Guarded tuple-generating dependencies (GTGDs) are a natural extension of description logics and referential constraints. It has long been known that queries over GTGDs can be answered by a variant of the chase —a quintessential technique for reasoning with dependencies. However, there has been little work on concrete algorithms and even less on implementation. To address this gap, we revisit Datalog rewriting approaches to query answering, where a set of GTGDs is transformed to a Datalog program that entails the same base facts on each base instance. We show that a rewriting consists of “shortcut” rules that circumvent certain chase steps, we present several algorithms that compute a rewriting by deriving such “shortcuts” efficiently, and we discuss important implementation issues. Finally, we show empirically that our techniques can process complex GTGDs derived from synthetic and real benchmarks and are thus suitable for practical use.

Greenhouse gas emissions have become a common means for determining the carbon footprint of any commercial activity, ranging from booking a trip or manufacturing a product to training a machine learning model. However, calculating the amount of emissions associated with these activities can be a difficult task, involving estimations of energy used and considerations of location and time period. In this paper, we introduce the Transparent Emissions Calculation (TEC) toolkit, an open source effort aimed at addressing this challenge. Our contributions include two ontologies (ECFO and PECO) that represent emissions conversion factors and the provenance traces of carbon emissions calculations (respectively), a public knowledge graph with thousands of conversion factors (with their corresponding YARRRML and RML mappings) and a prototype carbon emissions calculator which uses our knowledge graph to produce a transparent emissions report. Resource permanent URL: https://w3id.org/tec-toolkit .

Guarded tuple-generating dependencies (GTGDs) are a natural extension of description logics and referential constraints. It has long been known that queries over GTGDs can be answered by a variant of the chase ---a quintessential technique for reasoning with dependencies. However, there has been little work on concrete algorithms and even less on implementation. To address this gap, we revisit Datalog rewriting approaches to query answering, where GTGDs are transformed to a Datalog program that entails the same base facts on each base instance. We show that the rewriting can be seen as containing "shortcut" rules that circumvent certain chase steps, we present several algorithms that compute the rewriting by simulating specific types of chase steps, and we discuss important implementation issues. Finally, we show empirically that our techniques can process complex GTGDs derived from synthetic and real benchmarks and are thus suitable for practical use.