Reuse and enrichment for building an ontology for Obsessive-Compulsive Disorder

Building ontologies for mental diseases and disorders facilitates effective communication and knowledge sharing between healthcare providers, researchers, and patients. General medical and specialized ontolo- gies, such as the Mental Disease Ontology, are large repositories of concepts that require much effort to create and maintain. This paper proposes ontology reuse and automatic enrichment as means for design- ing and building an Obsessive-Compulsive Disorder (OCD) ontology. The methods are demonstrated by designing and building an ontology for the OCD. Ontology reuse is proposed through ontology alignment design patterns to allow for full, partial or nominal reuse. Enrichment is proposed through deep learning with a language representation model pre-trained on large-scale corpora of clinical notes and discharge summaries, as well as a text corpus from an OCD discussion forum. An ontology design pattern is proposed to encode the discovered related terms and their degree of similarity to the ontological concepts. The proposed approach allows for the seamless extension of the ontology by linking to other ontological resources or other learned vocabularies in the future. The OCD ontology is available online on Bioportal

TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media

Language evolves over time, and word meaning changes accordingly. This is especially true in social media, since its dynamic nature leads to faster semantic shifts, making it challenging for NLP models to deal with new content and trends. However, the number of datasets and models that specifically address the dynamic nature of these social platforms is scarce. To bridge this gap, we present TempoWiC, a new benchmark especially aimed at accelerating research in social media-based meaning shift. Our results show that TempoWiC is a challenging benchmark, even for recently-released language models specialized in social media.Comment: Accepted to COLING 2022. Used to create the TempoWiC Shared Task for EvoNL