Language-Model-Based Architecture for Automatic Concept Placement in Ontologies

Abstract

Integrating newly emerging terms into existing ontologies is a recurring maintenance problem in knowledge engineering, particularly in biomedical domains where terminology evolves faster than manual curation can accommodate. This paper addresses the placement of concepts that are absent from the target ontology—the out-of-knowledge-base setting—in which a textual mention must be assigned one or more insertion positions in the subsumption hierarchy rather than linked to an existing node. We propose a three-stage framework that extends the conventional retrieve-then-select paradigm with an intermediate stage of edge generation and enrichment, which expands the candidate set by traversing the local structure of the ontology. Stage 1 retrieves candidate edges using a fine-tuned bi-encoder trained with a max-margin objective; Stage 2 constructs and structurally enriches candidate edges; Stage 3 selects among them using either a fine-tuned cross-encoder or a large language model under explainable instruction tuning. We evaluate on two datasets derived from SNOMED CT, MM-S14-Disease and MM-S14-CPP, under a strict out-of-knowledge-base protocol. Fine-tuned pre-trained language models outperform zero-shot and instruction-tuned large language models on ranking accuracy, while the instruction-tuned configuration produces expert-auditable justifications at a modest cost in accuracy. On MM-S14-Disease, the strongest configuration places a correct insertion edge among the ten highest-ranked candidates for 38.7% of test mentions and recovers the complete gold edge set for 16.4%, against 26.1% and 9.2% for retrieval alone. The framework is positioned as decision support for ontology curators rather than as an autonomous ontology generator.