Remarkable progress in research has shown the efficiency of Knowledge Graphs (KGs) in extracting valuable external knowledge in various domains. A Knowledge Graph (KG) can illustrate high-order relations that connect two objects with one or multiple related attributes. The emerging Graph Neural Networks (GNN) can extract both object characteristics and relations from KGs. This paper presents how Machine Learning (ML) meets the Semantic Web and how KGs are related to Neural Networks and Deep Learning. The paper also highlights important aspects of this area of research, discussing open issues such as the bias hidden in KGs at different levels of graph representation.
Nowadays, cultural spaces (e.g., museums and archaeological sites) are interested in adding intelligence in their ecosystem by deploying different types of smart applications such as automated environmental monitoring, energy saving, and user experience optimization. Such an ecosystem is better realized through semantics in order to efficiently represent the required knowledge for facilitating interoperability among different application domains, integration of data, and inference of new knowledge as insights into what may have not been observed at first sight. This paper reports on our recent efforts for the engineering of a smart museum (SM) ontology that meets the following objectives: (a) represent knowledge related to trustworthy IoT entities that “live” and are deployed in a SM, i.e., things, sensors, actuators, people, data, and applications; (b) deal with the semantic interoperability and integration of heterogeneous SM applications and data; (c) represent knowledge related to museum visits and visitors toward enhancing their visiting experience; (d) represent knowledge related to smart energy saving; (e) represent knowledge related to the monitoring of environmental conditions in museums; and (f) represent knowledge related to the space and location of exhibits and collections. The paper not only contributes a novel SM ontology, but also presents the updated HCOME methodology for the agile, human-centered, collaborative and iterative engineering of living, reused, and modular ontologies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.