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Knowledge Management

Give people and GenAI the content they need.

Organizational knowledge is often scattered across different departments and applications, or stuck in unstructured documents. Search across these content repositories is not effective and the content is often not usable by AI.

By contrast, knowledge graphs can map to any content – across any application, business unit, or geography. In addition, search functionality can be radically improved by enriching metadata with semantic meaning.

Get the right information to the right person at the right time with semantics:

Harmonize

tag sets, using LLM automation

Build

links and enrich meaning to improve search

Unite

concepts across disparate systems

Knowledge Graph

Harmonize

Automation: Connect and link every new tag or concept with EDG and LLMs.

Data Quality:  helps content managers update content relevancy, ownership, and similarity across

Build

Semantics creates a “Google-like” experience for organizational knowledge, as it helps comprehend the meaning behind user queries, and by understanding user intent deliver more relevant results. Benefits include:

  • ‍Entity Recognition and Disambiguation
  • Contextualized Search Results
  • ‍Query Expansion and Refinement
  • Personalized Search and Search by Role
  • Semantic Search across Languages

Unite

Instead of struggling to manage different repositories, each with their own limited tag-set management capabilities, you can use knowledge graphs as a single overarching semantic framework and content-tag management tool. It is a data-centric approach that isn’t limited by applications’ built-in capabilities or integrations. Instead of being locked in a specific application, you can layer together any CMS, like SharePoint and create a single view of them. The knowledge graph is composed in an open data standard that can be consumed by any application, or even AI.
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