1. The UMLS Semantic Network Support for semantic integration and reasoning Workshop UMLS Semantic Network NLM, NIH, Bethesda, 7-8 Apr 2005 Anita Burgun

2. Overview Semantic integration –Role of the SN –Integration of resources –Integration of data Reasoning –Reasoning with hierarchies –Reasoning with associative relations Perspectives Illustration –Genes, gene products, diseases –Findings, signs, diseases

3. Semantic integration 1- Role of ontologies

4. Integration DWH Patient files External resources SWISS PROT MED LINE ….. Data Warehouse Micro-array data GEN BANK Gene instances Ontologies Mediation system GOA Local res.

5. Integrating data in the domain of organ failure and transplantation Local Information Systems EfG transplantation REIN End stage renal failure EfG terminology server dialysis

6. T1 T2 T3 M A P I N G ONTO- TERM mapping term-term Semantic Network Metathesaurus

7. Semantic integration 2- Resource Integration

8. Integration DWH Patient files External resources SWISS PROT MED LINE ….. Data Warehouse Micro-array data GEN BANK Gene instances Ontologies Mediation system GOA Local res.

9. Introduction Increasing need for physicians and biologists to access information on the Internet Biomedical sources –Scattered –Multiple heterogeneity –Rapid evolution and frequent creation Integration Homo sapiens Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi; Mammalia; Eutheria; Primates; Catarrhini; Hominidae; Homo 1 2 PRI Homo sapiens Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi;Mammalia; Eutheria; Primates; Catarrhini; Hominidae; Homo. Homo sapiens Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi; Mammalia; Eutheria; Primates; Catarrhini; Hominidae; Homo 1 2 PRI

10. Objectives Overall: creating a system –Global access –Homogeneous and up-to-date information Specific: acquiring sources schemas –As automatically as possible –Dealing with updates, adding new resources –Generate different paths to access information

11. Sources schema Rarely available or hard to exploit No existing standard Identifying the schema of each source by exploiting its contents –Informs on the type of information present in the source –Extraction from its Web site

13. Use of UMLS Heterogeneity of schemas Need of a common vocabulary: the UMLS Example : finding the site of expression of a gene starting from a gene symbol

16. Results 279 distinct terms extracted from 11 sources –232 found in the UMLS corresponding to 495 MTH concepts 318 were correct 177 were not –47 not found Of the 318 MTH concepts, 60 concepts are common to at least 2 distinct extracted terms (158 are specific)

17. Semantic Type FrequencyExtracted termMT Concept Intellectual Product33Other Database Linksdatabases Qualitative Concept32Approved Gene SymbolApproved Functional Concept28BIOCHEMICAL FEATURESBiochemical Spatial Concept26Chromosomal LocationLocation Quantitative Concept20 Gross insertions & duplications Gross Gene or Genome17Related GenesGenes Nucleic Acid, Nucleoside, or Nucleotide14 Additional Gene cDNA sequence cDNA Biologically Active Substance13Nucleotide ProteinProteins Idea or Concept10Previously Approved Symbolssymbol Genetic Function9GENE FUNCTIONGene function, NOS Organism Attribute9relative lengthLength Temporal Concept9AliasesALIAS Amino Acid, Peptide, or Protein6 Name and origin of the protein Protein Indicator, Reagent, or Diagnostic Aid5Molecular reagentsReagents Occupation or Discipline5Nomenclature HistoryHistory Research Activity5CLONINGCloning Disease or Syndrome4Disorders & MutationsDisease Finding4 view Nucleotide Sequence4SNPs VariantsSNPs Occupational Activity4AbstractAbstracting

18. Mapping ULT to the UMLS General concepts –Citation -> Organism attribute –Description -> Research activity –Symbol -> Idea or Concept –Name -> Intellectual product –History -> Finding –Matches -> Manufactured object –Link -> Chemical Viewed Structurally –Association -> Mental Process/ Social Behavior

19. General concepts General classes « Metaterms » WordNet?? Upper Level Ontology General Ontology Domain Ontology Idea or Concept Intellectual Product Attributes Functional/Spatial/ Temporal Concept

20. Semantic integration 3- Data Integration

21. Functional genomics Post genomics Gene expression, protein function, biological process, disease van de Vijver MJ et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002 Dec 19; 347(25): 1999-2009. Objective : provide « medical » annotation of genes (BioMeKe) GeneTraces (Cantor, Lussier)

