Abstract-"Ontology matching" is the process of finding correspondences between entities belonging to different ontologies. This paper describes a set of algorithms that exploit upper ontologies as semantic bridges in the ontology matching process and presents a systematic analysis of the relationships among features of matched ontologies (number of simple and composite concepts, stems, concepts at the top level, common English suffixes and prefixes, ontology depth), matching algorithms, used upper ontologies, and experiment results. This analysis allowed us to state under which circumstances the exploitation of upper ontologies gives significant advantages with respect to traditional approaches that do no use them. We run experiments with SUMO-OWL (a restricted version of SUMO), OpenCyc and DOLCE. The experiments demonstrate that when our "structural matching method via upper ontology" uses an upper ontology large enough (OpenCyc, SUMO-OWL), the recall is significantly improved while preserving the precision obtained without upper ontologies. Instead, our "non structural matching method" via OpenCyc and SUMO-OWL improves the precision and maintains the recall. The "mixed method" that combines the results of structural alignment without using upper ontologies and structural alignment via upper ontologies improves the recall and maintains the F-measure independently of the used upper ontology.
International audienceA recent trend in programming language research is to use behavioral type theory to ensure various correctness properties of largescale, communication-intensive systems. Behavioral types encompass concepts such as interfaces, communication protocols, contracts, and choreography. The successful application of behavioral types requires a solid understanding of several practical aspects, from their representation in a concrete programming language, to their integration with other programming constructs such as methods and functions, to design and monitoring methodologies that take behaviors into account. This survey provides an overview of the state of the art of these aspects, which we summarize as the pragmatics of behavioral types
A recent trend in programming language research is to use behavioral type theory to ensure various correctness properties of largescale, communication-intensive systems. Behavioral types encompass concepts such as interfaces, communication protocols, contracts, and choreography. The successful application of behavioral types requires a solid understanding of several practical aspects, from their representation in a concrete programming language, to their integration with other programming constructs such as methods and functions, to design and monitoring methodologies that take behaviors into account. This survey provides an overview of the state of the art of these aspects, which we summarize as the pragmatics of behavioral types.
Recently, robotic applications have been seeing widespread use across industry, often tackling safety-critical scenarios where software reliability is paramount. These scenarios often have unpredictable environments and, therefore, it is crucial to be able to provide assurances about the system at runtime. In this paper, we introduce ROSMonitoring, a framework to support Runtime Verification (RV) of robotic applications developed using the Robot Operating System (ROS). The main advantages of ROSMonitoring compared to the state of the art are its portability across multiple ROS distributions and its agnosticism w.r.t. the specification formalism. We describe the architecture behind ROS-Monitoring and show how it can be used in a traditional ROS example. To better evaluate our approach, we apply it to a practical example using a simulation of the Mars curiosity rover. Finally, we report the results of some experiments to check how well our framework scales.
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