Media Organization Leverages Progress Semaphore for Automated Content Tagging to Improve User Experience

In today’s content-saturated, hyper-connected world, digital media publishers, information providers and media organizations face unique challenges when it comes to keeping audiences engaged.

In the US alone, consumers spend approximately 44% of each day interacting with media, and seven out of 10 homes have some form of content streaming. While competition is fierce, the industry is exhibiting growth; global media and entertainment revenues are forecast to reach $2.4 trillion in the next few years.

Building a successful brand in this dynamic ecosystem comes with significant challenges. Trying to compete in an overly fragmented marketplace is forcing media and publishing organizations to create more content and partner with multiple distribution platforms– leading to even greater fragmentation. Once loyal audiences are frustrated as they attempt to find a coherent media experience across multiple platforms and devices.

One organization integrated the Progress^® Semaphore^™ Semantic AI platform into their digital ecosystem to create an automated, transparent and consistent content classification process that results in precise and consistent tagging and improves customer and user search and retrieval experiences.

The platform Semaphore replaced was no longer supported by its vendor. Additionally, the previous platform relied on manually crafted rules therefore, given the velocity of change in any given news day, dozens of new rules had to be manually written and tested each day. Semaphore provided a future path for the organization with a unified expandable and consistent platform for additional use cases and coverage for additional products.

Content tagging at the organization consisted of hundreds of thousands of manually crafted rules that were developed over a 15-year period. Each rule identified the relevant words, title and subjects found in each article over an accumulated history of tens of thousands of descriptors, encompassing organizations, geographies, people and titles. Due to the sheer volume, rules were often duplicated and redundant.

Starting with a proof of concept, they demonstrated that Semaphore could produce precise, complete and accurate rules – in the same way as the existing system. The Semaphore challenge: moving the organization away from the manual rule writing mindset to a concept/model driven approach that supports collaboration with subject matter experts and is easily maintainable and sustainable over the long haul.

By using Knowledge Model Management (KMM) they developed a sophisticated concept model to more efficiently and collaboratively manage the topics and entities referenced in their previous system while still delivering the power of a rules-based approach to classification. The Lexical Enrichment Side Panel (LEX) assists in model development by suggesting synonyms and signpost terms. The Knowledge Review Tool (KRT) incorporates subject matter experts into the model development process by reviewing the model and suggesting relevant model concepts using the vocabulary of the business. Model developers can review suggested terms and incorporate them as required.

KMM, Document Analyzer and Classification Analysis Tool are deployed to test and validate rule development. Semaphore’s sophisticated rules language and NLP engine examine content and look for evidence of concepts—not strings. This results in rules that are derived from the model, written once and reused across content repositories, resulting in consistent and transparent classification. Semantic Integration Services and user experience widgets are used to assist authors and editors in selecting tags not automatically applied in classification.