1. 1 09/12/12 1 1 Abstraction Networks for Terminologies Yehoshua Perl Computer Science Dept. New Jersey Institute of Technology Newark, NJ 07102 USA yehoshua.perl@gmail.com

2. 2 09/12/12 Overview What are abstraction networks of terminologies? Characteristics of the abstraction networks Examples of abstraction network derived for UMLS, SNOMED CT and the MED Uses of abstraction networks in visual summarization, orientation, auditing and navigation of terminologies 09/12/122

3. 3 3 3 Motivation Terminologies are playing major roles in healthcare information systems. They are large, complex and difficult to maintain. Graphical displays are needed for better orientation to aid terminology use and maintenance. We have introduced abstraction networks as a way to support orientation.

4. 4 09/12/12 4 4 Nature of Abstraction Networks Most terminologies have a network structure, with a backbone of IS-A relationships. An abstraction network is a secondary network that provides a compact view of the structure and content of the primary terminology. – –Terminology Network Abstraction Network –

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7. 7 7 7 Derivation of Abstraction Networks Abstraction of a terminology is the process by which subsets of concepts are each replaced by a higher-level conceptual entity called a node. These nodes are interconnected by child-of hierarchical relationships. Terminology of Concepts Abstraction Network of Nodes Subset of concepts modeled by a node

8. 8 09/12/12 8 8 Abstraction Network Characteristics (1) Three characteristics –Disjointness –Derivation origin –Abstraction ratio Disjointness: Does an abstraction network divide the underlying terminology into disjoint parts? Disjoint abstraction network Intersection abstraction network

9. 9 09/12/12 Abstraction Network Characteristics (2) Derivation Origin: Are the nodes derived from the terminology (intrinsic) or are they formulated based on some external knowledge (extrinsic)? Abstraction ratio = Intrinsic derivationExtrinsic derivation # concepts of terminology # nodes of abstraction network

10. 10 09/12/12 1009/12/1210 Intersection Abstraction Network An abstraction network is disjoint if each concept of the terminology is mapped to a unique node. An abstraction network is an intersection abstraction network if some concepts belong to multiple nodes. Anatomical Abnormality Disease Dynamic subaortic stenosis

11. 11 09/12/12 1109/12/1211 More on Orientation An abstraction network offers a high-level view of the terminology for orientation into its content. The orientation problem has two facets –Orientation on the macro level to provide context for the content and structure of the whole terminology. –Orientation on the micro level into details of small portions of the terminology. Without an orientation on the macro level, it is difficult to obtain an orientation on the micro level due to lack of context. Abstraction networks provide macro level orientation.

12. 12 09/12/12 12 Example Abstraction Networks We cover abstraction networks for some known terminological systems. –UMLS –SNOMED CT –MED We describe the derivation for each example We categorize them according to the 3 characteristics above: Disjointness, source origin and abstraction ratio.

13. 13 09/12/12 An Abstraction Network for the UMLS Metathesaurus The two major knowledge sources of the UMLS –Metathesaurus (META) –The Semantic Network (SN) The META is a large repository of concepts compiled from more than 160 source vocabularies. Its 2011AB META release comprises about 8.6 million terms mapped into more than 2.6 million concepts. 09/12/1213

14. 14 09/12/12 Semantic Network Excerpt Anatomical Abnormality Physical Object Entity Event Conceptual Entity Organism Attribute Clinical Attribute Phenomenon or Process Injury or Poisoning Natural Phenomenon or Process Biology Function Pathologic Function Disease or Syndrome Cell or Molecular Dysfunction Experimental Model of Disease Mental or Behavioral Dysfunction Neoplastic Process Congenital Abnormality Acquired Abnormality Anatomical Structure Fully Formed Anatomical Structure

15. 15 09/12/12 Semantic Network SN consists of 133 semantic types (high- level categories). The SN is organized through IS-A hierarchical relationships in two trees rooted at Entity and Event, respectively. 09/12/1215

16. 16 09/12/12 Characteristics of the SN abstraction network The SN is an extrinsic abstraction network for META, since it is not derived from META. Each concept in META is assigned one or more of SN's semantic types. Thus, SN is an intersection abstraction network since a concept may be assigned multiple semantic types. SN exhibits an abstraction ratio of about 19,500:1. SN has been used in conjunction with the underlying META in a variety of applications. 95 papers returned by PUBMED for “Metathesaurus Semantic Network”. 09/12/1216

