OntoPlot: A Novel Visualisation for Non-hierarchical Associations in Large Ontologies
Ontologies are formal representations of concepts and complex relationships among them. They have been widely used to capture comprehensive domain knowledge in areas such as biology and medicine, where large and complex ontologies can contain hundreds of thousands of concepts. Especially due to the large size of ontologies, visualisation is useful for authoring, exploring and understanding their underlying data. Existing ontology visualisation tools generally focus on the hierarchical structure, giving much less emphasis to non-hierarchical associations. In this paper we present OntoPlot, a novel visualisation specifically designed to facilitate the exploration of all concept associations whilst still showing an ontology's large hierarchical structure. This hybrid visualisation combines icicle plots, visual compression techniques and interactivity, improving space-efficiency and reducing visual structural complexity. We conducted a user study with domain experts to evaluate the usability of OntoPlot, comparing it with the de facto ontology editor Prot{\'e}g{\'e}. The results confirm that OntoPlot attains our design goals for association-related tasks and is strongly favoured by domain experts.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Predicting Gene-Disease Associations with Knowledge Graph Embeddings over Multiple Ontologies
Ontology-based approaches for predicting gene-disease associations include the more classical semantic similarity methods and more recently knowledge graph embeddings. While semantic similarity is typically restricted to…
Knowledge Graph EmbeddingsSemantic SimilaritySemantic Textual SimilarityA Review of Multilingualism in and for Ontologies
The Multilingual Semantic Web has been in focus for over a decade. Multilingualism in Linked Data and RDF has shown substantial adoption, but this is unclear for ontologies since the last review 15 years ago. One of the …
Protocol for evaluating ChatGPT in biomedical association generation and verification using a RAG-enabled, cross-model majority voting workflow
We present a protocol to evaluate ChatGPT's ability to generate disease-centric biomedical associations. It outlines how we generate the associations, validate the biological entities using biomedical ontologies, and ver…
From Knowledge Generation to Knowledge Verification: Examining the BioMedical Generative Capabilities of ChatGPT
The generative capabilities of LLM models offer opportunities for accelerating tasks but raise concerns about the authenticity of the knowledge they produce. To address these concerns, we present a computational approach…
Prompt EngineeringUnsupervised Terminological Ontology Learning based on Hierarchical Topic Modeling
In this paper, we present hierarchical relationbased latent Dirichlet allocation (hrLDA), a data-driven hierarchical topic model for extracting terminological ontologies from a large number of heterogeneous documents. In…
Topic Models