How Semantic AI & Knowledge Graphs Can Turn M365 Environments Into a Smart Knowledge Hub
For many business professionals, looking for content at work is a lot like looking for a needle in a haystack or getting lost in a maze. Content management systems such as Microsoft SharePoint have helped, but searching for content still requires individuals to sift through a myriad of folders, subfolders, and site collections. The sheer number of places a typical business stores and controls documents and other files leaves business users in a never-ending search for information as they continuously hit dead ends and backtrack through folders. This may be why almost 60% of respondents using SharePoint Online said they need a better search option.
Organizations thought they solved this problem by using SharePoint as a shared folder only to realize a different problem had been created, as finding information in large repositories with complex folder structures proved to be cumbersome. The content inside these structures is significant and is typically organized by teams and projects differently. So, unless a person knows where the actual document resides or the specific terms in the file name, it will be nearly impossible to find. If they are lucky enough to find some choices, the search will often produce a vast list of largely irrelevant options, forcing them to waste large amounts of time shifting through the options. Frustration and time inefficiencies aside, the inability to access enterprise knowledge locked in documents results in a loss of productivity, lack of compliance, additional operational costs, and lags in decision making.
How Semantic AI Helps Users Dig Out of the Document Maze
For most organizations, be it a small nonprofit or a tech giant that employs thousands of people, CMSs serves as the backbone of their content maintenance and creation processes. While most reliable CMSs have their own native tagging and search functionalities, these pale in comparison to the benefits you can get with automated tagging methods and semantic capabilities. Autoclassification—or semantic concept tagging—allows users to automatically categorize and tag content with rich descriptors that enable advanced findability of content in search engines.
To help make information easier to find, many organizations are looking for help from semantic AI, which relies on machine learning, natural language processing, and knowledge graphs to understand the meaning behind text. Here, a knowledge graph is used at the heart of a semantic-enhanced AI architecture, which allows organizations to make better use out of unstructured data and make it easily integrated in existing systems and/or coupled with other AI approaches such as generative AI. Based on a collection of interlinked descriptions of concepts, entities, relationships, and events, knowledge graphs put data in context via linking and semantic metadata and provide a framework for data integration, unification, analytics, and sharing.
Smarter Search
By eliminating data silos, semantic AI enriches customer data and content and enables greater knowledge discovery across an organization. Due to its diverse capabilities, such as text mining, tagging, semantic search, etc., it can be implemented along the whole data and content lifecycle in order to develop intelligent applications. When integrated with an organization’s CMS, semantic AI can help individuals get the information they need sooner.