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Abstraction Networks for Terminologies
Yehoshua PerlComputer Science Dept.
New Jersey Institute of TechnologyNewark, NJ 07102 USA
209/12/12
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
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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.
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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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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
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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
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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 derivation Extrinsic derivation
# concepts of terminology
# nodes of abstraction network
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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.
AnatomicalAbnormality Disease
Dynamic subaortic stenosis
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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.
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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.
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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.
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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
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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.
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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”.
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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”.
AnatomicalAbnormality
Deformity
Disease or Syndrome
Eyelid Diseases
Lacrimal Duct Obstruction
Simple Simple
Compound
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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)
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EMD EMD ∩ NP 26
NP
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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)
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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.
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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.
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EMD EMD ∩ NP 26
NP
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Acquired Abnormality
Congenital Abnormality
Anatomical Structure
Neoplastic Process
Mental or Behavioral
Dysfunction
Disease or Syndrome
Physical Object
Experimental Model of Disease
Phenomenon or Process
Entity Event
Natural Phenomenon
or Process
Human-caused Phenomenon or
Process
AcquiredAbnormality Disease or Syndrome
Anatomical Abnormality
Disease or Syndrome
Anatomical Abnormality
Biologic Function
Pathologic Function
Congenital Abnormality Disease
or Syndrome
Experimental Model of Disease
∩NeoplasticProcess
Natural Phenomenonor Process ∩Human-caused Phenomenon
or Process
Excerpt of the Refined Semantic Network
IntersectionSemantic Types
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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.
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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.
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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]
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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]
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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.
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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. 09/12/12 28
Morphology topography (3 concepts)
Areamorphology topography
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Area Taxonomy for Specimen
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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.
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B B
AA
child-of IS-A
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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.
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A B CA
(4)B
(6)C
(3)
Partial Area
Area
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Partial-Area Taxonomy
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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.
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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.
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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.
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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.
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disjoint partial-areaA B C
D
Area A(3)
B(5)
C(3)
D(1)
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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]).
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Excerpt of the disjoint partial-area taxonomy {substance} area
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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.
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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).
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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.
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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).
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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.
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ax
bx
a
x
cx
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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
c
r
d
r
d
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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.
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Excerpt from MED Abstraction NetworkMedical Entity
Anatomic Entity
Sampleable Entity
Measurable Entity
Etiologic Agent Disease or SyndromeICD9 Element Laboratory or Test Result
Event Component
CPMC Radiology Term
Diagnostic Procedure
LaboratoryResults
Abnormal Findings in Body Substances
Number orString Result
ICD9 (or CPT)Procedures
CultureResults
SmearResults
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
ComaCardiac Dysrhythmia
Microorganism
Organisms Seen on Smear
Radiology Event
Component
Orderable Tests
ICD9 Diagnostic Procedure
Microscopic Examination
Image-Guided Interventional
ProcedureCalcified Body Part or Structure
Abnormal Blood Hematology
Anemia
Hypoglycemia
Adrenal Calcification09/12/12 46
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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]
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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.
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Abstraction Network from MED Excerpt
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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 OrString Result
Unknown and Unspecified Cause of Morbid or Mortality
DiagnosticProcedure
American HospitalFormulary
Service Class
Laboratory DiagnosticProcedure
Antihistamine Drug
Heart Disease
Single-ResultLaboratory Test
CPMC Laboratory Diagnostic Procedure
Sampleable Entity
Calcified Pericardium
Pancreatin
Allen Serum Amylase Measurement
Chemical
Anatomical Structure
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Excerpt from MEDMedical Entity
Conceptual Entity Sampleable Entity Measurable EntityPhysical Object Event SpecimenEtiologic Agent
SubstanceAnatomic StructureOrderable 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 Concepts ICD9 Element Laboratory or Test Result Lesion Chemical Viewed Structurally Serum Chemistry Specimen Health Care Activity
Pharmacy Item(Drug and Nondrug)
Drug Enforcement Agency (DEA)
Controlled Substance Category
ICD9 Disease Number OrString Result
Calcified Body Part or Structure
Organic Chemical
Allen SerumSpecimen
LaboratoryProcedure
DiagnosticProcedure
American HospitalFormulary
Service Class
CPMC Formulary Drug Item
Disorder ofCirculatory System
Common In-PatientDiagnoses
Diphenhydramine Amino Acid,Peptide or Protein
Laboratory DiagnosticProcedure
Antihistamine DrugDrug Enforcement
Agency (DEA) Class 0
Cardiovascular DiseaseEnzyme
Single-ResultLaboratory Test
CPMC Laboratory Diagnostic Procedure
Heart DiseaseAmylase
Single-Result Chemistry Test CPMC Chemistry Panels
DiphenhydraminePreparation
CPMC DrugsBenadryl 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
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Excerpt from MED Abstraction NetworkMedical Entity
Anatomic Entity
Sampleable Entity
Measurable Entity
Etiologic Agent Disease or SyndromeICD9 Element Laboratory or Test Result
Event Component
CPMC Radiology Term
Diagnostic Procedure
LaboratoryResults
Abnormal Findings in Body Substances
Number orString Result ICD9 (or CPT)
Procedures
CultureResults
SmearResults
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
ComaCardiac Dysrhythmia
Microorganism
Organisms Seen on Smear
Radiology Event
Component
Orderable Tests
ICD9 Diagnostic Procedure
Microscopic Examination
Image-Guided Interventional
ProcedureCalcified Body Part or Structure
Abnormal Blood Hematology
Anemia
Hypoglycemia
Adrenal Calcification
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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.”
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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.
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Terminology Abstraction Network Meta-abstraction Network
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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].
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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.
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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
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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.
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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]
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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.
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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.
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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.
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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.
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Thank you
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Auxiliary Material on Meta Abstraction Networks
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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)
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The cohesive metaschema hierarchy.
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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).
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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.
