Knowledge Graphs have been established as one the most relevant representations to encode knowledge, with relevant applications in the public and private sectors. One common research direction concerning the analysis of created knowledge graphs relies on the assumption that their intrinsic properties and structure are similar to what is observed in complex networks. However, studies concerning identifying typical complex network structures in knowledge graphs are lacking in the literature. This paper bridges this gap by analyzing commonly and recently used knowledge graphs in the semantic web field, seeking to demonstrate their complex network properties. Evaluation involving DBpedia and Wikidata data confirms the occurrence of intrinsic complex network structures in their respective knowledge graphs.

Vast amounts of data are being generated daily with the adoption of Internet-of-Things (IoT) solutions in an ever-increasing number of application domains. There are problems associated with all stages of the lifecycle of these data (e.g., capture, curation and preservation). Moreover, the volume, variety, dynamicity and ubiquity of IoT data present additional challenges to their usability, prompting the need for constructing scalable data-intensive IoT data management and processing platforms. This paper presents a novel approach to model and build IoT data platforms based on the characteristics of an Actor-Oriented Database (AODB). We take advantage of two complementary case studies – in structural health monitoring and beef cattle tracking and tracing – to describe novel software requirements introduced by IoT data processing. Our investigation illustrates the challenges and benefits provided by AODB to meet these requirements in terms of modeling and IoT-based systems implementation. Obtained results reveal the advantages of using AODB in IoT scenarios and lead to principles on how to effectively use an actor model to design and implement IoT data platforms.

The highly dynamic nature of domain ontologies has a direct impact on semantic mappings established between concepts from different ontologies. Mappings must therefore be maintained according to ongoing ontology changes. Since many software applications exploit mappings for managing information and knowledge, it is important to define appropriate adaptation strategies to apply to existing mappings in order to keep their validity over time. In this article, we propose a set of mapping adaptation actions and present how they are used to maintain mappings up-to-date based on ontology change operations of different nature. We conduct an experimental evaluation using life sciences ontologies and mappings. We measure the evolution of mappings based on the proposed approach to mapping adaptation. The results confirm that mappings must be individually adapted according to the different types of ontology change.