17. 17 09/12/12 17 Simple & Compound Semantics In the SN intersection abstraction network, concepts with a single category have a simple semantics. Concepts with multiple categories have a compound semantics, elaborated by the respective category combination. Concepts with compound semantics are complex since they are both “a this and a that”. Anatomical Abnormality Deformity Disease or Syndrome Eyelid Diseases Lacrimal Duct Obstruction Simple Compound

18. 18 09/12/12 Intersection of Semantic Types The extent of a Semantic Type S is the set of concepts assigned S. There are 73 concepts in the extent of Experimental Model of Disease (EMD) Experimental Model of Disease has an intersection with Neoplastic Process (NP) 09/12/1218 EMD EMD ∩ NP 26 NP

19. 19 09/12/12 Non-Uniform Semantics Within EMD’s extent, 26 concepts are both experimental models of disease and neoplastic processes, and 47 are only experimental models of disease. The non-uniformity of EMD semantic type extent makes it difficult to comprehend the extent of EMD. EMD (47) EMD ∩ NP (26)

20. 20 09/12/12 Refined Semantic Network (RSN) To address this non-uniformity, we introduced the “Refined Semantic Network” (“RSN”) [Gu, JAMIA 2000]. RSN comprises two kinds of types: pure semantic types and intersection types. The extent of a pure semantic type S is the subset of concepts assigned S, exclusively. The pure semantic type Experimental Model of Disease is assigned to the 47 concepts. 09/12/1220

21. 21 09/12/12 Intersection Types An intersection type is a reifications of a non-empty intersection of the extents of semantic types. Example: the RSN contains an intersection type EMD∩ NP with an extent of 26. 09/12/1221 EMD EMD ∩ NP 26 NP

22. 22 09/12/12 Acquired Abnormality Congenital Abnormality Anatomical Structure Neoplastic Process Mental or Behavioral Dysfunction Disease or Syndrome Physical Object Experimental Model of Disease Phenomenon or Process EntityEvent Natural Phenomenon or Process Human-caused Phenomenon or Process Acquired Abnormality  Disease or Syndrome Anatomical Abnormality  Disease or Syndrome Anatomical Abnormality Biologic Function Pathologic Function Congenital Abnormality  Disease or Syndrome Experimental Model of Disease  Neoplastic Process Natural Phenomenon or Process  Human-caused Phenomenon or Process Excerpt of the Refined Semantic Network Intersection Semantic Types

23. 23 09/12/12 Characteristics of the RSN The RSN is an intrinsic abstraction network derived automatically from the SN and its semantic-type assignments to the concepts of META. The RSN is a disjoint abstraction network. The RSN contains a total of 539 types, including 406 intersection types and 133 semantic types. The abstraction ratio of approximately 4,800:1. 09/12/1223

24. 24 09/12/12 RSN Properties RSN hierarchy is a directed acyclic graph (DAG) due to multiple parents of intersection types. RSN’s hierarchical depth is 11 as compared to depth 9 for SN. In the description of the first version of SN, McCray & Hole state: –“The current scope of the [Semantic] Network is quite broad, yet the depth is fairly shallow. –We expect to make future refinements and enhancements to the Network, based on actual use and experimentation.” Introduction of the RSN abstraction network is a step in direction planned. 09/12/1224

25. 25 09/12/12 Uses of RSN (1) The RSN has been proven an excellent vehicle for the support of UMLS auditing. The intersection types with very small extents (1-6 concepts) proved to have high likelihood of errors. Structural group auditing was introduced for extents of RSN [Chen, JBI 2009, JAMIA 2011] 09/12/1225

26. 26 09/12/12 Uses of RSN (2) RSN can aid in efficient navigation of the content of META. The “Chemical Specialty Semantic Network,” abstraction network is focused on the chemical concepts of the UMLS [Morrey, Cheminformatics 2012]. The RSN framework supports accurate modeling of complex and conjugate chemicals [Chen, JAMIA, 2009]

