A large volume of XML data is produced in experiments carried out by robots in laboratories. In order to support the interoperability of data between labs, there is a motivation to translate the XML data into a knowledge graph. A key stage of this process is the enrichment of the XML schema to lay the foundation of an ontology schema. To achieve this, we present the RELRaE framework, a framework that employs large language models in different stages to extract and accurately label the relationships implicitly present in the XML schema. We investigate the capability of LLMs to accurately generate these labels and then evaluate them. Our work demonstrates that LLMs can be effectively used to support the generation of relationship labels in the context of lab automation, and that they can play a valuable role within semi-automatic ontology generation frameworks more generally.

The Metadata Object Description Schema (MODS) was developed to describe bibliographic concepts and metadata and is maintained by the Library of Congress. Its authoritative version is given as an XML schema based on an XML mindset which means that it has significant limitations for use in a knowledge graphs context. We have therefore developed the Modular MODS Ontology (MMODS-O) which incorporates all elements and attributes of the MODS XML schema. In designing the ontology, we adopt the recent Modular Ontology Design Methodology (MOMo) with the intention to strike a balance between modularity and quality ontology design on the one hand, and conservative backward compatibility with MODS on the other.

Healthcare analytics company Pulse8 is offering a tool to identify and code patient conditions by accessing content from their clinical data and converting it to XML schema for integration with a variety of systems. Called Popul8, the software leverages machine learning, natural language processing as well as optical character and pattern recognition technologies to create what the company described as a data-driven view of healthcare processes. "The goal is to reduce waste, eliminate unnecessary interventions, and improve patient and provider visibility by easily extracting clinical information from both structured and unstructured data," Pulse8 CEO John Criswell said. Popul8 parses and processes XML schema with a 2-stage coding engine. The first stage uses an ICD parser to discover conditions present in or implied by the chart and physician notes.