LLM-Powered Knowledge Graphs for Enterprise Intelligence and Analytics — Kumar et al. (2025)

Disconnected data silos within enterprises obstruct the extraction of actionable insights, diminishing efficiency in areas such as product development, client engagement, meeting preparation, and analytics-driven decision-making. This paper in-troduces a framework that uses large language models (LLMs) to unify various data sources into a comprehensive, activity-centric knowledge graph. The framework automates tasks such as entity extraction, relationship inference, and semantic enrichment, en-abling advanced querying, reasoning, and analytics across data types like emails, calendars, chats, documents, and logs. Designed for enterprise flexibility, it supports applications such as contex-tual search, task prioritization, expertise discovery, personalized recommendations, and advanced analytics to identify trends and actionable insights. Experimental results demonstrate its success in the discovery of expertise, task management, and data-driven decision making. By integrating LLMs with knowledge graphs, this solution bridges disconnected systems and delivers intelligent analytics-powered enterprise tools. This Brief reads the authors' abstract and conclusion in the authors' own words. Authors: R. Kumar, K. Ishan, H. Kumar, A. Singla Abstract source: PDF extraction Publication date: 2025 Read the paper Source license: Creative Commons Attribution 4.0 International Narration: Read verbatim by an AI-generated voice. The authors and publisher do not sponsor or endorse this recording.