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IoT environments are continuously changing. Changes may come from the service, connectivity, or physical layers of the IoT architecture. Therefore, to function appropriately, the system needs to dynamically adapt to its environment. In previous work, we posited eight challenges to foster adaptation through all architecture layers of IoT systems. In this paper, we address the challenges to manage the inclusion of new devices and devices' transient connection , by means of dynamic adaptations incorporated into our proposed software architecture for adaptive IoT systems. To manage dynamic adaptations, we extend the reference IoT architecture with our specialized components. In particular, we use (1) ontologies and instances to represent the domain knowledge; (2) a matching algorithm to pair services and IoT devices, taking into account their functional requirements, quality attributes and sensors properties; and (3) a match update algorithm used whenever sensors become (un)available. We evaluate the effectiveness of our solution with respect to the accuracy of matching services and IoT devices, and the response to environment changes. • This paper presents a new adaptive architecture for IoT Systems. • Ontology matching algorithm-based architecture to manage transient connections. • Functional, non-functional, and QoS matching algorithm between IoT components. • Matching algorithm with better precision and recall compared with the gold-standard. • Architecture deployed in a real case study for a SUDS system in the city of Bogotá. [ABSTRACT FROM AUTHOR]

Abstract: A challenge for Linked Data is to link instances from different data sources that denote the same real-world object. Millions of high-quality owl:sameAs linkages have been generated, but potential ones are still considerable. Traditional similarity-based methods to this data linkage problem do not scale well since they exhaustively compare every pair of instances. In this paper, we propose an automatic approach to data linkage generation for Linked Data. Specifically, a highly-accurate training set is automatically generated based on equivalence reasoning and common prefix blocking. The contexts of the instances in the training set, after extracting, are pairwise matched in order to learn discriminative property pairs supporting linkage discovery. For a particular class pair and a pay-level-domain pair, the discriminability of each property pair is measured, and a few property pairs with high discriminability are aggregated in order to be reused in the future to link instances between the same classes and domains. The experimental results show that our approach achieves good accuracy against some complex methods in two OAEI tests and the BTC2011 dataset. [Copyright &y& Elsevier]

Abstract: The variety of web resources available to users for their business or personal needs is growing, spanning from fast, short, ready-to-consume news/posts to well-structured, formal ontology instances of the Semantic Web. In this context, users require to retrieve very fast all available prominent information about target entities regarding events, people, situations and similar. In this paper, we introduce the notion of inCloud (information Cloud) and we propose an approach to web resource clouding for the construction of inClouds. inClouds are built for a target entity of interest by distinguishing, also in a visual way, how much prominent the retrieved web resource(s) are with respect to the target entity and by organizing web resources according to their reciprocal levels of closeness. An application of the proposed approach to a collection of real web resources about movies is presented. Applicability and evaluation issues are also discussed. [Copyright &y& Elsevier]

Instance matching is a key task in knowledge graph fusion, and it is critical to improving the efficiency of instance matching, given the increasing scale of knowledge graphs. Blocking algorithms selecting candidate instance pairs for comparison is one of the effective methods to achieve the goal. In this paper, we propose a novel blocking algorithm named MultiObJ, which constructs indexes for instances based on the Ordered Joint of Multiple Objects' features to limit the number of candidate instance pairs. Based on MultiObJ, we further propose a distributed framework named Follow-the-Regular-Leader Instance Matching (FTRLIM), which matches instances between large-scale knowledge graphs with approximately linear time complexity. FTRLIM has participated in OAEI 2019 and achieved the best matching quality with significantly efficiency. In this research, we construct three data collections based on a real-world large-scale knowledge graph. Experiment results on the constructed data collections and two real-world datasets indicate that MultiObJ and FTRLIM outperform other state-of-the-art methods. [ABSTRACT FROM AUTHOR]