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A. Gyrard et al.
domain because it covers more than 20 application domains (e.g., healthcare, smart
building, smart farm) that use sensors. PerfectO enhances knowledge expertise
quality implemented within any ontologies as demonstrated with the Linked Open
Vocabularies for IoT (LOV4IoT) ontology catalog.
Keywords Knowledge directory · Knowledge directory service · Semantic data
interoperability · Ontology quality · Methodology · Web of things · Internet of
things · Semantic web of things · Semantic web technologies
1 Introduction
We have been witnessing a growing number of sensors embedded in smart objects
(e.g., Fitbit watches). Sensor-based applications are increasingly present in our everyday life (e.g., Mother [61] reminds medication, Apple HealthKit [62] tracks fitness,
nutrition, and sleep, and Foobot measures air quality). The hardware devices, protocols, and infrastructures are already deployed. However, these devices have not been
engineered by the same company nor explicitly designed to be compatible with other
devices. The devices produce data that is sent to the Web to build the ‘Internet/Web
of Things’ (IoT/WoT) applications. According to Cisco’s predictions [63], there will
be more than 50 billion of devices connected to the Internet by 2020. Due to the enormous quantity of sensor data generated, interpreting data and building interoperable
IoT applications is needed. There are several heterogeneity issues to address (1) data
format, (2) languages to describe sensor metadata, (3) ontologies to structure sensor
datasets, (4) reasoning mechanisms and rule languages to interpret sensor datasets,
and (5) applications. The challenge today is finding better ways to reuse more meaningful and more accurate information, to get useful abstractions from sensor data.
Innovative methodologies are required to link the data from different domains to
improve knowledge discovery.
Semantic Web technologies are widely used in applications and in online activities that we perform every day. Google, reused the Knowledge Graph (KG) [64]
term in 2012, which became popular, and demonstrated the impact of semantic web
technologies. Everyday, we are using KG technologies without being aware of it.
Indeed, when we are looking for information (e.g., a famous person, a restaurant)
using the Google search engine, structured information appears on the right. According to Paulheim’s KG survey [1], a KG (1) mainly describes real-world entities and
their interrelations, organized in a graph, (2) defines possible classes and relations
of entities in a schema (e.g., ontologies), (3) allows for potentially interrelating arbitrary entities with each other, and (4) covers various domains. KGs are based on
Linked Data [2] mechanisms implemented by semantic web standards (i.e., XML,
RDF, OWL, SKOS, SPARQL, etc.) to enable: (1) large-scale data integration, and
(2) reasoning on information over the Web. Linked Data structures data according to
ontologies. Ontologies [3] facilitate data exchange and interoperability within appli-
A. Gyrard et al.
domain because it covers more than 20 application domains (e.g., healthcare, smart
building, smart farm) that use sensors. PerfectO enhances knowledge expertise
quality implemented within any ontologies as demonstrated with the Linked Open
Vocabularies for IoT (LOV4IoT) ontology catalog.
Keywords Knowledge directory · Knowledge directory service · Semantic data
interoperability · Ontology quality · Methodology · Web of things · Internet of
things · Semantic web of things · Semantic web technologies
1 Introduction
We have been witnessing a growing number of sensors embedded in smart objects
(e.g., Fitbit watches). Sensor-based applications are increasingly present in our everyday life (e.g., Mother [61] reminds medication, Apple HealthKit [62] tracks fitness,
nutrition, and sleep, and Foobot measures air quality). The hardware devices, protocols, and infrastructures are already deployed. However, these devices have not been
engineered by the same company nor explicitly designed to be compatible with other
devices. The devices produce data that is sent to the Web to build the ‘Internet/Web
of Things’ (IoT/WoT) applications. According to Cisco’s predictions [63], there will
be more than 50 billion of devices connected to the Internet by 2020. Due to the enormous quantity of sensor data generated, interpreting data and building interoperable
IoT applications is needed. There are several heterogeneity issues to address (1) data
format, (2) languages to describe sensor metadata, (3) ontologies to structure sensor
datasets, (4) reasoning mechanisms and rule languages to interpret sensor datasets,
and (5) applications. The challenge today is finding better ways to reuse more meaningful and more accurate information, to get useful abstractions from sensor data.
Innovative methodologies are required to link the data from different domains to
improve knowledge discovery.
Semantic Web technologies are widely used in applications and in online activities that we perform every day. Google, reused the Knowledge Graph (KG) [64]
term in 2012, which became popular, and demonstrated the impact of semantic web
technologies. Everyday, we are using KG technologies without being aware of it.
Indeed, when we are looking for information (e.g., a famous person, a restaurant)
using the Google search engine, structured information appears on the right. According to Paulheim’s KG survey [1], a KG (1) mainly describes real-world entities and
their interrelations, organized in a graph, (2) defines possible classes and relations
of entities in a schema (e.g., ontologies), (3) allows for potentially interrelating arbitrary entities with each other, and (4) covers various domains. KGs are based on
Linked Data [2] mechanisms implemented by semantic web standards (i.e., XML,
RDF, OWL, SKOS, SPARQL, etc.) to enable: (1) large-scale data integration, and
(2) reasoning on information over the Web. Linked Data structures data according to
ontologies. Ontologies [3] facilitate data exchange and interoperability within appli-