# Presentation: 101665

## Enriching and Designing Metaschemas for the UMLS Semantic Network 

- Department of  Computer Science
- New Jersey Institute of Technology

- Yehoshua Perl
- James Geller

## Problem 1

- Problem 1: the SN’s tree structure is restrictive since it does not allow multiple parents because each semantic type has at most one parent in the current SN.
- Example:Gene or Genome
- Current parent: **Fully Formed Anatomical Structure**
- Fact: **Gene or genome** is also a kind of **Molecular Sequence.**
- Result: this subsumption knowledge is omitted.

## Problem 1 (cont’d)

- Disadvantages:
- We have no direct access to the subsumption knowledge.
- We have difficulties in reasoning and decision making.
- The relationship modeling for **Gene or Genome** is limited, because it cannot inherit valid relationships from **Molecular Sequence.**

## Problem 2

- The SN is very complex, due to many relationships, making it difficult for user orientation.
- 135 semantic types
- 133 IS-A relationships
- About 7,000 semantic relationship occurrences
- It is difficult to gain knowledge from the picture of the SN.
- The following page shows about 1/4 of the SN with many relationships abbreviated by numbers.

## Slide 5

**Notes:**
Part of Event, Part of Entity, about 1⁄4 of SN
Show interactions between the two parts.

## Proposed Solutions

- For the problem of SN’s restrictive structure
- Expand the SN into a multiple subsumption structure with a Directed Acyclic Graph (DAG) hierarchy.
- Called the Enriched Semantic Network (ESN)
- Accommodates multiple inheritance of semantic relationships.
- For the problem of SN’s (ESN’s) comprehension
- Create a *Metaschema* as a higher-level abstraction of SN (do the same thing for ESN).
- The role of the Metaschema for the SN is similar to the role of the SN for the underlying META.

## Problem1: Expand the SN to the ESN

- Objective: Expand the SN from two trees to a DAG

-   Methods:
-  Identify viable IS-A links by imposing connectivity on a partition of the SN [McCray, Burgun, Bodenreider, MedInfo’01]
-  Identify viable IS-A links by string matching between semantic types’ names and definitions.

## Method 1: Imposing Connectivity

- [McCray, Burgun, Bodenreider, MedInfo’01] presented a partition of the SN consisting of 15 groups of semantic types.
- The partition is based on a semantic approach:
- externally identify subject areas
- place semantic types in areas
- Six principles for a partition are presented:
- One of them is *Semantic Validity*: the groups must be semantically coherent.

**Notes:**
Semantic Validity: Groups must be semantically coherent
Parsimony: the number of groups should be as small as possible
Completeness: The groups must cover the full domain
Exclusivity: each concept in the domain must belong to only one group
Naturalness: the groups must characterize the domain in a way that is acceptable to a domain expert
Utility: the groups must be useful for some purpose

## Semantic Validity

- Judging semantic validity:
- We check whether the types in a group are hierarchically related to each other (by IS-A links) to form a connected subgraph of the SN (“Connectivity Property”).
- Because the SN’s IS-A hierarchy consists of two trees, such a connected subgraph in the current SN must form a tree with a unique root.

## Semantic Validity (cont’d)

- Some groups are disconnected.
- They have multiple roots so that not all semantic types in the groups are subsumed under one category.
- E.g.:* Genes and Molecular Sequences** *group

## Identify IS-A based on Imposing Connectivity

- Step 1: Analyze disconnected groups in the partition.
- Step 2:
- Convert each disconnected group into a new  connected group (sometimes several connected groups).
- Identify viable IS-A links  during the conversion procedure.
- Present 4 kinds of transformations: IS-A addition, Root-addition, Split, and Root-moving.

**Notes:**
Based on the analysis, can we identify potentially additional IS-A links if we enforce connectivity property to those disconnected groups?

## Four Transformations

*(1) **“IS-A Addition” Transformation*
- Identify and add IS-A links to transform a disconnected group into a connected one.
*(2) **“Root-addition” Transformation*
- Create a new semantic type that will be an ancestor of all roots in the group.
- Disconnected group must have multiple roots, so we need to make these roots subsumed under one common category.
- Make the new semantic type a root of the new group by adding additional IS-A links to it from all roots in the group.

