# Presentation: 160434

##  The UMLS Semantic Network for Natural Language Processing Thomas C. Rindflesch, Ph.D.Lister Hill National Center for Biomedical Communications

**Workshop on the Future of ****the UMLS Semantic Network**

## Goal

- Sophisticated access to online information
- Supplement document retrieval with:
- Information extraction
- Automatic summarization
- Question answering
- Literature-based discovery
- Central concern of informatics research

## Challenge: Language Complexity

- The average age of participants (approximately 63 years), the predominance of women, and the high prevalence of comorbid conditions (for example, hypertension and cardiovascular disease) reflect typical characteristics of patients with osteoarthritis.

## Challenge: Language Complexity

- The average age of participants (approximately 63 years), the predominance of women, and the high prevalence of comorbid conditions (for example, hypertension and cardiovascular disease) reflect typical characteristics of patients with osteoarthritis.
-  Language encodes a lot of information

## Natural Language Processing

- Various approaches
- Correspond to levels of linguistic expression
- Words
- Phrases
- Relations

## Words

- The average age of participants (approximately 63 years), the predominance of women, and the high prevalence of comorbid conditions (for example, hypertension and cardiovascular disease) reflect typical characteristics of patients with osteoarthritis.

## Words

- ageapproximatelyaveragecardiovascularcharacteristicscomorbidconditionsdiseaseexamplehigh

- hypertensionosteoarthritisparticipantspatientspredominanceprevalencereflecttypicalwomenyears

## Phrases

- The average age of participants (approximately 63 years), the predominance of women, and the high prevalence of comorbid conditions (for example, hypertension and cardiovascular disease) reflect typical characteristics of patients with osteoarthritis.

## Phrases

- average ageparticipants approximately 63 years predominancewomenhigh prevalencecomorbid conditions

- examplehypertension cardiovascular diseasetypical characteristics patients osteoarthritis

## Semantic Predications

- The average age of participants (approximately 63 years), the predominance of women, and the high prevalence of comorbid conditions (for example, hypertension and cardiovascular disease) reflect typical characteristics of patients with osteoarthritis.

## Semantic Predications

- The average age of participants (approximately 63 years), the predominance of women, and the high prevalence of comorbid conditions (for example, hypertension and cardiovascular disease) reflect typical characteristics of patients with osteoarthritis.

## Semantic Predications

- Cardiovascular DiseasesCO-OCCURS_WITHDegenerative polyarthritis
- HypertensionCO-OCCURS_WITHDegenerative polyarthritis

## Semantic Interpretation

- Map syntactic structures to structured domain knowledge
- Concepts
- Relations
- Output is semantic predication
- Arguments and a predicate in a relationship
- Supports enhanced access to online information

## Related Research in Biomedicine

- MedLEE, GENIES
- Semantic grammar
- AQUA
- Definite clause grammar
- MPLUS
- Chart parser
- MEDSYNDIKATE
- Dependency grammar

- [Friedman, et al.]

- [Haug, et al.]

- [Johnson, Campbell]

- [Hahn, et al.]

## SemRep

- Interpret semantic predications in Medline
- Exploit the UMLS
- Concepts: Metathesaurus
- Relations: Semantic Network
- Syntax: SPECIALIST Lexicon
- Use other resources at NLM
- MetaMap
- UMLS Knowledge Source Server

## Minimal Commitment Approach

- Focused processing
- Syntax
- Semantics
- Incremental development
- Useful results

## Theoretical Framework

- Lexical semantics
- Meaning-text theory
- Ontological semantics
- Ontology is the main metalanguage of meaning
- Dependency grammar
- Corepresentational grammar

- [Mel’cuk]

- [Nirenburg; Raskin]

- [Cruse; Pustejovsky]

- [Tesnière; Sleator]

- [Kac]

## SemRep: System Overview

- SemRep: System Overview

- MedicalText

## SemRep Processing

## Input

- MedicalText

## Input

- The aim of this study was the characterization of the specific effects of alprazolam versus imipramine in the treatment of panic disorder with agoraphobia and the delineation of dose-response and possible plasma level-response relationships.

## Syntactic Processing

- Parser

- Text

- MedPost
- Tagger

- Lexical
- Look-up

## Syntactic Processing

- The aim of this study was the characterization of the specific effects NP[of alprazolam] [versus] NP[imipramine] NP[in the treatment]Nominalization NP[of panic disorder] NP[with Agoraphobia] and the delineation of dose-response and possible plasma level-response relationships.

## MetaMap: Metathesaurus Concepts

## MetaMap: Metathesaurus Concepts

- The aim of this study was the characterization of the specific effects NP[of Alprazolam] [versus] NP[Imipramine] NP[in treatment]Nominalization NP[of Panic Disorder] NP[with Agoraphobia] and the delineation of dose-response and possible plasma level-response relationships.

## Semantic Types

- The aim of this study was the characterization of the specific effects NP[of phsu] [versus] NP[phsu]  NP[in treatment]Nominalization NP[of dsyn] NP[with dsyn] and the delineation of dose-response and possible plasma level response relationships.

