# Can Semantics Uncover Hidden Relations between Neurodegenerative Diseases and Artistic Behaviors?
## 4 State-of-the-Art
### Ontologies Related to NDs  
[PERSON] et al. [70] investigated the issue of selecting an appropriate biomedical ontology for a given research topic. They found that the sheer amount, complexity, and diversity of available biomedical ontologies made it difficult to choose the right one for a specific research project, as the choice would depend on the study's characteristics. In response to this challenge, the National Centre for Biomedical Ontology (NCBO) [71] released the Ontology Recommender in 2010 [70]. This service provides assistance in choosing the right ontology by suggesting ontologies that are best suited to a biomedical text corpus or a list of keywords. The Ontology Recommender version 2.0 uses several criteria to evaluate the suitability of ontologies for biomedical text data, including the extent to which the ontology covers the input data, the level of detail of the ontology classes that cover the input data, the acceptance of the ontology in the biomedical community, and the domain-specific specialization of the ontology. The service is available via a web service API and a web-based user interface, which was developed using the Ruby-on-Rails web framework [72] and the JavaScript language. The evaluation of Ontology Recommender 2.0 showed that it provides higher quality suggestions, better coverage of input data, and more detailed information about concepts compared to previous implementations. An ontology that is intended to support the integration and sharing of data and knowledge related to diseases across multiple domains, including clinical practice, research, and public health, is the Disease Ontology (DO) [73]. It is a formal ontology that provides a standardized vocabulary and hierarchy of concepts for representing knowledge related to human diseases. The DO includes a set of classes and relationships that define concepts related to diseases, including \"disease\", \"symptom\", \"diagnosis\", and \"treatment\". These classes are organized into a hierarchical structure, with more specific concepts being subclasses of more general concepts. For example, \"lung cancer\" is a subclass of \"cancer\". The DO includes a set of relationships that define the connections between different concepts and is used in a variety of applications and systems, including electronic health records, CDSSs, and biomedical research databases. The DO is freely available [74] and can be accessed through several online resources, including the National Centre for Biomedical Ontology (NCBO) BioPortal [71] and the UMLS Metathesaurus [57]. Another ontology concerning diseases that are related to motor symptoms is the Geriatric Motion Assessment (GMA) ontology, which is related to the assessment of motion and function in older adults [75]. The ontology is intended to support the integration and sharing of data and knowledge related to geriatric motion assessment across multiple domains, including clinical practice, research, and education. The GMA ontology includes a set of classes and relationships that define concepts related to geriatric motion assessment, including \"assessment instrument\", \"test\", \"task\", and \"outcome measure\". These classes are organized into a hierarchical structure, with more specific concepts being subclasses of more general concepts. For example, \"balance test\" is a subclass of \"assessment instrument\". The ontology is used in a variety of applications and systems, including electronic health records, CDSSs, and biomedical research databases. [PERSON] et al. [76] conducted an analysis and comparison of ontologies used in the area of AD as part of an expert system. Ontologies play an important role in homogenizing information, allowing for the integration of different types and structures of information as well as the inference of additional information based on stored data. The authors analyzed several ontologies and evaluated them based on criteria such as standardization of terminology, information storage and retrieval, and support for diagnosis.