## (3) “Split” Transformation
Split a group into several smaller connected groups.
Each of the smaller groups is either a tree or can be transformed into a tree by using other transformations.

(4) “Root-moving” Transformation 
Find the lowest common ancestor of all roots of the disconnected group. 
Make this lowest common ancestor the root of the new group.

- Split a group into several smaller connected groups.
- Each of the smaller groups is either a tree or can be transformed into a tree by using other transformations.
*(4)* “Root-moving” Transformation
- Find the lowest common ancestor of all roots of the disconnected group.
- Make this lowest common ancestor the root of the new group.

_*Four Transformations (cont’d)*_

## Root-addition Transformation Example

_*Root-addition Transformation Example*_

**Notes:**
Body Location or Region, Body Space or Junction, Body System are in conceptual entity branch;
Body Substance, Anatomical Structure are in Physical Object branch.

##  We utilized the analysis of anatomy concepts of the Digital Anatomist Foundational Model (DAFM).

-  DAFM was developed at the U. of Washington [C. Rosse, et al. Amia ‘95, Jamia ‘98]

_*Root-addition Transformation Example*_

## Anatomical Entity Group

**Notes:**
Using the analysis of anatomical concepts in [Rosse01] , we found:
Body Location or Region and Body System have neither mass nor spatial dimension;
Body Space or Junction has spatial dimension but no mass
Body substance has mass but no spatial dimension
Add following IS-A links:
	Body Location or Region IS-A Conceptual Anatomical Entity
	Body System IS-A Conceptual Anatomical Entity
	Body Space or Junction IS-A Physical Anatomical Entity
	Body Substance IS-A Material Physical Anatomical Entity
	Anatomical Structure IS-A Material Physical Anatomical Entity
	Material Physical Anatomical Entity IS-A Physical Anatomical Entity
	Physical Anatomical Entity IS-A Physical Object
	Physical Anatomical Entity IS-A Anatomical Entity
	Conceptual Anatomical Entity IS-A Anatomical Entity
	Anatomical Entity IS-A Entity

Body Location or Region, Body Space or Junction are in Conceptual Entity tree, while Body System and Anatomical Structure are in Physical Object tree. Can not connect them under one semantic type in current tree structure.

## IS-A addition and Split Transformation Example

_*IS-A addition and Split Transformation Example*_

**Notes:**
Action: 
Split the group into three small groups:
Pathologic Function group;
Anatomical Abnormality group;
Finding Group
All three groups are trees, satisfying the connectivity property

Injury or Poisoning emphasizes more on the disorder result than on the process producing this disorder result.

Here, we identified a missing IS-A link from Injury or Poisoning to Disease or Syndrome by transformation 1

## Method 2: String Matching

- Definition (CP-pair): a pair (T1; T2) is a CP-pair if T1 is a child of T2
- Definition (String match):** **A string match from a semantic type T1 to another semantic type T2 is a triple (T1; T2; *S*) such that *S *is a string appearing both in the definition of T1 and in the name of T2. *S *is called the common string.
- In the definition, lexical normalization is used to convert adjectives and other formats to noun format.

## Observation

- Observation: among the 133 CP-pairs of semantic types, 88 have matches from children to their respective parents.
- If there is a match from one semantic type to another not connected by IS-A path, then it may imply an IS-A relationship between them.
- Method: Find string matches between any two semantic types having no IS-A path between them.

## Example

-  Three matches:
-  (Enzyme; Amino Acid, Peptide, or Protein; “protein”)
-  (Enzyme; Cell; “cell”)
-  (Enzyme; Cell Component; “cell”)
-  The match between Enzyme and Chemical is not considered, because Chemical is an ancestor of Enzyme in the SN.
-  Viable IS-A: Enzyme IS-A Amino Acid, Peptide, or Protein

_*Example*_

## Matching Results

- All matches were reviewed by a domain expert
- There are only a few valid matches that indicate new viable IS-A links (5):
- Enzyme IS-A Amino Acid, Peptide, or Protein
- Receptor IS-A Cell Component
- Vitamin IS-A Pharmacologic Substance
- Vitamin IS-A Organic Chemical
- Gene or Genome IS-A Molecular Sequence

