Count Distinct Semantic Queries over Multiple Linked Datasets

In this paper, we revise count distinct queries and their semantics over datasets with incomplete knowledge, which is a typical case for the linked data integration scenario where datasets are viewed as ontologies. We focus on counting individuals present in the signature of the ontology. Specifically, we investigate the Certain Epistemic Count (CEC) and the Possible Epistemic Count (PEC) interval based semantics. In the case of CEC semantics, we propose an algorithm for its evaluation and we prove its correctness under a practical constraint of the queried ontology. We conduct and report experiments with the implementation of the proposed algorithm. We also prove decidability of the PEC semantics

Machine learning for predictive modelling of ambulance calls

A novel machine learning approach is presented in this paper, based on extracting latent information and using it to assist decision making on ambulance attendance and conveyance to a hospital. The approach includes two steps: in the first, a forward model analyzes the clinical and, possibly, non-clinical factors (explanatory variables), predicting whether positive decisions (response variables) should be given to the ambulance call, or not; in the second, a backward model analyzes the latent variables extracted from the forward model to infer the decision making procedure. The forward model is implemented through a machine, or deep learning technique, whilst the backward model is implemented through unsupervised learning. An experimental study is presented, which illustrates the obtained results, by investigating emergency ambulance calls to people in nursing and residential care homes, over a one-year period, using an anonymized data set provided by East Midlands Ambulance Service in United Kingdom

Query Answering over SROIQ Knowledge Bases with SPARQL

Abstract. W3C currently extends the SPARQL query language with so-called entailment regimes, which define how queries are evaluated using logical entailment relations. We describe a sound and complete algorithm for the OWL Direct Semantics entailment regime. Since OWL’s Direct Semantics is based on Description Logics (DLs), this results in an expressive query language for DL knowledge bases. The query language differs from the commonly studied conjunctive queries in that it only has distinguished variables. Furthermore, variables can occur within complex concepts and can also bind to concept or role names. We provide a prototypical implementation and propose several novel optimizations strategies. We evaluate the efficiency of the proposed optimizations and find that for ABox queries our system performs comparably to already deployed systems. For complex queries an improvement of up to three orders of magnitude can be observed.