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LLM-Powered Knowledge Graphs for Enterprise Intelligence and Analytics

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arxiv 2503.07993 v1 pith:WKDA6B3I submitted 2025-03-11 cs.AI

Rajeev Kumar , Kumar Ishan , Harishankar Kumar , Abhinandan Singla This is my paper
classification cs.AI
keywords analyticsdataenterpriseknowledgeactionableadvanceddisconnecteddiscovery
verification ladder T0 review T1 audit T2 compute T3 formal
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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 introduces 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, enabling advanced querying, reasoning, and analytics across data types like emails, calendars, chats, documents, and logs. Designed for enterprise flexibility, it supports applications such as contextual 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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. From Unstructured Communication to Intelligent RAG: Multi-Agent Automation for Supply Chain Knowledge Bases

    cs.AI 2025-06 conditional novelty 5.0 of 10

    Converting raw support tickets into a 3.4%-volume, category-structured knowledge base with three LLM agents improves RAG helpful answers from 38.60% to 48.74% on a real supply chain ticket dataset.

  2. Matching Game Preferences Through Dialogical Large Language Models: A Perspective

    cs.AI 2025-07 conditional novelty 4.0 of 10

    This perspective paper proposes the D-LLM framework, which couples the authors' GRAPHYP knowledge graphs with LLMs to personalize AI responses and make reasoning traceable, but no empirical validation is presented.

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