Many modern companies wish to maintain knowledge in the form of a corporate knowledge graph and to use and manage this knowledge via a knowledge graph management system (KGMS). We formulate various requirements for a fully fledged KGMS. In particular, such a system must be capable of performing complex reasoning tasks but, at the same time, achieve efficient and scalable reasoning over Big Data with an acceptable computational complexity. Moreover, a KGMS needs interfaces to corporate databases, the web, and machine-learning and analytics packages. We present KRR formalisms and a system achieving these goals.

Data wrangling, the multi-faceted process by which the data required by an application is identified, extracted, cleaned and integrated, is often cumbersome and labor intensive. In this paper, we present an architecture that supports a complete data wrangling lifecycle, orchestrates components dynamically, builds on automation wherever possible, is informed by whatever data is available, refines automatically produced results in the light of feedback, takes into account the user's priorities, and supports data scientists with diverse skill sets. The architecture is demonstrated in practice for wrangling property sales and open government data.

No abstract

Abstract. Second-Order tuple generating dependencies (SO tgds) were introduced by Fagin et al. to capture the composition of simple schema mappings. Testing the equivalence of SO tgds would be important for applications like model management and mapping optimization. However, we prove the undecidability of the logical equivalence of SO tgds. Moreover, under weak additional assumptions, we also show the undecidability of a relaxed notion of equivalence between two SO tgds, namely the so-called conjunctive query equivalence.