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map with planning techniques in Planning Domain Description Language (PDDL) that converts the goals into
actions (moving the robot, picking and dropping an object, etc.). Wang et al. [25] employed the relationships
among objects to represent the spatial layout. Object recognition and region inference are implemented by us-
ing stereo image data. Vasudevan et al. [26] created a hierarchical probabilistic concept-oriented representation
of space, based on objects. Diab M et al. [27] introduced an interpretation ontology to identify possible failures
which occur during automatic planning and the execution phase; this ontology aims to improve planning and
allow automatic replanning after error. Balakirsky etal. [28] proposed an ontology-based framework that allows
a robotic system to automatically recognize and adapt to changes that occur in its workflow and dynamically
change the details of task assignment, increasing process flexibility by allowing plans to adapt to production
errors and task changes.

In short, semantic maps enable a robot to solve reasoning problems of geometric, topological, ontological,
and logical nature, in addition to localization and path planning [29] . Formal conceptualization of the robotics
domain is an essential requirement for the future of robotics, in order to be able to design robots that can
autonomously perform a wide variety of tasks in a wide variety of environments.

2.3. Applications in robotic systems
Different research groups have used semantic knowledge in the area of robotics. Semantic knowledge allows
a clear dialog between all stakeholders involved in the life cycle of a robotic system and enables the efficient
integration and communication of heterogeneous robotic systems. These facilitate communication and knowl-
edge exchange between groups from different fields, without actually forcing them to align their research with
the particular view of a particular research group [30] .

One of the most recent advances in the field of robotics can be denoted by analyzing the KnowRob project,
where researchers aimed to enable a robot to answer different types of questions about possible interactions
with its environment, using semantic knowledge [31,32] . For example, they developed an ontology that allows
the robot to start an assembly activity, with incomplete knowledge. It identifies the missing parts, having
the ability to reason about how the missing information can be obtained [32] . KnowRob employs the DUL
foundational ontology, which is a slim version of the Descriptive Ontology for Linguistic and Cognitive En-
gineering (DOLCE). DUL and DOLCE have a clear cognitive bias, and they are both well established in the
knowledge engineering community as foundational ontologies. However, DUL does not define very specific
[8]
conceptssuchasforkordish. Theseconceptsareneededforourrobotsthatdoeverydayactivities . Thereare
also other relevant works that aim at the standardization of knowledge representation in the robotics domain,
such as IEEE-ORA [33] , ROSETTA [34] , CARESSES [35] , RoboEarth [36] , RoboBrain [37] , RehabRobo-Onto [38] ,
and OROSU [39] .

All of the above work already represents promising advances in the use of semantics in robotic systems; how-
ever, it lacks the ability to perform advanced reasoning and relies heavily on ad hoc reasoning solutions, signif-
icantly limiting its scope. A general standardized framework for working with ontologies is needed, natively
supporting symbolic logic and advanced reasoning paradigms.

Thenextsectionspresentthereasoningframeworks, withparticularemphasisonthedomainspecificontology,
home environment. The proposed ontology is designed to be easily reusable in different environments of a
house, as well as by different robotic agents.

3. DESIGN METHODOLOGY

The focus of the developed ontology is to enable robotic agents to interact with elderly people within a home
environment. The robots are to assist the elderly people to manage and better perform their daily lives, and