# Special Issue on Smart Data and Semantics in a Sensor World
## 3 The Papers  
A total of 18 papers were submitted to the Special Issue on Smart Data and Semantics in a Sensor World. After peer review, four of them were accepted and published. Afterwards, an additional paper co-authored by the guest editors was submitted and managed independently by another editor of Applied Sciences, which after the review process was finally accepted and incorporated also to the special issue. The paper \"Using Adverse Weather Data in Social Media to Assist with City-Level Traffic Situation Awareness and Alerting\", authored by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], presents a forecasting and alerting approach for traffic incidents in a city. It exploits temporal, spatial, and meteorological features extracted from social media to estimate the city's traffic status and suggest the appropriate warning levels. The proposal is evaluated with a traffic incidents dataset (tweets extracted from Sina Weibo, a microblogging service for Chinese-speaking people). The paper \"Ontological Representation of Smart City Data: From Devices to Cities\", co-authored by [PERSON], [PERSON], [PERSON], and [PERSON], presents a systematic literature review of available smart city ontologies and proposes a set of ontology design patterns. These patterns, which provide core concepts to guide the development of ontologies for smart cities, are defined by considering the ontological requirements for smart city data and the findings of the survey presented. The paper \"MEBN-RM: A Mapping between Multi-Entity Bayesian Network and Relational Model\", co-authored by [PERSON] and [PERSON], presents MEBN-RM, a set of mapping rules between key elements of the knowledge representation formalism Multi-Entity Bayesian Network (MEBN), which is the basis of PR-OWL (a probabilistic extension to OWL), and the Relational Model (RM). An algorithm that transforms a relational schema into a MEBN model has been released as an open software tool. The proposal is illustrated with two example use cases for situation awareness (a critical infrastructure defense system and a smart manufacturing system), where sensor data are stored in relational databases and converted to MEBN models, and the performance is evaluated in terms of the mapping speed and the accuracy of the mapping. The paper \"Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens\", co-authored by [PERSON], [PERSON], [PERSON], and [PERSON], focuses on the problem of monitoring and forecasting of environmental conditions and reviews earth science data representation and access standards and technologies. It identifies nine key challenges that need to be tackled for an effective and efficient smart geospatial searching and browsing. Finally, the paper \"Semantic Traffic Sensor Data: The TRAFAIR Experience\", co-authored by [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], tackles the problem of modeling and semantic annotation of traffic data. It presents the tools and techniques used in the TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project for data modeling and its semantic enrichment in the cities of Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows the performance of the approach proposed to publish Linked Data. Paper writing, special issue coordination and management of submitted papers, S.I; Paper writing and management of submitted papers, L.P.; Paper writing and management of submitted papers, R.T.-L. All authors have read and agreed to the published version of the manuscript. We want to thank the support of the projects that fund our work in relation to the topics of this special issue: the TRAFAIR project 2017-EU-IA-0167, co-financed by the Connecting Europe Facility of the European Union, the project ITN2016-78011-C4-3-R (AEI/FEDER, UE), and the Government of Aragon (Group Reference T64_20R, COSMOS research group). This issue would have not been possible without the contributions of the authors who submitted their valuable papers.