# Can Semantics Uncover Hidden Relations between Neurodegenerative Diseases and Artistic Behaviors?
## 4 State-of-the-Art
### Ontologies Related to NDs
This ontology is an important resource for researchers and clinicians to annotate, share, and integrate data related to PD and other movement disorders, as it provides a standardized vocabulary for these conditions. Another well-known ontology related to PD is the Human Phenotype Ontology (HPO), which is a standardized vocabulary of human phenotypic abnormalities and related medical terms organized into a hierarchical structure [90]. The HPO is used to annotate clinical and genetic data, and it enables the integration of diverse types of data in biomedicalresearch, including genetic, clinical, and model organism data. HPO contains over 13,000 standardized terms that describe phenotypic abnormalities, such as abnormal morphology or function of organs, tissues, or cells, and the signs and symptoms of diseases [90]. The ontology also includes information about the inheritance patterns of phenotypic abnormalities and links to other biomedical ontologies and databases. The HPO is used in various applications, including clinical diagnosis, genetic counseling, and research. It is also used in the analysis of genome-wide association studies (GWAS) and in the development of computational algorithms for the identification of disease-causing genetic variants. The HPO is maintained and developed by the HPO Consortium [90], an international group of researchers and clinicians who collaborate to improve the ontology and its applications encouraging community participation and contributions to improve the quality and coverage of the ontology. The Parkinson's Disease Knowledge Ontology (PDKO) is another formal representation of knowledge related to PD and PD-related disorders [91]. It aims to provide a standardized vocabulary and hierarchy of concepts for representing PD-related knowledge, making it easier to share, integrate, and reason about data from different sources. The PDKO is based on the OWL and includes a set of classes, properties, and axioms that define the relationships between different concepts [91]. Some of the key classes in the PDKO include \"Parkinson's disease\", \"symptom\", \"neurotransmitter\", \"gene\", \"drug\", and \"clinical trial\". These classes are organized into a hierarchical structure, with more specific concepts (e.g., \"tremor\" and \"dopamine\") being subclasses of more general concepts (e.g., \"symptom\" and \"neurotransmitter\"). The PDKO also includes a set of properties that define relationships between concepts. There is a \"causes\" property that links a disease to the factors that contribute to its development and a \"treats\" property that links a drug to the conditions it is used to treat. It was developed as part of the National Institute of Neurological Disorders and Stroke (NINDS) Parkinson's Disease Biomarkers Program [92], which is a multi-institutional initiative aimed at improving the diagnosis and treatment of PD. The PDKO is also a component of the larger Neuroscience Information Framework (NIF) project [93], which is a collaborative effort to develop a comprehensive ontology for neuroscience research. [PERSON] et al. [94] recognized the significant barrier to effective information retrieval and integration in the field of AD. To overcome this, they proposed the use of formal ontologies, which can help construct specific knowledge domains. Gene ontology and SNOMED already demonstrated the benefits of using ontologies for annotating and recording clinical data. The Fraunhofer Institute SCAI [95], as a bioinformatics partner in the Neuroallianz Consortium [96], saw the need for a more efficient system to represent the knowledge structure in the AD domain. This prompted them to draft an ontology that would represent various aspects of current knowledge in AD, including clinical features, treatment, risk factors, and more. The ontology, known as the Alzheimer's Disease Ontology (ADO) [97], was designed to conform to typical higher ontologies while still maintaining concept descriptions that are similar to normal language terms.