http://www.ronpub.com/index.php/journals/OJSW/issues?volume=4&issue=ALL All papers of this volume en-us https://www.ronpub.com/ojsw/OJSW_2017v4i1n01_Font.html http://nbn-resolving.de/urn:nbn:de:101:1-201705194949 With the development of knowledge graphs and the billions of triples generated on the Linked Data cloud, it is paramount to ensure the quality of data. In this work, we focus on one of the central hubs of the Linked Data cloud, DBpedia. In particular, we assess the quality of DBpedia for domain knowledge representation. Our results show that DBpedia has still much room for improvement in this regard, especially for the description of concepts and their linkage with the DBpedia ontology. Based on this analysis, we leverage open relation extraction and the information already available on DBpedia to partly correct the issue, by providing novel relations extracted from Wikipedia abstracts and discovering entity types using the dbo:type predicate. Our results show that open relation extraction can indeed help enrich domain knowledge representation in DBpedia. https://www.ronpub.com/ojsw/OJSW_2017v4i1n02_Kejriwal.html http://nbn-resolving.de/urn:nbn:de:101:1-2017100112160 Structured knowledge bases gain their expressive power from both the ABox and TBox. While the ABox is rich in data, the TBox contains the ontological assertions that are often necessary for logical inference. The crucial links between the ABox and the TBox are served by is-a statements (formally a part of the ABox) that connect instances to types, also referred to as classes or concepts. Latent space embedding algorithms, such as RDF2Vec and TransE, have been used to great effect to model instances in the ABox. Such algorithms work well on large-scale knowledge bases like DBpedia and Geonames, as they are robust to noise and are low-dimensional and real-valued. In this paper, we investigate a supervised algorithm for deriving type embeddings in the same latent space as a given set of entity embeddings. We show that our algorithm generalizes to hundreds of types, and via incremental execution, achieves near-linear scaling on graphs with millions of instances and facts. We also present a theoretical foundation for our proposed model, and the means of validating the model. The empirical utility of the embeddings is illustrated on five partitions of the English DBpedia ABox. We use visualization and clustering to show that our embeddings are in good agreement with the manually curated TBox. We also use the embeddings to perform a soft clustering on 4 million DBpedia instances in terms of the 415 types explicitly participating in is-a relationships in the DBpedia ABox. Lastly, we present a set of results obtained by using the embeddings to recommend types for untyped instances. Our method is shown to outperform another feature-agnostic baseline while achieving 15x speedup without any growth in memory usage. https://www.ronpub.com/ojsw/OJSW_2017v4i1n03_Pandey.html http://nbn-resolving.de/urn:nbn:de:101:1-201711266890 There has been an exponential growth and availability of both structured and unstructured data that can be leveraged to provide better emergency management in case of natural disasters and humanitarian crises. This paper is an extension of a semantics-based web application for safety check, which uses of semantic web technologies to extract different kinds of relevant data about a natural disaster and alerts its users. The goal of this work is to design and develop a knowledge intensive application that identifies those people that may have been affected due to natural disasters or man-made disasters at any geographical location and notify them with safety instructions. This involves extraction of data from various sources for emergency alerts, weather alerts, and contacts data. The extracted data is integrated using a semantic data model and transformed into semantic data. Semantic reasoning is done through rules and queries. This system is built using front-end web development technologies and at the back-end using semantic web technologies such as RDF, OWL, SPARQL, Apache Jena, TDB, and Apache Fuseki server. We present the details of the overall approach, process of data collection and transformation and the system built. This extended version includes a detailed discussion of the semantic reasoning module, research challenges in building this software system, related work in this area, and future research directions including the incorporation of geospatial components and standards.