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A. Chatzimichail et al.
technologies, improve patients everyday activities and enhance patients residence in
healthy environments. Some of them are presented below.
A plain vocabulary to describe people, activities and information about their
relations is provided by Friend Of A Friend (FOAF) [113, 114] ontology. This
ontology is often used to describe personal information, social connections and networks between people, for instance their membership in groups. People, described
as instances of foaf: Person class, can contain many properties like name, email
address, image and age. On the other hand, General User Model Ontology (GUMO)
[115] offers a more uniform representation of user models. The ontology describes
a user from many different perspectives: contact information, demographics, ability,
personality, characteristics, motion, role, nutrition, facial expression and emotional,
physiological and mental state.
A more extensive ontology for capturing patient information is AHA [116]. AHA
ontology uses information coming from wearables to support Ambient Assisting
Living environments. The main scope is monitoring activities and extending smart
home abilities to assist in lifestyle profiling and healthy ageing issues. The ontology
captures body measurements information (such as weight, height), activity-specific
information (such as activity levels, energy expenditure, body position that each
activity affects) and health state information (such as general health, heart rate,
temperature). AHA ontology schema is presented below.
3.3 Ontology-Based Reasoning
This section is related to semantic complex event processing, queries, reasoning rules
and algorithms as well as different reasoning frameworks.
3.3.1 Reasoning Frameworks
Description Logics (DLs) [117] are a group of knowledge representation frameworks
characterized by logically driven semantics and well-defined inference structures.
The key components are classes representing sets of objects (e.g. Person), properties
representing entity associations (e.g. livesIn) and individuals representing individual
objects (e.g. Tom). Description Logics provide a powerful set of reasoning frameworks. Pellet [118] Racer [119], Fact++ [120] and Hermit [121] are examples of
state of the art implementations of such frameworks. Starting from basic definitions,
such as Person, it is possible to describe more complicated concepts. For example,
the concept ∃has Father.Person describes those objects that are related through the
has Father property with an object from the class Person. A DL knowledge base
K typically consists of a TBox T (terminological knowledge) and an ABox A (assertional knowledge). The TBox includes axioms describing possible ways of associating domain objects. For example, the TBox axiom Cat Animal asserts that all
objects that belong to the class Cat, are members of the class Animal too. The ABox
A. Chatzimichail et al.
technologies, improve patients everyday activities and enhance patients residence in
healthy environments. Some of them are presented below.
A plain vocabulary to describe people, activities and information about their
relations is provided by Friend Of A Friend (FOAF) [113, 114] ontology. This
ontology is often used to describe personal information, social connections and networks between people, for instance their membership in groups. People, described
as instances of foaf: Person class, can contain many properties like name, email
address, image and age. On the other hand, General User Model Ontology (GUMO)
[115] offers a more uniform representation of user models. The ontology describes
a user from many different perspectives: contact information, demographics, ability,
personality, characteristics, motion, role, nutrition, facial expression and emotional,
physiological and mental state.
A more extensive ontology for capturing patient information is AHA [116]. AHA
ontology uses information coming from wearables to support Ambient Assisting
Living environments. The main scope is monitoring activities and extending smart
home abilities to assist in lifestyle profiling and healthy ageing issues. The ontology
captures body measurements information (such as weight, height), activity-specific
information (such as activity levels, energy expenditure, body position that each
activity affects) and health state information (such as general health, heart rate,
temperature). AHA ontology schema is presented below.
3.3 Ontology-Based Reasoning
This section is related to semantic complex event processing, queries, reasoning rules
and algorithms as well as different reasoning frameworks.
3.3.1 Reasoning Frameworks
Description Logics (DLs) [117] are a group of knowledge representation frameworks
characterized by logically driven semantics and well-defined inference structures.
The key components are classes representing sets of objects (e.g. Person), properties
representing entity associations (e.g. livesIn) and individuals representing individual
objects (e.g. Tom). Description Logics provide a powerful set of reasoning frameworks. Pellet [118] Racer [119], Fact++ [120] and Hermit [121] are examples of
state of the art implementations of such frameworks. Starting from basic definitions,
such as Person, it is possible to describe more complicated concepts. For example,
the concept ∃has Father.Person describes those objects that are related through the
has Father property with an object from the class Person. A DL knowledge base
K typically consists of a TBox T (terminological knowledge) and an ABox A (assertional knowledge). The TBox includes axioms describing possible ways of associating domain objects. For example, the TBox axiom Cat Animal asserts that all
objects that belong to the class Cat, are members of the class Animal too. The ABox