- Pharmacologic Substance

- Disease or Syndrome

## Construct Relation

## Semantic Interpretation

- Indicator rules
- Establish a link between words and predicates in the Semantic Network
- Argument identification rules
- Syntactic constraints
- Validation of semantic predications
- Semantic Network

**Notes:**
System overview

Minimize syntax and pre-MetaMap processing
Minimize details; just get across the general idea
Enhance how the characteristics of the Semantic Network are involved

## Semantic Network Predicates

- functionally_related_to

- brings_about

- physically

- spatially

- temporally

- conceptually

- associated_with

- occurs_in

## Core SemRep Predicates

- affects

- functionally_related_to

- brings_about

- physically

- spatially

- temporally

- conceptually

- associated_with

- CO-OCCURS_WITH

- PREVENTS

- OCCURS_IN

- CAUSES

- LOCATION_OF

## Semantic Network Predication

- functionally_related_to

- brings_about

- physically

- spatially

- temporally

- conceptually

- associated_with

- occurs_in

- treats

## Indicator Rules: Overview

-  Establish a correspondence between a syntactic item and
-  a Semantic Network predicate

## Semantic Types

- The aim of this study was the characterization of the specific effects NP[of phsu] [versus] NP[phsu]  NP[in treatment]Nominalization NP[of dsyn] NP[with dsyn] and the delineation of dose-response and possible plasma level response relationships.

- Pharmacologic Substance

- Disease or Syndrome

## Apply Indicator Rule

- The aim of this study was the characterization of the specific effects NP[of phsu] [versus] NP[phsu]  NP[in treatment]Nominalization NP[of dsyn] NP[with dsyn] and the delineation of dose-response and possible plasma level response relationships.

- TREATS

## Argument Constraints

- The aim of this study was the characterization of the specific effects NP[of phsu] [versus] NP[phsu]  NP[in treatment]Nominalization NP[of dsyn] NP[with dsyn] and the delineation of dose-response and possible plasma level response relationships.

- TREATS

## Semantic Network Predication

- The aim of this study was the characterization of the specific effects NP[of phsu] [versus] NP[phsu]  NP[in treatment]Nominalization NP[of dsyn] NP[with dsyn] and the delineation of dose-response and possible plasma level response relationships.

## Match Semantic Types

- The aim of this study was the characterization of the specific effects NP[of phsu] [versus] NP[phsu]  NP[in treatment]Nominalization NP[of dsyn] NP[with dsyn] and the delineation of dose-response and possible plasma level response relationships.

## Substitute Concepts

- The aim of this study was the characterization of the specific effects NP[of phsu] [versus] NP[Alprazolam]  NP[in treatment]Nominalization NP[of Panic Disorder] NP[with dsyn] and the delineation of dose-response and possible plasma level response relationships.

- Alprazolam-TREATS-Panic Disorder

## Evaluation

- Developing a test collection
- 2,000 sentences from MEDLINE
- Mainly drug therapies
- TREATS, OCCURS_IN, LOCATION_OF, ISA
- Preliminary results
- TREATS: 49% recall, 78% precision
- ISA: 83% precision

## Applications

- Automatic summarization
- Marcelo Fiszman
- Machine translation
- Halil Kilicoglu
- Discovery
- Information extraction in genomics
- Bisharah Libbus
- Question answering
- Dina Demner-Fushman

## Semantic Medline

- PubMed

- Semantic Processing

## SemRep Processing

- [head(“Sumatriptan” phsu)]NP [prep(for),head(“Migraine” dsyn)]NP

- [head(sumatriptan )]NP [prep(for),head(migraine)]NP

## Automatic Summarization

- PubMed search with query “migraine”
- Retain 500 most recent citations
- Process with SemRep
- Summarize SemRep output
- Condense list of predications
- Visualize results (Halil Kilicoglu)
- Translate summarized results (using MeSH)

## Slide 44

## Slide 45

## Summarization for Discovery

- Investigate “unexpected” connections
- PubMed search with
- sleep AND gastrointestinal
- Run SemRep and summarize on
- Gastroesophageal reflux disease

## Slide 47

## Summarization for Discovery

- Investigate “unexpected” connections
- PubMed search with
- sleep AND gastrointestinal
- Run SemRep and summarize on
- Gastroesophageal reflux disease
- New PubMed search on “cpap AND gerd”

## Slide 49

## Slide 50

## Slide 51

## Semantic Network for NLP

- Very useful as is
- Issues noted while developing SemRep
- Missing relations
- Infelicitous relations
- Semantic type hierarchy
- Recommendations for development
- Theory and practice
- Incremental development
- Maintenance

## Missing Relations

- Genomics
- “Genes” CAUSE “Disease”
- “Genes” INTERACT_WITH “Genes”
- Treatment
- “Intervention” TREAT “Patients” / “Organism”
- Donepezil for patients with Alzheimer’s

## Semantic Type Hierarchy

- Groups and group members (organisms)
- dsyn OCCURS_IN podg
- Adults with acidosis
- dsyn PROCESS_OF mamm
- Dogs with acidosis

## Infelicitous Relations

- OCCURS_IN
- Dementia OCCURS_IN Adults
- Dementia OCCURS_IN Alzheimer’s disease

## Recommendations

- Create an “ontologically-oriented” Semantic Network
- Developed incrementally
- Loosely tied to the normal UMLS production cycle

## Conclusion

- The Semantic Network (with the Metathesaurus and Lexicon)
- Supports semantic interpretation of medical text
- Enables innovative applications in cooperation with PubMed
- Provides enhanced access to information in Medline