A Linked Data Driven Visual Interface for the Multi perspective Exploration of Data Across Repositories
As more data from heterogeneous sources become available, interfaces that support the federated exploration of these data are gaining importance to uncover relations between entities across multiple sources. Instead of explicit queries, visual interfaces enable a follow your nose style of exploration by which a user can seamlessly navigate between entities from different data sources. This requires an alignment of the ontologies used by said sources as well as the coreference resolution of entities across them. Together with Semantic Web technologies, the Linked Data paradigm provides the technological foundations to address these challenges. Nonetheless, the majority of work studies these components in isolation, focusing either on the alignment, coreference resolution, or visualization. Some interesting aspects, however, only arise when all puzzle pieces are in place. Two of these aspects are the seamless transitions between visualization and interaction paradigms as well as the combination of entity and type queries. In this work, we present a multi perspective visual interface that enables the seamless exploration of major scientific geo data sources that contain millions of RDF triples. Introduction and Motivation Linked Data as a paradigm describes how to break up data silos and support the publication, retrieval, reuse, and interlinkage of data on the Web. Together with other Semantic Web technologies, Linked Data shows promise to address many challenges that have affected semantic interoperability between repositories and services within and across domains that are highly heterogeneous in nature, e.g. the broader geosciences [9] However, making use of the largely machine oriented global graph of Linked Data also requires human centric interfaces to query data or to explore it by following links across entities and even repositories. Unsurprisingly, user interfaces, vocabularies for their creation, and visual aids for the construction of SPARQL queries have been an active research area for many years [3,13,5,11,16] The integration and deployment of such interfaces on top of heterogeneous and conflated sources, however, is still rare. In other words, research on topics such as ontology alignment, coreference resolution, visualization, querying, and so forth, often takes place in isolation. As a consequence, findings that only emerge once the full stack is implemented are frequently overlooked. One such example is the fact that co reference resolution without data conflation (fusion) hampers the reuse of data while one would intuitively assume that the opposite is true. The reason for this lies in the fact that data sources which contain data about the same entities share overlapping information. Consequently, to give a concrete example, establishing that two URIs identify the same entity without fusing the data about them leads to places having more than one (and different) population counts, geographic coordinates, names, and so forth, e.g. for Kobe, Japan in DBpedia and GeoNames.1 In this work, we are interested in aspects that arise from exploring Linked Data via graphical user interfaces and more specifically in three observations made when sharing scientific data from several major oceanographic repositories. (I) There is no one size fits all visualization and interaction paradigms. However, offering multiple perspectives on data works only if users can seamlessly change between these perspectives. (II) exploratory interfaces such as implemented by the popular Relfinder [10] benefit from query capabilities that enabled the user to select entities as well as classes as nodes. (III) With an increasing number of data sources (and triples) aspects that may seem like mere convenience function become essential features, e.g. the ability to expand and compress local nodes and edges in a graph view, support for multiple layers (from multiple data sources) in a map view, and so forth.
VOILA@ISWC