If you are facing your first taxonomy challenge, or just trying to get your head around what taxonomies can do to drive your content, then Taxonomy Boot Camp London’s Track A sessions will speak directly to you.
Bloomberg has launched a new website—Enterprise Access Point — to help firms derive value and enterprise-wide efficiencies. The website will enable enterprise clients, developers and data scientists to easily discover and act on ready-to-use datasets.
Today’s consumers have an ocean of content, including movies, programs, and news and short-form videos to choose from an array of linear and streaming services. However, the abundance of choice can be overwhelming, as viewers cannot find what they want to watch quickly and easily. The primary reason for this frustration is the lack of structured data as it drives search, recommendations and discovery. In their peer-reviewed technical paper, Lijin Chungapalli, Senior Software Engineer, Metadata Engineering Group, and Venkata Babji Perambattu, Engineering Manager at TiVo, demonstrate how to overcome this challenge.
Named entities are specific language elements that belong to predefined categories such as names, locations, organizations, chemical elements or names of space missions. They are not easy to find and classify. However, named entities are of significant help for various tasks such as improving search capabilities, relating documents with internal or with external information, and causes with effects. Hence, named entity recognition is one of the key components of information extraction (IE) and knowledge discovery (KD). In this blog post, Dr. Alessandro Negro, Chief Scientist and Dr. Vlasta Kůs, Data Scientist at GraphAware, highlight how combining graph models and named entity recognition (NER) can provide higher accuracy than the pure Stanford natural language processing (NLP) NER.
Disparate data is an asset for organizations. According to a TDWI research, organizations that use disparate data for analytics are more likely to measure top- or bottom-line impact from their analytics efforts than those that do not. Fern Halper, Ph.D., director of TDWI Research for advanced analytics, describes three real world use cases where disparate data is being used. For instance, to classify image and sound, input for predictive models, and chatbots in customer experience. In addition, Fern explores what the research has to say about accessing and using disparate data.