The vision of @math -DB introduces deterministic scientific hypotheses as a kind of uncertain and probabilistic data, and opens some key technical challenges for enabling data-driven hypothesis management and analytics. The @math -DB system addresses those challenges throughout a design-by-synthesis pipeline that defines its architecture. It processes hypotheses from their XML-based extraction to encoding as uncertain and probabilistic U-relational data, and eventually to their conditioning in the presence of observations. In this demo we present a first prototype of the @math -DB system. We showcase its core innovative features by means of use case scenarios in computational science in which the hypotheses are extracted from a model repository on the web and evaluated (rated ranked) as probabilistic data.
. Finally, our framework is comparable with Bioinformatics' initiatives that address hypothesis encoding into the RDF data model @cite_18 . We point out the Robot Scientist, HyBrow and SWAN (cf. @cite_18 ), all of which consist in some ad-hoc RDF encoding of sequence and genome analysis hypotheses under varying levels of structure (viz., from gene G has function A' statements to free text). Our framework in turn consists in the U-relational encoding of hypotheses from mathematical equations, which is (to our knowledge) the first work on hypothesis relational encoding.
@cite_18
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With the rise of the Semantic Web more and more data become available encoded using the Semantic Web standard RDF. RDF is faced towards machines: designed to be easily processable by machines it is difficult to be understood by casual users. Transforming RDF data into human-comprehensible text would facilitate non-experts to assess this information. In this paper we present a languageindependent method for extracting RDF verbalization templates from a parallel corpus of text and data. Our method is based on distant-supervised simultaneous multi-relation learning and frequent maximal subgraph pattern mining. We demonstrate the feasibility of our method on a parallel corpus of Wikipedia articles and DBpedia data for English and German.