James Geller

Area of research

Artificial Intelligence · Molecular Biology

Research interest

Research topics from publications: Utilizing a structural meta-ontology for family-based quality assurance of the BioPortal ontologies; Topological-Pattern-Based Recommendation of UMLS Concepts for National Cancer Institute Thesaurus; Auditing the assignments of top-level semantic types in the UMLS semantic network to UMLS concepts; Preliminary Analysis of Difficulty of Importing Pattern-Based Concepts into the National Cancer Institute Thesaurus; Extended Analysis of Topological-Pattern-Based Ontology Enrichment; Leveraging Horizontal Density Differences between Ontologies to Identify Missing Child Concepts: A Proof of Concept; Perceiving the Usefulness of the National Cancer Institute Metathesaurus for Enriching NCIt with Topological Patterns; Ontology enrichment using a large language model: Applying lexical, semantic, and knowledge network-based similarity for concept placement; Alternative classification of identical concepts in different terminologies: Different ways to view the world. Representative work: The National Cancer Institute Thesaurus (NCIt) is a reference terminology used to support clinical, translational and basic research as well as administrative activities. As medical knowledge evolves, concepts that might be missing from a particular needed subdomain are regularly added to the NCIt. However, terminology development is known to be labor-intensive and error-prone. Therefore, cost-effective semi-automated methods for identifying potentially missing concepts would be useful to terminology curators. Previously, we have developed a structural method leveraging the native term mappings of the Unified Medical Language System to identify potential concepts in several of its source voc The Unified Medical Language System (UMLS) is an important terminological system. By the policy of its curators, each concept of the UMLS should be assigned the most specific Semantic Types (STs) in the UMLS Semantic Network (SN). Hence, the Semantic Types of most UMLS concepts are assigned at or near the bottom (leaves) of the UMLS Semantic Network. While most ST assignments are correct, some errors do occur. Therefore, Quality Assurance efforts of UMLS curators for ST assignments should concentrate on automatically detected sets of UMLS concepts with higher error rates than random sets. In this paper, we investigate the assignments of top-level semantic types i