22. Gene, gene product, disease HUGO : manage heterogeneity of data Superoxide dismutase 1, soluble/ amyotrophic lateral sclerosis 1 (adult) C1420306SOD1 gene (symbol)gene or Genome C0669516SOD1 gene product (symbol) Amino acid, protein C0002736ALS (previous symbol)Disease or Syndrome No relation in MTH

23. Gene, gene product, disease HUGO Aconitase 1, soluble C1412126ACO1 gene (symbol)gene or Genome C0378502ACO1 protein (symbol)/ IRP 1 protein (alias) Amino acid, protein OR relation between the two concepts in MTH

24. Gene, gene product, disease HUGO synonymous terms T1T3T2 C2 C1C3 ST1 ST3 ST2

25. Gene, gene product, disease Gene or Genome AA, proteinDisease or Syndrome produceslocation_of affects causes

26. Reasoning

27. categorization SN relations

28. Reasoning : relations 1- The hierarchy and the economy principle

29. The economy principle R1. Ad hoc precision –The intent is to establish a set of semantic types, which will be useful for a variety of tasks without introducing undue complexity. The most specific semantic type in the semantic type hierarchy is assigned to the concept. R2. No hybrid types –Instead of creating a lattice structure, with hybrid types inheriting from two supertypes, the SN has a single inheritance tree structure. As a consequence, a Metathesaurus concept inheriting from two STs is assigned to both types. R3. No category “other” –Rather than proliferating the number of semantic types to encompass multiple additional subcategories, concepts that cannot be categorized by any sibling Semantic Type are simply assigned their common supertype.

30. The economy principle and the theory Intensions and extensions –Taxonomies (isa) are systems in which categories (intensions) are related to one another by means of subordination, or, in class parlance (extensions), systems in which classes are related to one another by means of class inclusion. Categories and classes –When a category K has subcategories K 1, K 2, …. K n, its extension, the class C K is the union of the classes for each of its subcategories, i.e. C K1, C K2,……C Kn.

31. Research Device Manufactured object used primarily in carrying out scientific research or experimentation Medical Device Manufactured object used primarily in the diagnosis, treatment, or prevention of physiologic or anatomic disorders Clinical Drug Pharmaceutical preparation as produced by the manufacturer Manufactured Object physical object made by human beings C MD C RD C CD C MO C MD  C RD  C CD Categories Classes  45 inch calibre bullet magnetic tape, matches, corridor

32. Reasoning : relations 2- Associative relations

33. Diseases and Findings Conceptual entity Finding Entity Event Sign or Symptom Disease or syndrome Pathologic function Natural phenomenon or process

34. Diseases and Findings: SN Finding Sign or SymptomDisease or syndrome Associated_with Evaluation_of Manifestation_of Diagnoses is_a

35. Relations SN Disease or Syndrome affects Disease or Syndrome Disease or Syndrome associated_with Disease or Syndrome Disease or Syndrome co-occurs_with Disease or Syndrome Disease or Syndrome complicates Disease or Syndrome Disease or Syndrome degree_of Disease or Syndrome Disease or Syndrome manifestation_of Disease or Syndrome Disease or Syndrome occurs_in Disease or Syndrome Disease or Syndrome precedes Disease or Syndrome Disease or Syndrome process_of Disease or Syndrome Disease or Syndrome result_of Disease or Syndrome

36. Relations in SNOMED CT vs SN Class A SNCT = SNCT concepts assigned to the Semantic Type A Class DISEASES SNCT = SNCT concepts assigned to ‘Diseases or Syndrome’ A MTH restricted to SNCT C B

37. Relations in SNOMED CT MTH restricted to SNOMED CT Relations whose SAB = SNOMED CT 2,220,144 relations 1,392,380 associative relations (including inverse relations) 113 associative relationships (all have inverse except associated_with) 18 relationships have less than 100 instances –Has_time_aspect_of : 1 –Has_property : 77 The most frequent : –Has_onset : 114,173 –has_finding_site : 99,156 –has_method : 70,682

38. Relations in SNOMED CT Focus on Diseases and Findings Class DISEASES SNCT = SNCT concepts assigned to ‘Disease or Syndrome’ Class FINDINGS SNCT = SNCT concepts assigned to {‘Finding’ + ‘Sign or Symptom’} Disease or Sd MTH restricted to SNCT Sign or symptom Finding

39. Diseases-Diseases relations SNCT due_to definitional_manifestation_of associated_with occurs_before mapped_to has_finding_site has_associated_finding interprets has_associated_morphology

40. Diseases-Diseases relations SNCT/SN due_to definitional_manifestation_of associated_with occurs_before mapped_to has_finding_site has_associated_finding interprets has_associated_morphology result_of manifestation_of associated_with precedes, occurs_in, complicates? co-occurs_with degree_of process_of affects SNCT SN