27. 27 09/12/12 Taxonomies for SNOMED CT Three related kinds of taxonomies have been formulated as abstraction networks for description- logic-based (DL) terminologies. They are the area taxonomy, the partial-area taxonomy, and the disjoint partial-area taxonomy. DL Terminologies examples: SNOMED CT and NCIt Taxonomies are also applicable for similarly modeled terminologies. –Convergent Medical Terminology (CMT )of Kaiser Permanente –Enterprise Reference Terminology (ERT) of the VA. 09/12/1227

28. 28 09/12/12 Area Taxonomy The nodes of the area taxonomy are derived from a partition of a terminology based on the relationships of its concepts. Concepts with the exact same relationships are grouped together into an area. In the area taxonomy, each area is a node. 09/12/1228 Morphology topography (3 concepts) Area morphology topography

29. 29 09/12/12 Area Taxonomy for Specimen 09/12/1229

30. 30 09/12/12 Area Taxonomy The area taxonomy is disjoint since each concept has a unique set of relationships. Areas are connected with links called child-of relationships. –A root is top-level concept in an area whose parents all reside in other areas. –There can be multiple root per area. 09/12/1230 B B A A child-of IS-A

31. 31 09/12/12 Partial-Area Taxonomy The partial-area taxonomy refines the area taxonomy by considering local hierarchical configurations within an area. A partial-area is a division of an area consisting of a root with all its descendants in the area. Each partial-area is a node within the area. The partial-area taxonomy is not disjoint. 09/12/1231 ABC A (4) B (6) C (3) Partial Area

32. 32 09/12/12 32 Partial-Area Taxonomy

33. 33 09/12/12 Summary Visualization A partial-area taxonomy refines the visualization of area taxonomy. For example, inside area {substance}, there are 11 white boxes, each with the name of the respective partial-area and the number of concepts. The name of the partial-area, after its root, represents the overarching semantics of the group. 09/12/1233

34. 34 09/12/12 Overlap of Partial Areas The partial-area taxonomy provides a summarization of the 102 concepts that only exhibit the substance relationship. The sum of the cardinalities of the four large partial-areas 137, is greater than the cardinality 102 of the entire area. This occurs due to the overlap among these four non-disjoint partial- areas. 09/12/1234

35. 35 09/12/12 Auditing Small Partial Areas In partial area taxonomy we see many small partial-areas of one or two concepts. As shown in [Halper, AMIA 2007], the partial- areas of very few concepts have a higher likelihood of concepts in error. The partial-area taxonomy visualization serves to enhance a framework for quality-assurance. 09/12/1235

36. 36 09/12/12 Overlaps of Partial Areas Concepts in multiple partial-area complicate the categorization of the partial-area taxonomy. In a given partial-area, some concepts belong solely to that partial- area elaborating the semantics of its root only, others belong to multiple partial-areas. We get a partition of the concepts of an area into disjoint partial-areas with no overlaps. 09/12/1236 disjoint partial-area ABC D Area A (3) B (5) C (3) D (1)

37. 37 09/12/12 Disjoint Partial Area Taxonomy A Disjoint Partial Area Taxonomy is a refinement of the partial-area taxonomy. The disjoint partial-areas are the nodes. These nodes are connected via child-of links, in a manner similar (but more complex) to that in a partial- area taxonomy. The partitioning is carried out in a recursive manner due to the potential of “hierarchical tangling” within the an area (see [Wang, JBI 2012]). 09/12/1237

38. 38 09/12/12 Excerpt of the disjoint partial-area taxonomy {substance} area 09/12/1238

39. 39 09/12/12 Better Orientation This figure illustrates how the disjoint partial-area taxonomy supports orientation to the most tangled parts of a SNOMED hierarchy, as area {substance} of the Specimen hierarchy. Six color-coded overlapping partial-areas are on Level 1. The overlaps among these six partial-areas are displayed utilizing combinations of their color coding. They are arranged in layers according to the number of overlapping partial-areas. 09/12/1239

40. 40 09/12/12 Orientation into a Tangled Hiercharchy There are 7 disjoint partial-areas inheriting from both partial-areas Body substance sample and Fluid sample with 30 concepts. The largest disjoint partial-area, Body fluid sample, has 15 concepts, which were counted twice before, once with respect to Body substance sample (55) and the other with respect to Fluid sample (44). The other six disjoint partial-areas (on Level 3) are overlaps of three partial-areas, where Blood specimen (25) is the third with 15 overlapping concepts counted three times in the partial-area taxonomy. By the arrangement of these 30 concepts into disjoint partial-areas, the figure gives a picture of their actual nature and respective grouping, with largest disjoint partial-area Acellular blood (serum or plasma) specimen (9). 09/12/1240

