The reuse of electronic health records information models in the oncology domain: Studies with the bioframe framework.

Objective: The reuse of Electronic Health Records (EHR) information models (e.g., templates and archetypes) may bring various benefits, including higher standardization, integration, interoperability, increased productivity in developing EHR systems, and unlock potential Artificial Intelligence applications built on top of medical records.

Context-based refinement of mappings in evolving life science ontologies.

Background: Biomedical computational systems benefit from ontologies and their associated mappings. Indeed, aligned ontologies in life sciences play a central role in several semantic-enabled tasks, especially in data exchange. It is crucial to maintain up-to-date alignments according to new knowledge inserted in novel ontology releases.

KGen: a knowledge graph generator from biomedical scientific literature.

Background: Knowledge is often produced from data generated in scientific investigations. An ever-growing number of scientific studies in several domains result into a massive amount of data, from which obtaining new knowledge requires computational help. For example, Alzheimer's Disease, a life-threatening degenerative disease that is not yet curable.

Towards natural language question generation for the validation of ontologies and mappings.

Background: The increasing number of open-access ontologies and their key role in several applications such as decision-support systems highlight the importance of their validation. Human expertise is crucial for the validation of ontologies from a domain point-of-view. However, the growing number of ontologies and their fast evolution over time make manual validation challenging.

DyKOSMap: A framework for mapping adaptation between biomedical knowledge organization systems.

Background: Knowledge Organization Systems (KOS) and their associated mappings play a central role in several decision support systems. However, by virtue of knowledge evolution, KOS entities are modified over time, impacting mappings and potentially turning them invalid. This requires semi-automatic methods to maintain such semantic correspondences up-to-date at KOS evolution time.

Recognizing lexical and semantic change patterns in evolving life science ontologies to inform mapping adaptation.

Background: Mappings established between life science ontologies require significant efforts to maintain them up to date due to the size and frequent evolution of these ontologies. In consequence, automatic methods for applying modifications on mappings are highly demanded. The accuracy of such methods relies on the available description about the evolution of ontologies, especially regarding concepts involved in mappings.

The influence of similarity between concepts in evolving biomedical ontologies for mapping adaptation.

Biomedical ontologies continuously evolve which demands maintain associated mappings up-to-date. This article studies whether similarity calculated between values of concept attributes issued from successive ontology versions plays a role in deciding mapping adaptation actions. We empirically analyse the evolution of official mappings established between large biomedical ontologies.

Characterizing semantic mappings adaptation via biomedical KOS evolution: a case study investigating SNOMED CT and ICD.

Mappings established between Knowledge Organization Systems (KOS) increase semantic interoperability between biomedical information systems. However, biomedical knowledge is highly dynamic and changes affecting KOS entities can potentially invalidate part or the totality of existing mappings. Understanding how mappings evolve and what the impacts of KOS evolution on mappings are is therefore crucial for the definition of an automatic approach to maintain mappings valid and up-to-date over time.

Understanding semantic mapping evolution by observing changes in biomedical ontologies.

Knowledge Organization Systems (KOSs) are extensively used in the biomedical domain to support information sharing between software applications. KOSs are proposed covering different, but overlapping subjects, and mappings indicate the semantic relation between concepts from two KOSs. Over time, KOSs change as do the mappings between them.