Titlebook: On the Move to Meaningful Internet Systems. OTM 2017 Conferences; Confederated Interna Hervé Panetto,Christophe Debruyne,Robert Meersman Co

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Linked Data and Ontology Reference Model for Infectious Disease Reporting Systemsr contents and meta-models semantically, using ontologies in their backbone. This semantic enhancement brings an add-on value to standard systems, enabling an overall better data management and allowing a more intelligent data processing. In our work we focus on such a standard system, which we enha

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PFSgeo: Preference-Enriched Faceted Search for Geographical Dataduced extension, that we call ., the objects can have geographical coordinates, the interaction model is extended, and the web-interface is enriched with a map which the user can use for inspecting and restricting his focus, as well as for expressing .. Preference inheritance is supported as well as

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Speeding up Publication of Linked Data Using Data Chunking in LinkedPipes ETL processing examples, or smaller real world data. However, a majority of these tools become hard to use when faced with a larger dataset such as hundreds of megabytes large CSV file. Tools which load the entire resulting RDF dataset into memory usually have memory requirements unsatisfiable by commo

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Agent-Based Assistance in Ambient Assisted Living Through Reinforcement Learning and Semantic Techno agents in ambient assisted environments provide services that aim at supporting elderly and impaired people. However, these agents act in complex stochastic and indeterministic environments where the concrete effects of a performed action are usually unknown at design time. Furthermore, they have t

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On the Need for Applications Aware Adaptive Middleware in Real-Time RDF Data Analysis (Short Paper)eral RDF Stream Processing (RSP) engines are available to query those data streams. Application designers have the freedom to select the best available RSP engine based on their application requirements. However, this selection needs to be done at design time resulting in early bound rigid solutions

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Learning Probabilistic Relational Models Using an Ontology of Transformation Processesrning them is a difficult task. In this paper, we propose a method that learns a PRM from data using the semantic knowledge of an ontology describing these data in order to make the learning easier. To present our approach, we describe an implementation based on an ontology of transformation process

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ORDAIN: An Ontology for Trust Management in the Internet of Thingso achieve interoperability and knowledge diffusion. Furthermore, this open, distributed and heterogeneous environment raises important challenges, such as trustworthiness among the various types of devices and participants. Developing and sharing ontologies that support trust management models and a

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