41. 41 09/12/12 Use in Auditing and Orientation In [Wang, JBI 2012], such overlapping concepts were shown to have a statistically significant higher ratio of errors. This taxonomy yields insights into the modeling of tangled portions of a hierarchy that can lead to improvements. 09/12/1241

42. 42 09/12/12 Taxonomies Characteristics All three of these abstraction networks are intrinsic as they are derived strictly from the terminology. The area taxonomy and disjoint partial-area taxonomy are disjoint. The partial-area taxonomy is not disjoint. The abstraction ratios for the area taxonomy and partial-area taxonomy are 58 (= 1,330 / 23) and 3.26 ( =1,330 / 407), respectively. For the disjoint partial-area taxonomy, the ratio is 2.73 (= 1,330 /487). 09/12/1242

43. 43 09/12/12 An Abstraction Network for the MED –In 2000, we presented an abstraction network for the Medical Entities Dictionary (MED) of Columbia –The group of all concepts with the same set of properties (i.e., attributes and relationships) is represented by a node with the same attributes and relationships. 09/12/1243 axax bxbx axax cxcx

44. 44 09/12/12 Root of a Node –A concept is a root of a given node if all its parent concepts do not belong to the node. –A child-of relationship is defined from node A to node B to reflect an IS-A relationship from the root concept of A to a concept in B. A root names the node since it generalizes all its concepts cr d r d 09/12/1244

45. 45 09/12/12 MED Abstraction Network Has 2 Kinds of Nodes The first kind, called a property-introduction node, has a unique root for which new properties are defined. The second kind, called an intersection node has multiple parents from different nodes. It inherits properties from each of its parents and thus has more properties than any single parent.

46. 46 09/12/12 46 Excerpt from MED Abstraction Network Medical Entity Anatomic Entity Sampleable Entity Measurable Entity Etiologic AgentDisease or Syndrome ICD9 Element Laboratory or Test Result Event Component CPMC Radiology Term Diagnostic Procedure Laboratory Results Abnormal Findings in Body Substances Number or String Result ICD9 (or CPT) Procedures Culture Results Smear Results ID Number Plus Text Results Date Result Quantity Result Numeric Result Restricted to Given Range of Values CPMC Electro- cardiograph Procedure Laboratory Diagnostic Procedure Chemical Antibiotics Single-Result Laboratory Test CPMC Laboratory Diagnostic Procedures Physical Anatomic Entity Water Cell Mental or Behavioral Dysfunction Coma Cardiac Dysrhythmia Microorganism Organisms Seen on Smear Radiology Event Component Orderable Tests ICD9 Diagnostic Procedure Microscopic Examination Image-Guided Interventional Procedure Calcified Body Part or Structure Abnormal Blood Hematology Anemia Hypoglycemia Adrenal Calcification 09/12/1246

47. 47 09/12/12 47 Deriving the MED Abstraction Network The abstraction network obtained is disjoint since descendants of more than one property- introduction root are defined to be concepts of a unique intersection node. A program to create such an abstraction network for a given terminology satisfying Cimino’s desiderata is given in [Liu, Distributed and Parallel Databases, 1999] 09/12/1247

48. 48 09/12/12 48 Properties of MED Abstraction Network For the MED, consisting of about 43,000 concepts (1996 version), the abstraction network contains 90 nodes; 53 introduction nodes and 37 intersection nodes. For the InterMED (a small offshoot of the MED of about 2,800 concepts), an abstraction network of 28 nodes was derived. The abstraction ratios for these two terminologies are respectively 478:1 and 89:1. The MED exhibits the characteristic of a unique introduction concept for each property. –Thus, the number of introduction nodes is bounded by the number of properties in the MED. 09/12/1248

49. 49 09/12/12 Abstraction Network from MED Excerpt 09/12/1249 Medical Entity Measurable Entity Specimen Etiologic Agent Disease or Syndrome ICD9 Element Laboratory or Test Result Pharmacy Item (Drug and Nondrug) Drug Enforcement Agency (DEA) Controlled Substance Category Number Or String Result Unknown and Unspecified Cause of Morbid or Mortality Diagnostic Procedure American Hospital Formulary Service Class Laboratory Diagnostic Procedure Antihistamine Drug Heart Disease Single-Result Laboratory Test CPMC Laboratory Diagnostic Procedure Sampleable Entity Calcified Pericardium Pancreatin Allen Serum Amylase Measurement Chemical Anatomical Structure

