The Enriched Semantic Network (ESN) has previously been presented as an enhancement of the Semantic Network (SN) of the UMLS. The ESN's hierarchy is a DAG (Directed Acyclic Graph) structure allowing for multiple parents. The ESN is thus more complex than the SN and can be more difficult to view and comprehend. We have previously introduced the notion of a metaschema for the SN as a compact abstraction to support SN comprehension. We extend the definition of metaschema to make it applicable to a DAG classification hierarchy, such as the one exhibited by the ESN. We specify the requirements for and describe the general process of deriving such a metaschema. We derive two particular metaschemas of the ESN based on a pair of partitions. These two metaschemas and their underlying partitions are compared. Both metaschemas serve as compact representations of the ESN, allowing for convenient viewing of its hierarchy and easier comprehension.

1 SNOMED is one of the leading health care terminologies being used worldwide. As such, quality assurance is an important part of its maintenance cycle. Methodologies for auditing SNOMED based on structural aspects of its organization are presented. In particular, automated techniques for partitioning SNOMED into smaller groups of concepts based primarily on relationships patterns are defined. Two abstraction networks, the area taxonomy and p-area taxonomy, are derived from the partitions. The high-level views afforded by these abstraction networks form the basis for systematic auditing. The networks tend to highlight errors that manifest themselves as irregularities at the abstract level. They also support group-based auditing, where sets of purportedly similar concepts are focused on for review. The auditing methodologies are demonstrated on one of SNOMED’s top-level hierarchies. Errors discovered during the auditing process are reported.

The art and science of building ontologies have been developed to the point where it is not sufficient anymore to design and implement a new ontology. Rather, one needs to follow the process of building an ontology by evaluating its quality in absolute numeric terms. If another ontology in the same domain exists, then the two ontologies should be compared in a quantitative manner to determine which one of them is better. Furthermore, the quality scoring mechanism should provide clues concerning the sections of the ontology (one or both) that need improvement. Ontologies are complex structures which exist in many different variations. Even after imposing a basic structural framework and choosing a domain, two ontologies may be evaluated with respect to a number of different features. In this paper we will concentrate on one single ontology feature and assume that all other features are fixed. We have developed a mechanism to measure the quality of this ontology feature, preferred term(s) based on the concept of naturalness, and show that it agrees very well with human judgments. Thus we provide an approach towards the principled selection of the preferred terms in an ontology.

In previous work, we have modeled a vocabulary given as a semantic network by an OODB (Object-Oriented Database). The OODB schema thus obtained provides a compact abstract view of the vocabulary. This enables fast traversal of the vocabulary by a user. In the semantic network vocabulary, the IS-A relationships express the specialization hierarchy. In our OODB modeling of the vocabulary, the SUBCLASS relationship expresses the specialization hierarchy of the classes and supports the inheritance of their properties. A typical IS-A path in the vocabulary has a corresponding shorter SUBCLASS path in the OODB schema.