41. Findings-Diseases relations SNCT has_associated_finding / associated_finding_of has definitional manifestation/ definitional_manifestation_of interprets / is_interpreted_by/ has_interpretation occurs_after / occurs_before mapped_to /mapped from has_associated_morphology / associated_morphology_of due_to / cause_of focus_of has_finding_site isa / inverse is-a

42. Diseases-Findings relations SNCT/SN has_associated_finding / associated_finding_of has definitional manifestation/ definitional_manifestation_of interprets / is_interpreted_by/ has_interpretation occurs_after / occurs_before mapped_to /mapped from has_associated_morphology / associated_morphology_of due_to / cause_of focus_of has_finding_site isa / inverse is-a associated_with manifestation_of diagnoses evaluation_of SNCT SN

43. Diseases and Findings Finding Sign or SymptomDisease or syndrome Associated_with Evaluation_of Manifestation_of Diagnoses is_a Is_a 5,592 instances

44. Diseases and Findings Conceptual entity Finding Entity Event Sign or Symptom Disease or syndrome Pathologic function Natural phenomenon or process

45. Diseases and Findings Finding Sign or Symptom Disease or syndrome C0000727 Abdomen, acute is_a C1300028 Disorder characterized by pain

46. Diseases and Findings Finding Sign or Symptom Disease or syndrome C0008767 Scar has_finding_site C1300028 Endometriosis in scar of skin

47. Diseases and Findings Conceptual entity Finding Entity Event Sign or Symptom Disease or syndrome Pathologic function Natural phenomenon or process

48. Formal properties Guarino, Welty Rigidity –property that is essential to all the instances. Person (+R). Physician (not R). Identity –there is a property that is both necessary and sufficient for identifying an instance. Person (+I) Unity –instances are intrinsic wholes. Person (+U). Dependence –for all the instances x, necessarily some instance of Z must exist, which is not a part of x, nor a constituent of x (+D). Food (+D)

49. Formal properties Rules Rules –(not U) cannot subsume (+U) e.g., Substance cannot subsume Physical Object –[…] Distinction between roles and sortal types –Roles: (Not Rigid) (+Dependent) –Sortal types : (+Rigid) (Not Dependent)

50. Formal properties: signs Signs or Symptoms are Roles Metathesaurus concepts that are assigned only to roles with no sortal Semantic Type represent a numerous set of entities About 90% of the MTH concepts assigned to Findings, and Signs or Symptoms are not assigned to another Semantic Type.

51. Roles vs relations Findings? Sign or Symptom associated_with Disease or Syndrome Sign or Symptom diagnoses Disease or Syndrome Sign or Symptom evaluation_of Disease or Syndrome Sign or Symptom manifestation_of Disease or Syndrome Finding associated_with Disease or Syndrome Finding evaluation_of Disease or Syndrome Finding manifestation_of Disease or Syndrome

52. Diseases: frames Has_location Has_lesion : necrosis Has_process : infection –(has_agent) Has_discriminating_sign_or_finding –hematuria Occurs_in

53. Discussion

54. Perspectives (1) : coverage Extend the SN ??? –Economy principle vs adding general concepts Resource integration ??? –Needs in BIOmedical –Clarify conceptual entities –Semantic Types corresponding to general entities

55. Perspectives (2) : compatibility Compatibility with general ontologies Semantic web Alignment with existing domain ontologies FMA (Zhang Medinfo 2004) SNOMED CT (Burgun ongoing work on SN relations) Rules (classification), consistency SN-MTH E.g. sign or symptom is-a disease

56. Perspectives (3): formal aspects Formal ontology –Make relations and concepts more explicit, e.g. roles (ULO), relationships between genes and diseases –Cohérence, e.g. is-a relations between findings and diseases (studies and processing) –Classification of new concepts, e.g upper MTH concepts (Bodenreider Medinfo 2004) –Inference, e.g. use relations between anatomical sites and diseases to suggest new relations between diseases (Burgun submitted AMIA 2005)

57. References Mougin F, Burgun A, Loreal O, Le Beux P. Towards the automatic generation of biomedical sources schema. Medinfo. 2004;2004:783-7. Welty C, Guarino N. Supporting Ontological Analysis of Taxonomic Relationships (2001) Data Knowledge Engineering, http://www.ladseb.pd.cnr.it/infor/Ontology/Papers Zhang S, Bodenreider O. Comparing Associative Relationships among Equivalent Concepts Across Ontologies. Medinfo. 2004;2004:459-66.