the abdomen, extending from the osteocartilaginous thoracic cage to the pelvis. “outer margins of” (Abdominal wall, Abdomen) “that extends from” (Abdominal wall, Thorax) “that extends to” (Abdominal wall, Pelvis)

Step 3: Relation classification 15

“outer margins of” (Abdominal wall, Abdomen) “that extends from” (Abdominal wall, Thorax) “that extends to” (Abdominal wall, Pelvis) location(Abdominal wall, Abdomen) starts(Abdominal wall, Thorax) ends(Abdominal wall, Pelvis)

How to extract formal definitions? 16

CONCEPT ANNOTATION

RELATION EXTRACTION

RELATION CLASSIFICATION

RELATION EXTRACTION

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SUPERVISED

Approach #1: align existing resources 18

Atelectasis (Lung collapse) example:

Absence of air in the entire or part of a lung, such as an incompletely inflated neonate lung or a collapsed adult lung. Pulmonary atelectasis can be caused by airway obstruction, lung compression, fibrotic contraction, or other factors.

vs.

Atelectasis = Disorder of lung ⊓ ∃has_associate_morphology(Collapse) ⊓ ∃has_finding_site(Lung structure) ⊓ ∃has_episodicity(Episodicities) ⊓ ∃has_clinical_course(Courses)

…

Results 19

�  Relations: extract 3 SNOMED relations from MeSH textual definitions

�  Results: 75% success rate for single-label classification

A – relational string – B

A – relation label – B

Results 20

�  How to improve 75%? ¡  add new features ¡  use resources with consistent modeling

Be data-driven!

A – relational string – B

A – relation label – B

Approach #2: annotate a corpus 21

SemRep: �  a rule-based system for biomedical relation extraction �  26 relations �  a corpus of 500 annotated sentences �  1300 relation instances

Top relations: process_of, location_of, part_of, treats, isa, affects, causes, interacts_with, uses etc.

SemRep relations 22

Two key improvements 23

�  Consistent modeling Before: MeSH texts VS. SNOMED CT relations After: SemRep texts VS. SemRep relations �  The use of concept types Before: lexical features (ngrams) After: ngrams + concept types of relation arguments

Concept types 24

Motivation: every relation has a domain and a range è  only specific types of concepts can be used as arguments

UMLS (biggest knowledge source for biomedicine, thesaurus, upper ontology etc.): 133 semantic types Tissue, Cell Function, Animal, Behavior, Physical Object, Molecular Sequence etc. Hormone – affects – Cell Function Body Substance – causes – Anatomical Abnormality

Why concept types are useful? 25

given concepts A, B MeSH triple: A “is in some relation with” B Before: A – relation R1 – B both are candidates!

A – relation R2 – B After: A à type Аt, B à type Bt

R1 ⊆ At x Bt R2 ⊆ Ct x Dt

RESULTS 26

Before: 424 instances, top 3 relations, 75% After: 860 instances, top 5 relations, 94%

1144 instances, top 10 relations, 89% 1357 instances, all 26 relations, 83%

Comparison with SemRep 27

SemRep ML method

Quality top 5 95% 94%

top 10 94% 89%

all 94% 83%

Scalability

not scalable scalable

Training speed

manually annotated corpus + rules = months

annotated corpus + ML = minutes

still rely on the labeled corpus è approach #3

RELATION EXTRACTION

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UNSUPERVISED

Why is no annotated corpus needed? 29

Original approach:

term A – relational string – term B

concept A – formal relation – B concept Now add the concept types!

annotation

The corpus is not manually annotated! 30

term A – relational string – term B

concept A – relational string – concept B

concept type A’ – formal relation – concept type B’

known from taxonomy/thesaurus!

known from the corpus

Still we use SemRep as a background. Can we do better?

Approach #3: unsupervised relation extraction 31

Yes! �  no manual annotation �  no predefined relations

�  only taxonomy and annotation needed �  semantic clustering

term A – relational string – term B

concept A – relational string – concept B

concept type A’ – verb – concept type B’

Cluster examples 32

�  {attach, bind}

�  {cause, produce, induce}

�  {transmit, convey, carry}

�  {limit, inhibit, reduce}

�  {result, lead}

etc.

Conclusions 33

�  decompose the task of formal definition generation ¡  review of the existing approaches ¡  adaptation/creation ¡  implementation ¡  evaluation

�  explore non-taxonomic relation extraction ¡  feature analysis ¡  performance of 94% on a par with SemRep

�  suggest workflow for unsupervised relation extraction ¡  faster ¡  less resource dependent ¡  can be generalized to different domains and applications

QUESTIONS?

Thank you!

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T R I P L E E X T R A C T I O N

Back-up slides

Example 37

Abdominal Wall: the outer margins of the abdomen, extending from the osteocartilaginous thoracic cage to the pelvis. STEP #2: triple extraction “outer margins of the” (Abdominal wall, Abdomen) “that extends from the osteocartilaginous” (Abdominal wall, Thorax) “to the” (Abdominal wall, Pelvis)

Triple extraction steps 38

1.  separate the definition into head and body 2.  find the parent term, if there is one 3.  group coordinated concepts together 4.  organize concepts into concept pairs 5.  extract relational string for every pair 6.  detect negation

Triple extraction steps 39

�  separate the definition into head and body

Head: Abdominal wall Body: the outer margins of the abdomen… �  find the parent term, if there is one �  group coordinated concepts together �  organize concepts into concept pairs �  extract relational string for every pair �  detect negation

Triple extraction steps 40

�  separate the definition into head and body �  find the parent term, if there is one

“Cancer is a disease that…” è IS_A(Cancer, Disease)

�  group coordinated concepts together �  organize concepts into concept pairs �  extract relational string for every pair �  detect negation

Triple extraction steps 41

�  separate the definition into head and body �  find the parent term, if there is one

�  group coordinated concepts together

“X causes swelling and rashes” è causes(X, Swelling), causes(X, Rash)

�  organize concepts into concept pairs �  extract relational string for every pair �  detect negation

Triple extraction steps 42

�  separate the definition into head and body �  find the parent term, if there is one �  group coordinated concepts together �  organize concepts into concept pairs

�  extract relational string for every pair “that extends to the osteocartilaginous” (Abdominal wall, Thorax) �  detect negation

Triple extraction steps 43

�  separate the definition into head and body �  find the parent term, if there is one �  group coordinated concepts together �  organize concepts into concept pairs �  extract relational string for every pair

�  detect negation “that does not respond to the ordinary” (Refractory anemia, Treatment) è NEGATION

A N N O T A T I O N

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Back-up slides

Attribute Alignment Annotator 45

Problem # 1: missing annotations 46

Problem #2: ambiguity 47

D E T A I L S O F T H E A P P R O A C H

Back-up slides

Improvements since the last meeting 49

Old approach New approach

Text source MeSH definitions MEDLINE abstracts

Relation set R source SNOMED CT UMLS

Feature sources text of a definition text of a definition + concept types

Feature representations BoW, token and character ngrams, combination

character ngrams

Weighting schemes boolean, per-class weights boolean

Classification algorithm SVMs, Random Forests, Logistic Regression, Naïve Bayes

SVMs