Knowledge graphs built from Enterprise Social Networks

Photo by Eric Ziegler

More and more companies are building social networks in the enterprise. For those keeping track, the common term used today is Enterprise Social Networks (ESN).In almost all cases, the reasoning for implementing an ESN is to improve collaboration, break down silos, ease the flow information, etc. One that is often missing is how it could influence search results in the enterprise.

In the internet, social networks play a huge role in helping with search results. This help does not come in the straight up indexing of the content and adding to the overwhelming amount of content already being indexed, but rather from the building a a social or knowledge graph from the social networks.

Google, Microsoft, Facebook, etc. have built algorithms to mine the content in social networks and to try to understand the relationships between the interactions happening in the social networks. While this information is used for many purposes, the mining of the data is used build out what is called a social graph and to extend knowledge graphs (think really big taxonomies or ontologies).

There is a big opportunity for companies to leverage their ESN implementations to improve their search results. This concept goes beyond the idea of ESN and could even be applied to any location people interact with each other (online meetings, Chat sessions, even corporate email).

By mining the information from these interactions, a social graph of related to topics and concepts can be built, that will improve how people search and eventually find the information they are most interested in.

This note was inspired  +David Amerland book,  Google Semantic Search - Amazon location 2111.