50. 50 09/12/12 Excerpt from MED Medical Entity Conceptual Entity Sampleable EntityMeasurable Entity Physical Object EventSpecimen Etiologic Agent Substance Anatomic Structure Orderable Entity Intellectual Product Patient Problem Intravascular Fluid Specimen Activity Classification Disease or Syndrome Finding Acquired Abnormality Chemical Serum Specimen Intravascular Chemistry Specimen Occupational Activity Pharmacy ConceptsICD9 ElementLaboratory or Test ResultLesion Chemical Viewed Structurally Serum Chemistry SpecimenHealth Care Activity Pharmacy Item (Drug and Nondrug) Drug Enforcement Agency (DEA) Controlled Substance Category ICD9 Disease Number Or String Result Calcified Body Part or Structure Organic Chemical Allen Serum Specimen Laboratory Procedure Diagnostic Procedure American Hospital Formulary Service Class CPMC Formulary Drug Item Disorder of Circulatory System Common In-Patient Diagnoses Diphenhydramine Amino Acid, Peptide or Protein Laboratory Diagnostic Procedure Antihistamine Drug Drug Enforcement Agency (DEA) Class 0 Cardiovascular Disease Enzyme Single-Result Laboratory Test CPMC Laboratory Diagnostic Procedure Heart Disease Amylase Single-Result Chemistry Test CPMC Chemistry Panels Diphenhydramine Preparation CPMC Drugs Benadryl 25 MG Cap Disease of Pericardium Disease of Pericardium, Other (ICD9) Calcified Pericardium Pancreatin Intravascular Chemistry Test Serum Chemistry Test Serum Amylase Test Serum Total Amylase Test Allen Serum Amylase Measurement Amylase Panels

51. 51 09/12/12 Excerpt from MED Abstraction Network Medical Entity Anatomic Entity Sampleable Entity Measurable Entity Etiologic AgentDisease or Syndrome ICD9 Element Laboratory or Test Result Event Component CPMC Radiology Term Diagnostic Procedure Laboratory Results Abnormal Findings in Body Substances Number or String Result ICD9 (or CPT) Procedures Culture Results Smear Results ID Number Plus Text Results Date Result Quantity Result Numeric Result Restricted to Given Range of Values CPMC Electro- cardiograph Procedure Laboratory Diagnostic Procedure Chemical Antibiotics Single-Result Laboratory Test CPMC Laboratory Diagnostic Procedures Physical Anatomic Entity Water Cell Mental or Behavioral Dysfunction Coma Cardiac Dysrhythmia Microorganism Organisms Seen on Smear Radiology Event Component Orderable Tests ICD9 Diagnostic Procedure Microscopic Examination Image-Guided Interventional Procedure Calcified Body Part or Structure Abnormal Blood Hematology Anemia Hypoglycemia Adrenal Calcification

52. 52 09/12/12 Uses of MED Abstraction Network The abstraction network serves to capture the essence of the MED while ignoring its minutiae. It helped to expose and repair some errors and inconsistencies in the MED [Gu, JAMIA 1999]. It can help in accelerating navigation of the terminology in the search for a concept, the name of which is unfamiliar or forgotten. –Like “drive on highways, switch to service road near destination.”

53. 53 09/12/12 Meta-Abstraction Networks The abstraction network may still be too large for a compact display on a computer screen. In such a case, it is possible to re-apply abstraction and create an abstraction network of an abstraction network, called a meta-abstraction network. 53 Terminology Abstraction Network Meta-abstraction Network

54. 54 09/12/12 Meta-Abstraction Networks Meta-abstraction networks are analogous to the meta-level networks found in data modeling and database systems. In the following, we discuss two such meta- abstraction structures defined with respect to the UMLS's Semantic Network (SN) –The cohesive metaschema [Perl, JBI 2003] –The semantic group collection of NLM [McCray, MEDINFO 2001].

55. 55 09/12/12 Discussion The notion of an abstraction network for a medical terminology was formulated. The features of abstraction networks were discussed. We presented examples of existing abstraction networks. The need for abstraction networks in terms of their support for comprehension, visualization, navigation, and maintenance of terminology content was illustrated.

