Semantic Concept Tagging

Enterprises must be able to turn their data into knowledge assets in order to stay competitive. However, because data comes in large volumes, different formats, and types, organizations often have great difficulty ensuring that existing data can be specifically reused for the next steps such as analysis or reporting. This is where tagging comes in. Tags, or metadata, are information building blocks that help classify information assets, making them easier to find, use and link to each other.

However, manually tagging entire databases, file by file, is very time-consuming and involves a lot of people. To make matters worse, one of the common results of manual tagging is inconsistent search results. Even with clear guidelines, people end up developing their own way of assigning metadata and create inconsistencies. Therefore, manual tagging without controlled vocabularies is not scalable and doesn’t work for organizations that store large quantities of files.

This is why some of the world’s largest corporations use the PoolParty Semantic Suite and its world-class text-mining and entity extraction capabilities to automatically tag information assets with high precision. PoolParty is unique because it combines knowledge graphs, machine learning, natural language processing, and graph algorithms to better analyze text by not only processing words but understanding the underlying concepts and their context. As a result, our clients are able to create high-quality active metadata that improves search capabilities and build recommendation engines that deliver meaningful content.