1. Knowledge-Based Semantic Interpretation for Summarizing Biomedical Text Thomas C. Rindflesch, Ph.D. Marcelo Fiszman, M.D., Ph.D. Halil Kilicoglu, M.S. National Library of Medicine Artificial General Intelligence Research Institute Workshop

2. Overview u Symbol grounding l Meaning consists of the manipulation of an internal system of relationships among concepts (Rapaport 1995) u Illustrate the viability of this approach l Semantic interpretation for biomedical research literature u Suggest that the system adumbrates intelligence l Provides the basis for reasoning about medical topics

3. Unified Medical Language System (UMLS) u Developed at the National Library of Medicine l Compilation of more than 100 terminologies in the biomedical domain u Two domain knowledge components l Metathesaurus: concepts l Semantic Network: relationships u Constitutes the “meaning” of medicine l Incomplete l Inconsistent l Useful

4. Metathesaurus u More than 1,000,000 concepts in biomedicine l Disorders l Organisms l Anatomy, physiologic functions l Drugs, procedures u Synonyms u Hierarchical structure u Each concept assigned semantic types (or categories)

5. Metathesaurus Concept Drug Therapy, Combination; Combination Chemotherapy; Polychemotherapy Therapeutic or Preventive Procedure Analytical, Diagnostic and Therapeutic Techniques and Equipment Therapeutics Drug Therapy

6. Metathesaurus Concept Mycoplasma pneumonia; Eatons agent pneumonia; Endemic pneumonia; et al. Disease or Syndrome Respiratory Tract Diseases Lung Diseases Pneumonia Pneumonia, Bacterial

7. Semantic Network u 134 semantic types l Disease or Syndrome l Therapeutic or Preventive Procedure l Pharmacologic Substance l Body Part, Organ, or Organ Component u In two hierarchies: l Entity, Event u 54 Relationships between semantic types Bacterium - CAUSES - Pathologic Function Pathologic Function - PROCESS_OF - Organism

8. affects functionally_related_to brings_about physically spatially temporally conceptually associated_with Semantic Network Predicates occurs_in

9. TREATS affects functionally_related_to brings_about physically spatially temporally conceptually associated_with Semantic Network Predicates CO-OCCURS_WITH PREVENTS OCCURS_IN CAUSES LOCATION_OF

10. affects functionally_related_to brings_about physically spatially temporally conceptually associated_with Semantic Network Predication occurs_in Occupational Activity Health Care Activity Therapeutic or Preventive Procedure Disease or Syndrome Biologic Function Pathologic Function treats

11. Semantic Interpretation: SemRep u Exploit the UMLS for processing medical text u Interpret (some of) the meaning asserted in language u Map words in language to concepts l Metathesaurus u Use syntactic structure to identify relationships between concepts l Semantic Network

12. SemRep Output Mycoplasma pneumonia is an infection of the lung caused by Mycoplasma pneumoniae. Mycoplasma Pneumonia ISA Infection Lung LOCATION_OF Infection Lung LOCATION_OF Mycoplasma Pneumonia Mycoplasma pneumoniae CAUSES Infection Mycoplasma pneumoniae CAUSES Mycoplasma Pneumonia

13. Related Research in Biomedicine u BioMedLEE, GENIES l Semantic grammar u AQUA l Definite clause grammar u MPLUS l Chart parser u MEDSYNDIKATE l Dependency grammar [Friedman, et al.] [Haug, et al.] [Johnson, Campbell] [Hahn, et al.]

14. u Lexical semantics l Contribution of words to interpretation u Meaning-text theory l Network of semantic predications l Syntax rules are interpretive devices u Ontological semantics l Applied interpretation l Ontology is the main metalanguage of meaning Semantics Framework [Mel’cuk] [Nirenburg & Raskin] [Cruse; Pustejovsky]

15. SPECIALIST Lexicon MetaMap Parser Metathesaurus SemRep: System Overview Semantic Network Construct Relation Medical Text MedPost Tagger Lexical Look-up Resolve Ambiguity Semantic Predication

16. 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.

17. SPECIALIST Lexicon Parser Syntactic Processing Text MedPost Tagger Lexical Look-up Resolve Ambiguity

18. 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.

19. MetaMap: Metathesaurus Concepts SPECIALIST Lexicon MetaMap Parser Metathesaurus Text MedPost Tagger Lexical Look-up Resolve Ambiguity

20. 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.

21. 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

22. Construct Predication MetaMap Parser Metathesaurus Semantic Network Construct Relation Medical Text MedPost Tagger Lexical Look-up Resolve Ambiguity Semantic Predication SPECIALIST Lexicon

23. Semantic Interpretation u Indicator rules l Establish a link between n Words in text n Predicates in the Semantic Network u Argument identification rules l Syntactic constraints u Interpretation of semantic predications l UMLS Semantic Network

24. Indicator Rules in preposition TREATS Hemofiltration in digoxin overdose in preposition HAS_LOCATION Severe infections in both feet Establish a correspondence between a syntactic item and a Semantic Network predicate Item Structure Semantic Network treatment TREATS Drugs for the treatment of schizophrenia nominalization

25. 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

26. 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

27. 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

28. 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. medd-TREATS-dsyn phsu-TREATS-dsyn topp-TREATS-dsyn topp-TREATS-inpo

29. 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. medd-TREATS-dsyn phsu-TREATS-dsyn topp-TREATS-dsyn topp-TREATS-inpo

30. 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

31. Manipulate Predications u Abstraction summarization on a given topic l Treatment of disease u Apply to predications from multiple documents u Devise summarization rules l Relevance: “Stick to the point” n Predications adhere to a schema for treatment of disease l Novelty: “Don’t tell me what I already know” n Eliminate arguments high in the UMLS hierarchy l Salience: “Give me the main points” n Eliminate low frequency predications [Hahn]

32. Summary Results u Search Medline l Limit to previous year: 294 citations u Summarize retrieved documents l Provide an informative overview u Further reasoning on the summarized predications is feasible