56. 56 09/12/12 A Posteriori Derivation Schema DB An abstraction network is analogous to the notion of a database schema. A priori: All the previous examples were developed a posteriori from their underlying terminologies. A posteriori: Abstraction Network Terminology

57. 57 09/12/12 A Priori Design of Abstraction Networks for Terminologies Ideally, the abstraction network would be developed a priori to guide the design of a terminology similar to database design. We propose that terminology designers proceed in a top- down fashion of first creating an abstraction network for the desired terminology. We expect improved efficiency and correctness will occur. We hope that this NCBO webinar will motivate such future design approaches.

58. 58 09/12/12 Next Challenge in Abstraction Network Design The example abstraction networks illustrate various derivation techniques needed for different terminologies based on a variety of models. It can be tedious research work deriving new kinds of abstraction networks for each new kind of terminology encountered. The hope for more widespread use of abstraction networks lies in the standardization of their derivation. We saw same derivation technique for SNOMED and NCIt. If in the same way we identify families of terminologies that are similar in their properties and models, like these two DL terminologies, then we can probably devise a common technique for the automatic derivation of an abstraction network for each member of a family. The ontologies hosted in the NCBO Bioportal offer an opportunity for such design. We started with the OCRe ontology [Ochs, AMIA 2012]

59. 59 09/12/12 References MED Gu H, Cimino JJ, Halper M, Geller J, Perl Y. Utilizing OODB Schema Modeling for Vocabulary Management. In: Cimino JJ, editor. Proc. 1996 AMIA Annual Fall Symposium. Washington, DC; 1996. p. 274-278. Gu H, Halper M, Geller J, Perl Y. Benefits of an Object-Oriented Database Representation for Controlled Medical Terminologies. JAMIA. 1999 July/August;6(4):283-303. Liu L, Halper M, Gu H, Geller J, Perl Y. Modeling a Vocabulary in an Object-Oriented Database. In: Barker K, Ozsu MT, editors. CIKM-96, Proc. 5th Int'l Conference on Information and Knowledge Management. Rockville, MD; 1996. p. 179-188. Liu L, Halper M, Geller J, Perl Y. Controlled Vocabularies in OODBs: Modeling Issues and Implementation. Distributed and Parallel Databases. 1999 Jan;7(1):37-65. Liu L, Halper M, Geller J, Perl Y. Using OODB Modeling to Partition a Vocabulary into Structurally and Semantically Uniform Concept Groups. IEEE Trans Knowledge & Data Engineering. 2002 July/August;14(4):850-866.

60. 60 09/12/12 References UMLS Gu H, Perl Y, Geller J, Halper M, Liu L, Cimino JJ. Representing the UMLS as an OODB: Modeling Issues and Advantages. JAMIA. 2000 Jan/Feb;7(1):66.80. Selected for reprint in: R. Haux and C. Kulikowski, editors, Yearbook of Medical Informatics: Digital Libraries and Medicine (International Medical Informatics Association), pages 271-285, Schattauer, Stuttgart, Germany, 2001. Geller J, Gu H, Perl Y, Halper M. Semantic Refinement and Error Correction in Large Terminological Knowledge Bases. Data & Knowledge Engineering. 2003 Apr;45(1):1-32. Morrey CP, Perl Y, Halper M, Chen L, Gu H. A Chemical Specialty Semantic Network for the Unified Medical Language System. Journal of Cheminformatics. 2012 May;4(2). doi:10.1186/1758-2946-4-9. Gu H, Elhanan G, Perl Y, Hripcsak G, Cimino JJ, Xu J, et al. A Study of Terminology Auditors' Performance for UMLS Semantic Type Assignments. Journal of Biomedical Informatics (2012), http://-dx.doi.org/10.1016/j.jbi.2012.05.006 (in press). Gu H, Perl Y, Elhanan G, Min H, Zhang L, Peng Y. Auditing Concept Categorizations in the UMLS. Articial Intelligence in Medicine. 2004;31(1):29-44. Zhang L, Perl Y, Halper M, Geller J, Cimino JJ. An Enriched Unified Medical Language System Semantic Network with a Multiple Subsumption Hierarchy. JAMIA. 2004 May/June;11(3):195-206. Chen L, Morrey CP, Gu H, Halper M, Perl Y. Modeling multi-typed structurally viewed chemicals with the UMLS Refined Semantic Network. J Am Med Inform Assoc. 2009 Jan- Feb;16(1):116-31.

