The power of data is indisputable and so is the challenge posed by poor quality data to procurement organizations that are turning increasingly to data for spotting patterns, predicting spend, and improving processes and procurement performances. Good data is the key to spend analysis and one of the cornerstones of good data in the context of spend analysis is structure.
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Semantic Artificial Intelligence (AI) emerged as a result of the limitations in Machine Learning (ML). However, it has grown from there to drive semantic search engines with the help of ontologies, semantic extraction using an ontology-based knowledge model, and chatbots infused with the domain knowledge of an organization. But, where is semantic AI heading and how will it impact enterprise automation?
Data for drug discovery and healthcare is often trapped in silos. This hampers effective interpretation and reuse. An antidote is to link data both internally and externally and create a Findable, Accessible, Interoperable, and Reusable (FAIR) data landscape that can power semantic models and knowledge graphs.
Scientists and clinicians have responded to the rapid spread of COVID-19, by generating new research materials. This has given rise to two unique challenges. One, it is humanly impossible for scientists and clinicians to review the vast amount of new research on time. Two, there is no way to establish the quality of these preprint materials as they have not undergone peer-review.
A topic cluster is a content taxonomy method that uses a single page as a hub for many posts. The single page can be visualized as a pillar, with many posts clustering around it. A topic cluster offers many benefits such as helping to organize an internal content calendar and associating different high-level entities to a brand. But, is this model beneficial for all enterprises that are deploying content marketing at scale?