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• An upper or general ontology (upper ontology or foundation ontology) is a model of the common objects
that are generally applicable to a wide variety of domain ontologies. There are several higher ontologies
standardized for use, such as SUMO (suggested upper merged ontology) [10] , Cyc ontology [11] , BFO (basic
formal ontology) [12] , and DOLCE (descriptive ontology for linguistic and cognitive engineering) [13] .
• Domain ontologies (domain ontology or domain-specific ontology) model a specific domain or part of the
world (e.g., robotic [14] , electronic, medical, mechanical, or digital domain).
• Task ontologies describe generic tasks or activities [15] .
• Application ontologies are strictly related to a specific application and used to describe concepts of a par-
ticular domain and task.
In the next subsections, the related work is reviewed about semantic maps, ontologies for semantic maps, and
some applications of knowledge representation for robotic systems.

2.1. Semantic maps
The daily challenges have drive the research for automated and autonomous solutions to enable mobile robots
to operate in highly dynamic environments. For this purpose, mobile robots need to create and maintain an
internal representation oftheir environment, commonly referred to as a map. Robotic systems rely on different
types of maps depending on their goals. Different map typology’s have been developed such as metric and
topological maps, which are generally 2D representations of the environment [16] , or hybrids (a combination
of the previous two) [17,18] . There are also maps with 3D representation (sparse map, semi-dense map, and
dense map). Metric and topological maps only contain spatial information [19] . A fundamental requirement
for the successful construction of maps is to deal with uncertainty arising, from errors in robot perception
(limited field of view and sensor range, noisy measurements, etc.), from inaccurate models and algorithms, etc.

To get around this limitation, semantic maps were developed to add additional information, such as instances,
categories, and attributes of various constituent elements of the environment (objects, rooms, etc.) [5–7] . These
provide robots with the ability to understand beyond the spatial aspects of the environment, the meaning of
each element, and how humans interact with them (features, events, relationships, etc.). Semantic maps deal
with meta information that models the properties and relationships of relevant concepts in the domain in
question, encoded in a knowledge base (KB).

2.2. Ontologies for semantic maps

Oneofthetaskstobe solvedinmobilerobotnavigationistheacquisitionofinformationfromtheenvironment.
In the field of semantic navigation, information includes concepts such as objects, utilities, or room types. The
robot needs to learn the relationshipsthatexist between the concepts included in the knowledge representation
model. Semantic maps add to classical robotic maps spatially grounded object instances anchored in a suitable
way for knowledge representation and reasoning. The classification of instances through the analysis of the
data collected by sensors is one of the biggest challenges in the creation of semantic maps (i.e., to give a richer
semantic meaning to the sensor data) [20,21] .

In the last decade, several papers have appeared in the literature contributing different representations of
semantic maps. Kostavelis et al. [5] summarized the significant progress made on a broad range of mapping
approaches and applications for semantic maps, including task planning, localization, navigation, and human–
robot interaction. Semantics has been used in a diverse range of applications. Lim et al. [22] presented an
approach for unified robot knowledge for service robots in indoor environments. Rusu et al. [23] developed a
mapcalled SemanticObject Maps(SOM),whichencodesspatial informationaboutindoorhouseholdenviron-
ments, in particular kitchens, but in addition it also enriches the information content with encyclopedic and
common sense knowledge about objects, as well as includes knowledge derived from observations. Galindo
et al. [24] proposed an approach for robotic agents to correct situations in the world that do not conform to
the semantic model by generating appropriate goals for the robot. In short, it combines the use of a semantic