Workshop on Enterprise Knowledge Graphs using Large Language Models

Abstract

Knowledge graphs are used for organizing and connecting individual entities to integrate the information extracted from different data sources. Typically, knowledge graphs are used to connect various real-world entities like persons, places, things, actions, etc. For the knowledge graphs created using the enterprise data, the knowledge graph entities can be of different types-static entities (e.g., people, projects), communication entities (e.g., emails, meetings, documents), derived entities (e.g., rules, definitions, entities from emails), etc. The graphs are used to connect these entities with enriched context (as edges and node attributes) and used for powering various search and recommendations applications. With the advent of large language models, the whole lifecycle of knowledge graphs involving -information extraction, graph construction, application of graphs, querying knowledge graphs, using the graph for recommendations, etc., - is impacted. With large language models such as GPT, LLaMA, PALM, etc., entity and relationship extraction can be improved. Similarly, one can answer different types of queries with the help of LLMs which were very difficult without them. This workshop is about improving the enterprise knowledge graphs and its applications using large language models. Enterprise graphs can be of different scopes-whether they contain data from individual users/customers, a sub-organization, or the whole enterprise. This workshop will also cover various privacy and access control related issues which are typical for any enterprise graph. These include privacy preserving federated learning, using LLMs to extract information from private data, querying the knowledge graph in a privacy preserving manner, etc.