Background: Access to medical care is strongly dependent on resource allocation, such as the geographical distribution of medical facilities. Nevertheless, this data is usually restricted to country official documentation, not available to the public. While some medical facilities' data is accessible as semantic resources on the Web, it is not consistent in its modeling and has yet to be integrated into a complete, open, and specialized repository. This work focuses on generating a comprehensive semantic dataset of medical facilities worldwide containing extensive information about such facilities' geo-location. Results: For this purpose, we collect, align, and link various open-source databases where medical facilities' information may be present. This work allows us to evaluate each data source along various dimensions, such as completeness, correctness, and interlinking with other sources, all critical aspects of current knowledge representation technologies. Conclusions: Our contributions directly benefit stakeholders in the biomedical and health domain (patients, healthcare professionals, companies, regulatory authorities, and researchers), who will now have a better overview of the access to and distribution of medical facilities.

In the below, Dr Scott Zoldi, chief analytics officer at analytic software firm FICO, explains to Information Age why data science cowboys and citizen data scientists could cause catastrophic failures to a business' AI and analytics ambitions. Although the future will see fast-paced adoption and benefits driven by applying AI to all types of businesses, we will also see catastrophic failures due to the over-extension of analytic tools, and the rise of citizen data scientists and data science cowboys. The former does not have data science training but uses analytic tooling and methods to bring analytics into their businesses; the latter has data science training, but a disregard for the right way to handle AI. Citizen data scientists often use algorithms and technology they don't understand, which might result in inappropriate use of their AI tools; the risk from the data science cowboys is that they build AI models that may incorporate non-causal relationships learned from limited data, spurious correlations and outright bias -- which could have serious consequences for driverless car systems, for example. Today's AI threat stems from the efforts of both citizen data scientists and data scientist cowboys to tame complex machine learning algorithms for business outcomes.

Concept-to-text generation refers to the task of automatically producing textual output from non-linguistic input. We present a joint model that captures content selection ("what to say") and surface realization ("how to say") in an unsupervised domain-independent fashion. Rather than breaking up the generation process into a sequence of local decisions, we define a probabilistic context-free grammar that globally describes the inherent structure of the input (a corpus of database records and text describing some of them). We recast generation as the task of finding the best derivation tree for a set of database records and describe an algorithm for decoding in this framework that allows to intersect the grammar with additional information capturing fluency and syntactic well-formedness constraints. Experimental evaluation on several domains achieves results competitive with state-of-the-art systems that use domain specific constraints, explicit feature engineering or labeled data.