Environmental, social and governance (ESG) investments have fast become an important area of interest. It was estimated that sustainable investments amounted to some $30 trillion in 2018, up by 34 per cent from 2016. Indeed, investors (and our societies in general) are increasingly keen to understand whether and by what means businesses are being environmentally and socially responsible and governed. Simultaneously, boards and managements have become cognisant that ESG is crucial to the long-term survival of their companies. Small wonder, then, that some 90 per cent of investors globally already have in place, or have plans to develop, specific ESG investment policies.

According to a recent Deloitte report on the rise of Cloud and AI deployments in the IT industry, Paul Sallomi had explained the intricate relationship between these two technologies. The most popular path to accelerate multi-cloud and hybrid Cloud deployments go through the acquisition of AI capabilities. Bringing AI to the center of all Cloud and MSP operations help in optimizing internal businesses in addition to further enhancing various products, solutions and services associated with Product Development, Marketing and Sales, Customer interactions, and Partner vendor management channels. Gartner suggests the need to urgently deploy AI capabilities has its own set of pitfalls– mostly, customers make a hasty choice while choosing the best Cloud AI developers and put billions of dollars at risk. Security and Virtualization are other challenges in this regard.

In the expanding Semantic Web, an increasing number of sources of data and knowledge are accessible by human and software agents. Sources may differ in granularity or completeness, and thus be complementary. Consequently, unlocking the full potential of the available knowledge requires combining them. To this aim, we define the task of knowledge reconciliation, which consists in identifying, within and across sources, equivalent, more specific, or similar units. This task can be challenging since knowledge units are heterogeneously represented in sources (e.g., in terms of vocabularies). In this paper, we propose a rule-based methodology for the reconciliation of $n$-ary relations. To alleviate the heterogeneity in representation, we rely on domain knowledge expressed by ontologies. We tested our method on the biomedical domain of pharmacogenomics by reconciling 50,435 $n$-ary relations from four different real-world sources, which highlighted noteworthy agreements and discrepancies within and across sources.

Shaun Gupta has a MSci in Physics from UCL and a PhD in Particle Physics from Oxford. He tells us how he started his career in Data Science and what being a Data Scientist is like. Tell us about your job. I am currently employed as a Data Scientist at a startup called Row Analytics. Data Science is an emerging field, and it involves using a mixture of coding and statistical analysis to answer questions using big datasets.