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A major obstacle to the wider use of semantic technology is the perceived complexity of RDF data by stakeholders who are not familiar with the Linked Data paradigm, or are otherwise unaware of a dataset's underlying schema. In order to help overcome this barrier, we propose the ExConQuer Framework (Explore, Convert, and Query Framework) as a set of tools that preserve the semantic richness of the data model while catering for simplified and workable views of the data. Through the available tools users are able to explore and query linked open datasets without requiring any knowledge of SPARQL or the datasets' underlying schema. Moreover, executed queries are persisted so that they can be easily explored and re-used, and even edited. Therefore, with this framework we attempt to target the evident niche in existing tools that are intended to be used by non-experts to consume Linked Data to its full potential. [ABSTRACT FROM AUTHOR]

Climate science has become more ambitious in recent years as global awareness about the environment has grown. To better understand climate, historical climate(e.g. archived meteorological variables such as temperature, wind, water, etc.) and climate-related data (e.g. geographical features and human activities) are widely used by today's climate research to derive models for an explainable climate change and its effects. However, such data sources are often dispersed across a multitude of disconnected data silos on the Web. Moreover, there is a lack of advanced climate data platforms to enable multi-source heterogeneous climate data analysis, therefore, researchers must face a stern challenge in collecting and analyzing multi-source data. In this paper, we address this problem by proposing a climate knowledge graph for the integration of multiple climate data and other data sources into one service, leveraging Web technologies (e.g. HTTP) for multi-source climate data analysis. The proposed knowledge graph is primarily composed of data from the National Oceanic and Atmospheric Administration's daily climate summaries, OpenStreetMap, and Wikidata, and it supports joint data queries on these widely used databases. This paper shows, with a use case in Ireland and the United Kingdom, how climate researchers could benefit from this platform as it allows them to easily integrate datasets from different domains and geographical locations. • Interoperable knowledge graphs advances integrating and updating multi-source data. • OpenStreetMap data enhance the geographical features of the knowledge graph data. • Exploring the knowledge graph structures visually is necessary for non-experts. [ABSTRACT FROM AUTHOR]

Abstract: DBpedia is one of the largest datasets in the linked Open Data cloud. Its centrality and its cross-domain nature makes it one of the most important and most referred to knowledge bases on the Web of Data, generally used as a reference for data interlinking. Yet, in spite of its authoritative aspect, there is no work so far tackling the provenance aspect of DBpedia statements. By being extracted from Wikipedia, an open and collaborative encyclopedia, delivering provenance information about it would help to ensure trustworthiness of its data, a major need for people using DBpedia data for building applications. To overcome this problem, we propose an approach for modelling and managing provenance on DBpedia using Wikipedia edits, and making this information available on the Web of Data. In this paper, we describe the framework that we implemented to do so, consisting in (1) a lightweight modelling solution to semantically represent provenance of both DBpedia resources and Wikipedia content, along with mappings to popular ontologies such as the W7 –what, when, where, how, who, which, and why – and OPM – open provenance model – models, (2) an information extraction process and a provenance-computation system combining Wikipedia articles’ history with DBpedia information, (3) a set of scripts to make provenance information about DBpedia statements directly available when browsing this source, as well as being publicly exposed in RDF for letting software agents consume it. [Copyright &y& Elsevier]