350
G. Chuiko et al.
Despite the fact that huge number of ontologies have been developed and this
process is still ongoing, they are facing a shortcoming of compatibility lack with
each other. There is a tendency that when one ontology seems to be describing all
necessary aspects of a domain needs to be combined with another one, multiple
contradictions might be found. The mapping of instances from both domain to a
third “unification” domain can solve it so they can exchange data without conflicts.
However, that leads to development of the new ontology. Even one of the most wellknown schemes, Semantic Sensor Networks (SSN), contains the most common types
of information, does not overlap sensor domain completely and new ontologies are
constantly appearing. The functionality available in the general-purpose framework
is not enough to fulfill the solution for the narrow domain. At the same time, domainspecific ontologies will be appearing constantly to extend higher-level ones.
Project Ontobee from University of Michigan stands out among projects for
medical ontologies. It concentrated an array of ontologies that relate to the healthcare. They contain terms about the disease, physical state, biological, biochemical,
drug description, etc. The list is not limited to the mentioned domains and comprises
208 ontologies. They can be used in further developments that involve annotation of
medical data in IoT system. However, while it does contains ontologies that overlap
IoT system, there is not ready-to-use ontology for provenance of medical data.
We assume that at this point this development cannot be unified up to some level.
Either way single ontology is not able to take into consideration all the peculiarities
that may appear in the future. Hence, the ontology should provide extension points
where new artifacts can be added. That implies the presence of abstract entities that
serve as a base for the development of new types and relations.
One of the biggest semantic-related projects is M3 project [5, 14]. It consolidates contemporary researches and their software artifacts with approximately 550
ontologies collected. M3 also establishes its own solutions to common problems in
the field of IoT. Speaking precisely, it is just one of the elements of semantic infrastructure. It covers all dataflow for sensor processing and even supports the generation
of software for the specific sensor system. The software includes an application for
acquiring sensor data, higher-level user interfaces, reasoning engine, and storage of
the data.
The part of the M3 project that collects ontologies is Linked Open Vocabularies
for IoT (LOV4IoT). The catalog is dynamic and constantly includes novel findings
described in scientific researches with a link to the source code repository. The main
goal of the subproject is to link existing vocabularies with each other and to establish
fundamentals for the usage of the vocabularies. From the point of view of healthcare,
the ontologies categorized as:
• General-purpose healthcare ontologies;
• Ambient assisted living;
• Wearable ontologies;
• Emotions ontologies;
• Activity recognition in smart home;
• Nutrition-related ontologies;
G. Chuiko et al.
Despite the fact that huge number of ontologies have been developed and this
process is still ongoing, they are facing a shortcoming of compatibility lack with
each other. There is a tendency that when one ontology seems to be describing all
necessary aspects of a domain needs to be combined with another one, multiple
contradictions might be found. The mapping of instances from both domain to a
third “unification” domain can solve it so they can exchange data without conflicts.
However, that leads to development of the new ontology. Even one of the most wellknown schemes, Semantic Sensor Networks (SSN), contains the most common types
of information, does not overlap sensor domain completely and new ontologies are
constantly appearing. The functionality available in the general-purpose framework
is not enough to fulfill the solution for the narrow domain. At the same time, domainspecific ontologies will be appearing constantly to extend higher-level ones.
Project Ontobee from University of Michigan stands out among projects for
medical ontologies. It concentrated an array of ontologies that relate to the healthcare. They contain terms about the disease, physical state, biological, biochemical,
drug description, etc. The list is not limited to the mentioned domains and comprises
208 ontologies. They can be used in further developments that involve annotation of
medical data in IoT system. However, while it does contains ontologies that overlap
IoT system, there is not ready-to-use ontology for provenance of medical data.
We assume that at this point this development cannot be unified up to some level.
Either way single ontology is not able to take into consideration all the peculiarities
that may appear in the future. Hence, the ontology should provide extension points
where new artifacts can be added. That implies the presence of abstract entities that
serve as a base for the development of new types and relations.
One of the biggest semantic-related projects is M3 project [5, 14]. It consolidates contemporary researches and their software artifacts with approximately 550
ontologies collected. M3 also establishes its own solutions to common problems in
the field of IoT. Speaking precisely, it is just one of the elements of semantic infrastructure. It covers all dataflow for sensor processing and even supports the generation
of software for the specific sensor system. The software includes an application for
acquiring sensor data, higher-level user interfaces, reasoning engine, and storage of
the data.
The part of the M3 project that collects ontologies is Linked Open Vocabularies
for IoT (LOV4IoT). The catalog is dynamic and constantly includes novel findings
described in scientific researches with a link to the source code repository. The main
goal of the subproject is to link existing vocabularies with each other and to establish
fundamentals for the usage of the vocabularies. From the point of view of healthcare,
the ontologies categorized as:
• General-purpose healthcare ontologies;
• Ambient assisted living;
• Wearable ontologies;
• Emotions ontologies;
• Activity recognition in smart home;
• Nutrition-related ontologies;