61. 61 09/12/12 References SNOMED-CT Wang Y, Halper M, Min H, Perl Y, Chen Y, Spackman KA. Structural Methodologies for Auditing SNOMED. Journal of Biomedical Informatics. 2007 Oct;40(5):561-581. Min H, Perl Y, Chen Y, Halper M, Geller J, Wang Y. Auditing as Part of the Terminology Design Life Cycle. JAMIA. 2006 November/December;13(6):676-690. Wang Y, Halper M, Wei D, Perl Y, Geller J. Abstraction of Complex Concepts with a Rened Partial-Area Taxonomy of SNOMED. Journal of Biomedical Informatics. 2012 Feb;45(1):15-29. Wang Y, Halper M,Wei D, Gu H, Perl Y, Xu J, et al. Auditing Complex Concepts of SNOMED using a Refined Hierarchical Abstraction Network. Journal of Biomedical Informatics. 2012 Feb;45(1):1-14. Halper M, Wang Y, Min H, Chen Y, Hripcsak G, Perl Y, et al. Analysis of Error Concentrations in SNOMED. In: Teich JM, Suermondt J, Hripcsak G, editors. Proc. 2007 AMIA Annual Symposium. Chicago, IL; 2007. p. 314-318. 09/12/1261

62. 62 09/12/12 References METASCHEMA Perl Y, Chen Z, Halper M, Geller J, Zhang L, Peng Y. The cohesive metaschema: A higher-level abstraction of the UMLS Semantic Network. Journal of Biomedical Informatics. 2003 Jun;35(3):194 - 212. McCray AT, Burgun A, Bodenreider O. Aggregating UMLS Semantic Types for Reducing Conceptual Complexity. In: Proc. Medinfo2001. London, UK; 2001. p. 171- 175. Zhang L, Perl Y, Halper M, Geller J, Hripcsak G. A Lexical Metaschema for the UMLS Semantic Network. Articial Intelligence in Medicine. 2005 Jan;33(1):41-59. Chen Y, Perl Y, Geller J, Hripcsak G, Zhang L. Comparing and Consolidating Two Heuristic Metaschemas. Journal of Biomedical Informatics. 2008 Apr;41(2):293-317. Zhang L, Perl Y, Halper M, Geller J. Designing Metaschemas for the UMLS Enriched Semantic Network. Journal of Biomedical Informatics. 2003 Dec;36(6):433-449. 09/12/1262

63. 63 09/12/12 Thank you

64. 64 09/12/12 Auxiliary Material on Meta Abstraction Networks

65. 65 09/12/12 Metaschema A metaschema comprises a collection of nodes, each a group of connected semantic types following some criterion. For the cohesive metaschema, the criterion is a set of semantic types with (almost) same relationships. –collection of disjoint, singly-rooted, connected sets called meta-semantic types. –Sets promoted to meta nodes to form the cohesive metaschema Anatomical Abnormality Congenital Abnormality Acquired Abnormality Anatomical Abnormality (3)

66. 66 09/12/12 The cohesive metaschema hierarchy.

67. 67 09/12/12 Semantic Groups A partition of the SN into disjoint groups was proposed based on six general principles: semantic validity (assessable by connectivity), parsimony, completeness, exclusivity, naturalness, and utility. Its application yielded a collection of 15 so-called “semantic groups” (“SGs”), each comprising a set of semantic types. The SGs form the nodes of a meta-abstraction structure that we call the SG collection. Example SGs include: Genes & Molecular Sequences (containing five semantic types), Activities & Behaviors (nine semantic types), Anatomy (11), and Chemicals & Drugs (26) (Some SG groups not connected in SN).

68. 68 09/12/12 Characteristics of META Abstraction Networks The SG collection is coarser-grained view of the Metathesaurus than SN, in an effort to reduce complexity. Both the cohesive metaschema and the SG collection are disjoint. SG is extrinsic, derived from the subject areas covered by the SN. The metaschema is intrinsic, derived from SN itself. The abstraction ratios-defined for the SN-are 5:1 for the metaschema and 9:1 for the SG network.