Biomedical subjects

Yehoshua Perl

Publications and source records attributed to Yehoshua Perl.

6 recordsLinked to original sources

PubMed2002

The cohesive metaschema: a higher-level abstraction of the UMLS Semantic Network.

The Unified Medical Language System (UMLS) joins together a group of established medical terminologies in a unified knowledge representation framework. Two major resources of the UMLS are its Metathesaurus, containing a large number of concepts, and the Semantic Network (SN), containing semantic types and forming an abstraction of the Metathesaurus. However, the SN itself is large and complex and may still be difficult to view and comprehend. Our structural partitioning technique partitions the SN into structurally uniform sets of semantic types based on the distribution of the relationships within the SN. An enhancement of the structural partition results in cohesive, singly rooted sets of semantic types. Each such set is named after its root which represents the common nature of the group. These sets of semantic types are represented by higher-level components called metasemantic types. A network, called a metaschema, which consists of the meta-semantic types connected by hierarchical and semantic relationships is obtained and provides an abstract view supporting orientation to the SN. The metaschema is utilized to audit the UMLS classifications. We present a set of graphical views of the SN based on the metaschema to help in user orientation to the SN. A study compares the cohesive metaschema to metaschemas derived semantically by UMLS experts.

Algorithms↗

PubMed2002

Partitioning the UMLS semantic network.

The unified medical language system (UMLS) integrates many well-established biomedical terminologies. The UMLS semantic network (SN) can help orient users to the vast knowledge content of the UMLS Metathesaurus (META) via its abstract conceptual view. However, the SN itself is large and complex and may still be difficult to comprehend. Our technique partitions the SN into smaller meaningful units amenable to display on limited-sized computer screens. The basis for the partitioning is the distribution of the relationships within the SN. Three rules are applied to transform the original partition into a second more cohesive partition.

Algorithms↗

PubMed2002

Evaluation and application of a semantic network partition.

Semantic networks (SNs) are excellent knowledge representation structures. However, large semantic networks are hard to comprehend. To overcome this difficulty, several methods of partitioning have been developed that rely on different mixes of structural and semantic methods. However, little has appeared in the literature concerning the question whether a partition of a semantic network creates subnetworks that agree with human insight. We address this issue by presenting a comparison between the results of an algorithmic partitioning method and a partition created by a group of experts. Subsequently, we show how a network partition can be used to generate various partial views of a semantic network, which facilitate user orientation. Examples from the Unified Medical Language System (UMLS) SN are used to demonstrate partial views.

Algorithms↗

PubMed2002

Using the metaschema to audit UMLS classification errors.

The Unified Medical Language System integrates about 800,000 concepts from 99 biomedical terminologies. Each concept is assigned to at least one semantic type of the Semantic Network. During the integration, it is unavoidable that some classification errors and inconsistencies will be introduced. In this paper, we present an auditing technique to find such errors and inconsistencies. Our technique is based on an expert reviewing the pure intersections of meta-semantic types of the metaschema, a compact abstract view of the Semantic Network. Results regarding the pure intersections are reported. The analysis results for pure intersections with 1 to 6 concepts are presented. Various kinds of errors are identified.

Semantics↗

PubMed2002

Auditing the UMLS for redundant classifications.

The UMLS's Semantic Network (SN) serves as a valuable abstraction for the underlying concept repository called the Metathesaurus (META). Specifically, the SN forms a classification layer for the META, with each of the META's constituent concepts assigned to one or more semantic types in the SN. The rule in the design of the SN is to have concepts explicitly assigned to the lowest possible semantic types in the SN's IS-A hierarchy. Implicit assignment to higher semantic types can be inferred via the IS-A relationships. However, in subsequent versions of the UMLS, unnecessary, simultaneous assignments to descendant and ancestor semantic types have been discovered (e.g., 8,622 in the UMLS 1998 version and 12,657 in the 2001 version). The assignment of concepts to such ancestor semantic types is called redundant classification. There is a need for an automated auditing tool that can identify all these redundant classifications. In this paper, an efficient algorithm for this auditing task is introduced. Details of its application to the current (2001) version of the UMLS are presented and the results are discussed.

Algorithms↗

PubMed2002

Enriching the structure of the UMLS semantic network.

The Unified Medical Language System's (UMLS's) Semantic Network (SN)---consisting of a network of semantic types---has a two-tree structure, where each semantic type has at most one parent semantic type. This arrangement is restrictive because some semantic types are, by their definition, specializations of several parents. As a proposed enhancement to the SN, its semantic types have previously been partitioned into groups, each of which contains semantic types of some specific area. However, some groups of this proposed partition contain forest (i.e., multiple-tree) structures or even isolated semantic types. Both situations imply a disconnected internal structure. Connectivity is actually one way to assess the proposed "semantic validity" principle for partitions. It is a desired, although not required, property. In this paper, we introduce a methodology for identifying "missing" IS-A links and adding them to the SN. This process transforms the SN into a Directed Acyclic Graph (DAG) structure, with semantic types permitted to have multiple parents. A result of our methodology is the transformation of the proposed SN partition into groups satisfying the connectivity property.

Semantics↗