Providing a semantic layer for structured data is relatively straightforward compared to dealing with the other 80% of corporate knowledge, which is in the form of unstructured information. “The semantic layer is not a product, a tool, or a specific solution,” said Lulit Tesfaye, partner and VP of knowledge and data services at Enterprise Knowledge, LLC. “Rather, it is a framework through which organizations can connect all of their knowledge.” Enterprise Knowledge is a consultancy dedicated to KM, originally assisting its clients with taxonomies and enterprise search and now focusing on knowledge graphs and ontologies.
Tesfaye cited the components of the semantic layer framework—knowledge assets, business glossary, metadata, taxonomy, and the knowledge graph. The value of knowledge graphs to KM is exemplified by an overseas wealth management organization that Enterprise Knowledge worked with to unify its view of its information. “The company had eight different systems for making deals and 12 that were centered around investments,” explained Joe Hilger, co-founder and COO of Enterprise Knowledge, LLC, “but they could not get a clear picture of what they had.”
Enterprise Knowledge pulled the most important information together, putting it into a single lens that showed everything about the company and all its deals. “They did not have a clear picture of the status of their deals,” Hilger commented, “and had previously attempted an AI solution that they deemed unsuccessful.” After organizing their metadata, creating ontologies, and developing a knowledge graph, the company was able to have an overall view of their activities and produce in 1 day a report that had previously taken 3 weeks. “They were able to gain insights into the state of their deals and achieve more leverage in their decision making,” noted Hilger.