Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Display Omitted Diff Abstraction Networks are introduced to summarize and visualize ontology change.The Diff Area Taxonomy (DAT) and the Diff Partial-area Taxonomy (DPAT) are introduced.DPATs are derived for three ontologies and are shown to highlight important changes.DPATs offer a compact way of viewing many changes due to various editing operations. Biomedical ontologies are a critical component in biomedical research and practice. As an ontology evolves, its structure and content change in response to additions, deletions and updates. When editing a biomedical ontology, small local updates may affect large portions of the ontology, leading to unintended and potentially erroneous changes. Such unwanted side effects often go unnoticed since biomedical ontologies are large and complex knowledge structures. Abstraction networks, which provide compact summaries of an ontology's content and structure, have been used to uncover structural irregularities, inconsistencies and errors in ontologies. ,  introduce Diff Abstraction Networks ("Diff AbNs"), compact networks that summarize and visualize global structural changes due to ontology editing operations that result in a new ontology release. A Diff AbN can be used to support curators in identifying unintended and unwanted ontology changes. The derivation of two Diff AbNs, the Diff Area Taxonomy and the Diff Partial-area Taxonomy, is explained and Diff Partial-area Taxonomies are derived and analyzed for the Ontology of Clinical Research, Sleep Domain Ontology, and eagle-i Research Resource Ontology. Diff Taxonomy usage for identifying unintended erroneous consequences of quality assurance and ontology merging are demonstrated.
['Christopher Ochs', 'Yehoshua Perl', 'James Geller', 'Melissa Haendel', 'Matthew H. Brush', 'Sivaram Arabandi', 'Samson W. Tu']
I am writing a research paper on a idea Summarizing and visualizing structural changes during the evolution of biomedical ontologies using a Diff Abstraction Network
[{"from": "human", "value": "I am writing a research paper on a idea Summarizing and visualizing structural changes during the evolution of biomedical ontologies using a Diff Abstraction Network"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Display Omitted Diff Abstraction Networks are introduced to summarize and visualize ontology change.The Diff Area Taxonomy (DAT) and the Diff Partial-area Taxonomy (DPAT) are introduced.DPATs are derived for three ontologies and are shown to highlight important changes.DPATs offer a compact way of viewing many changes due to various editing operations. Biomedical ontologies are a critical component in biomedical research and practice. As an ontology evolves, its structure and content change in response to additions, deletions and updates. When editing a biomedical ontology, small local updates may affect large portions of the ontology, leading to unintended and potentially erroneous changes. Such unwanted side effects often go unnoticed since biomedical ontologies are large and complex knowledge structures. Abstraction networks, which provide compact summaries of an ontology's content and structure, have been used to uncover structural irregularities, inconsistencies and errors in ontologies. ,  introduce Diff Abstraction Networks (\"Diff AbNs\"), compact networks that summarize and visualize global structural changes due to ontology editing operations that result in a new ontology release. A Diff AbN can be used to support curators in identifying unintended and unwanted ontology changes. The derivation of two Diff AbNs, the Diff Area Taxonomy and the Diff Partial-area Taxonomy, is explained and Diff Partial-area Taxonomies are derived and analyzed for the Ontology of Clinical Research, Sleep Domain Ontology, and eagle-i Research Resource Ontology. Diff Taxonomy usage for identifying unintended erroneous consequences of quality assurance and ontology merging are demonstrated."}]