## Slide 22

## ESN’s Relationship Structure

- ESN is different from SN:
- Allows semantic type to inherit more relationships from its new parent (“multiple inheritance”).
- Has 21 semantic types having multiple parents/ancestors
- Expands the relationship model of these 21 types
- ESN’s relationship structure:
- Preserves existing relationships in the SN (6,977)
- Includes new relationships inherited from new parents/ancestors

## Observations: New relationships come from the four new semantic types or semantic types having multiple parents or ancestors.
4 new semantic types, 12 new relationships for them
414 newly inherited relationships involving the 21 semantic types having multiple parents/ancestors.
Question: are all the 414 relationships valid? 
For each of the 21 semantic types, we checked the validity of the new relationships inherited from its new parent/ancestor.

- 4 new semantic types, 12 new relationships for them
- 414 newly inherited relationships involving the 21 semantic types having multiple parents/ancestors.
- Question: are all the 414 relationships valid?
- For each of the 21 semantic types, we checked the validity of the new relationships inherited from its new parent/ancestor.

_*Validity of Newly Inherited Relationships*_

## Validity Check Example

- For example:
- Injury or Poisoning has new parent Disease or Syndrome.
- It has 112 new relationships inherited from Disease or Syndrome.
- After review, 92 are valid and retained in the ESN, 20 are invalid and blocked in the ESN.

## Among the 414 newly inherited relationships, 314 are valid and inherited by 12 semantic types, 100 are invalid.
Only seven blockings suffice to prevent 100 invalid  relationships.
The ESN has 7,303 (6,977+12+314) relationship occurrences.
Among the 139 semantic types in the ESN, 16 (12+4 new) have different relationship structures.

- Only seven blockings suffice to prevent 100 invalid  relationships.
- The ESN has 7,303 (6,977+12+314) relationship occurrences.
- Among the 139 semantic types in the ESN, 16 (12+4 new) have different relationship structures.

_*ESN relationship Structure Summary*_

## ESN Summary

- ESN’s IS-A hierarchy:
- 139 semantic types, 150 IS-A links
- 21 semantic types have multiple parents/ancestors
- ESN’s relationship structure:
- 7,303 semantic relationship occurrences (5% more)

## Problem 2: SN/ESN’s comprehension

- The SN is still too hard to understand.
- There are 135 semantic types, 133 IS-A links
- About 7,000 semantic relationships (6977)
- Solution:
- Build a higher-level abstraction for the SN/ESN.
- Referred to as a *Metaschema*

## Metaschema

##  Metaschema Requirements and Derivation 

- Metaschema:
- A set of meta-semantic types (MSTs)
- Hierarchical *meta-child-of *relationships between MSTs
- Meta-relationships* *between MSTs
- A Metaschema of the SN (ESN) will represent a partition of the SN (ESN).

## Metaschema Requirements and Derivation (cont’d)

- Procedure to build metaschema:
- Step 1: Partition the SN (ESN) into disjoint semantic-type groups.
- Step 2: Define a meta-semantic type (MST) to represent each semantic-type group.
- Step 3: Derive hierarchical *meta-child-of* relationships between meta-semantic types.
- Step 4: Derive meta-relationships between meta-semantic types.

## Partition Example

## Metaschema example

_*Metaschema example*_

## Meta-relationship Example

- meta-relationship example

## ESN’s two metaschemas

- Q-metaschema (Qualified Metaschema)
- Basis: the partition of 19 disjoint semantic-type groups obtained when we expanded the SN to the ESN [Zhang, JBI 2003]
- C-metaschema (Cohesive Metaschema)
- Basis: cohesive partition which partitioned all semantic types exhibiting the same relationship set into one *semantic type group* [M. Halper, et al. Amia 2001][Perl JBI 2003]

## Q-metaschema hierarchy

## Q-metaschema including meta-relationships

_*Q-metaschema including meta-relationships*_

## C-metaschema hierarchy

_*C-metaschema hierarchy*_