We want to know if the agent talked about their most important talking points: that is, did the agent ask if the customer has a budget, or the authority to make a decision or a timeline about when they need the new technology or whether they actually have expressed their need. None of this can be solved by only simple entity extraction but requires elaborate rule-based and machine learning techniques. The main challenge we tackle is to really deeply understand what the customer and agent are talking about. N3 Results helps big tech companies to sell their high tech solutions, mostly cloud-based products and services but also helps their clients sell many other technologies and services.
Knowledge graph builder software#
Our knowledge graph software provides real-time decision support to make the call center agents more efficient. We analyze in real-time the text chats and spoken conversations between call center agents and customers. Whereas the previous use case was very static, this one is highly dynamic. This is a completely different use case (See a recent KMWorld Article). A simple example we extracted from an author quoting Chomsky is that neoliberalism ultimately causes childhood death.Įxample 2: N3 Results and the Intelligent Call Center The biggest challenges for this project are finding causal relationships in his work using event and relationship extraction. Ultimately students, researchers, journalists, lobbyists, people from the AI community, and linguists can all use this knowledge graph for their particular goals and questions. The Chomsky Legacy Project is a project run by a group of admirers of Noam Chomsky with the primary goal to preserve all his written work, including all his books, papers and interviews but also everything written about him. First is the Chomsky Legacy Project where we have a large set of very dense documents and very different knowledge sources, Second is a knowledge graph for an intelligent call center where we have to deal with high volume dynamic data and real-time decision support and finally, a large government organization where it is very important that people can do a semantic search against documents and policies that steadily change over time and where it is important that you can see the history of documents and policies. We have applied these techniques for several Knowledge Graphs but in this document we will primarily focus on three completely different examples that we summarize below. In this document we discuss how the techniques described in can be combined with popular software tools to create a robust Document Knowledge Graph pipeline. A core competency for Franz Inc is turning text and documents into Knowledge Graphs (KG) using Natural Language Processing (NLP) and Machine Learning (ML) techniques in combination with AllegroGraph.