New Facets of Semantic Interoperability: Adding JSON - JSON-LD Transformation Functionality to the BIG IoT API

The BIG IoT project focuses on easy integration of the IoT data and services offered by existing IoT platforms and services based on semantic technologies. To enable IoT applications to consume data and services provided by heterogeneous systems and different stakeholders, a common set of ontologies and an RDF triple store with querying functionalities are used in the service discovery phase. The subsequent communication between such applications or services (as consumer) and the provider can be optimized as well. The process of automatically transforming JSON-serialized responses from an IoT data provider to linked data format is discussed and the benefits of this automation are explored in this poster contribution

An Infrastructure for Spatial Linking of Survey Data

Research on environmental justice comprises health and well-being aspects, as well as topics related to general social participation. In this research field, among others, there is a need for an integrated use of social science survey data and spatial science data, e.g. for combining demographic information from survey data with data on pollution from spatial data. However, for researchers it is challenging to link both data sources, because (1) the interdisciplinary nature of both data sources is different, (2) both underlie different legal restrictions, in particular regarding data privacy, and (3) methodological challenges arise regarding the use of geo-information systems (GIS) for the processing and analysis of spatial data. In this article, we present an infrastructure of distributed web services which supports researchers in the process of spatial linking. The infrastructure addresses the challenges researchers have to face during that process. We present an example case study on the investigation of environmental inequalities with regards to income and land use hazards in Germany by using georeferenced survey data of the GESIS Panel and the German Socio-economic Panel (SOEP), and by using spatial data from the Monitor of Settlement and Open Space Development (IOER Monitor). The results show that increasing income of survey respondents is associated with less exposure to land-use-related environmental hazards in Germany

Interaction Network Analysis Using Semantic Similarity Based on Translation Embeddings

Biomedical knowledge graphs such as STITCH, SIDER, and Drugbank provide the basis for the discovery of associations between biomedical entities, e.g., interactions between drugs and targets. Link prediction is a paramount task and represents a building block for supporting knowledge discovery. Although several approaches have been proposed for effectively predicting links, the role of semantics has not been studied in depth. In this work, we tackle the problem of discovering interactions between drugs and targets, and propose SimTransE, a machine learning-based approach that solves this problem effectively. SimTransE relies on translating embeddings to model drug-target interactions and values of similarity across them. Grounded on the vectorial representation of drug-target interactions, SimTransE is able to discover novel drug-target interactions. We empirically study SimTransE using state-of-the-art benchmarks and approaches. Experimental results suggest that SimTransE is competitive with the state of the art, representing, thus, an effective alternative for knowledge discovery in the biomedical domain

Simple-ML: Towards a Framework for Semantic Data Analytics Workflows

In this paper we present the Simple-ML framework that we develop to support efficient configuration, robustness and reusability of data analytics workflows through the adoption of semantic technologies. We present semantic data models that lay the foundation for the framework development and discuss the data analytics workflows based on these models. Furthermore, we present an example instantiation of the Simple-ML data models for a real-world use case in the mobility domain. © 2019, The Author(s)

GADES: A graph-based semantic similarity measure

Knowledge graphs encode semantics that describes resources in terms of several aspects, e.g., neighbors, class hierarchies, or node degrees. Assessing relatedness of knowledge graph entities is crucial for several data-driven tasks, e.g., ranking, clustering, or link discovery. However, existing similarity measures consider aspects in isolation when determining entity relatedness. We address the problem of similarity assessment between knowledge graph entities, and devise GADES. GADES relies on aspect similarities and computes a similarity measure as the combination of these similarity values. We empirically evaluate the accuracy of GADES on knowledge graphs from different domains, e.g., proteins, and news. Experiment results indicate that GADES exhibits higher correlation with gold standards than studied existing approaches. Thus, these results suggest that similarity measures should not consider aspects in isolation, but combinations of them to precisely determine relatedness