Related papers: Leveraging Knowledge Graphs and LLMs for Context-Aware Messaging

Leveraging Knowledge Graphs and LLMs for Context-Aware Messaging

URL: http://arxiv.org/abs/2503.13499v1
Date: Wed, 12 Mar 2025 02:17:15 GMT
Title: Leveraging Knowledge Graphs and LLMs for Context-Aware Messaging
Abstract summary: This paper introduces a framework that uses the Knowledge Graph (KG) to dynamically rephrase written communications.<n>The extraction of relevant information, such as preferences, professional roles, and cultural norms, is then combined with the original message.<n>The framework demonstrates notable message acceptance rates in various domains: 42% in healthcare, 53% in education, and 78% in professional recruitment.
Score: 2.8402080392117757
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Personalized messaging plays an essential role in improving communication in areas such as healthcare, education, and professional engagement. This paper introduces a framework that uses the Knowledge Graph (KG) to dynamically rephrase written communications by integrating individual and context-specific data. The knowledge graph represents individuals, locations, and events as critical nodes, linking entities mentioned in messages to their corresponding graph nodes. The extraction of relevant information, such as preferences, professional roles, and cultural norms, is then combined with the original message and processed through a large language model (LLM) to generate personalized responses. The framework demonstrates notable message acceptance rates in various domains: 42% in healthcare, 53% in education, and 78% in professional recruitment. By integrating entity linking, event detection, and language modeling, this approach offers a structured and scalable solution for context-aware, audience-specific communication, facilitating advanced applications in diverse fields.

Related papers

Enhancing Persona Classification in Dialogue Systems: A Graph Neural Network Approach [0.0]
This study proposes a framework that combines text embeddings with Graph Neural Networks (GNNs) for effective persona classification. Given the absence of dedicated persona classification datasets, we create a manually annotated dataset to facilitate model training and evaluation. Our approach, in particular the integration of GNNs, significantly improves classification performance, especially with limited data.
arXiv Detail & Related papers (2024-12-17T19:27:24Z)
Bridging Local Details and Global Context in Text-Attributed Graphs [62.522550655068336]
GraphBridge is a framework that bridges local and global perspectives by leveraging contextual textual information. Our method achieves state-of-theart performance, while our graph-aware token reduction module significantly enhances efficiency and solves scalability issues.
arXiv Detail & Related papers (2024-06-18T13:35:25Z)
Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems [58.561904356651276]
We introduce the Knowledge-Enhanced Entity Representation Learning (KERL) framework to improve the semantic understanding of entities for Conversational recommender systems. KERL uses a knowledge graph and a pre-trained language model to improve the semantic understanding of entities. KERL achieves state-of-the-art results in both recommendation and response generation tasks.
arXiv Detail & Related papers (2023-12-18T06:41:23Z)
Contextual information integration for stance detection via cross-attention [59.662413798388485]
Stance detection deals with identifying an author's stance towards a target. Most existing stance detection models are limited because they do not consider relevant contextual information. We propose an approach to integrate contextual information as text.
arXiv Detail & Related papers (2022-11-03T15:04:29Z)
Enhanced Knowledge Selection for Grounded Dialogues via Document Semantic Graphs [123.50636090341236]
We propose to automatically convert background knowledge documents into document semantic graphs. Our document semantic graphs preserve sentence-level information through the use of sentence nodes and provide concept connections between sentences. Our experiments show that our semantic graph-based knowledge selection improves over sentence selection baselines for both the knowledge selection task and the end-to-end response generation task on HollE.
arXiv Detail & Related papers (2022-06-15T04:51:32Z)
JAKET: Joint Pre-training of Knowledge Graph and Language Understanding [73.43768772121985]
We propose a novel joint pre-training framework, JAKET, to model both the knowledge graph and language. The knowledge module and language module provide essential information to mutually assist each other. Our design enables the pre-trained model to easily adapt to unseen knowledge graphs in new domains.
arXiv Detail & Related papers (2020-10-02T05:53:36Z)
Dialogue Relation Extraction with Document-level Heterogeneous Graph Attention Networks [21.409522845011907]
Dialogue relation extraction (DRE) aims to detect the relation between two entities mentioned in a multi-party dialogue. We present a graph attention network-based method for DRE where a graph contains meaningfully connected speaker, entity, entity-type, and utterance nodes. We empirically show that this graph-based approach quite effectively captures the relations between different entity pairs in a dialogue as it outperforms the state-of-the-art approaches.
arXiv Detail & Related papers (2020-09-10T18:51:48Z)
Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning [73.0598186896953]
We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs. Building upon entity-level masked language models, our first contribution is an entity masking scheme. In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training.
arXiv Detail & Related papers (2020-04-29T14:22:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.