Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering

Baek, Jinheon; Aji, Alham Fikri; Saffari, Amir

doi:10.18653/v1/2023.nlrse-1.7

Cited by 18 publications

(7 citation statements)

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“…They have grouped these works into three broad categories—knowledge‐graph driven inference, pretraining, and fine‐tuning. Knowledge graphs are also being considered as a way to generate relevant prompts at inference time as proposed in Baek et al (2023), and in Wu et al (2023). However, these works use only the textual components but not the graph structures.…”

Section: Knowledge Graphs and Large Language Models—an Emerging Areamentioning

confidence: 99%

Knowledge graph‐driven data processing for business intelligence

Dey

2024

WIREs Data Min & Knowl

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With proliferation of Big Data, organizational decision making has also become more complex. Business Intelligence (BI) is no longer restricted to querying about marketing and sales data only. It is more about linking data from disparate applications and also churning through large volumes of unstructured data like emails, call logs, social media, News, and so on in an attempt to derive insights that can also provide actionable intelligence and better inputs for future strategy making. Semantic technologies like knowledge graphs have proved to be useful tools that help in linking disparate data sources intelligently and also enable reasoning through complex networks that are created as a result of this linking. Over the last decade the process of creation, storage, and maintenance of knowledge graphs have sufficiently matured, and they are now making inroads into business decision making also. Very recently, these graphs are also seen as a potential way to reduce hallucinations of large language models, by including these during pre‐training as well as generation of output. There are a number of challenges also. These include building and maintaining the graphs, reasoning with missing links, and so on. While these remain as open research problems, we present in this article a survey of how knowledge graphs are currently used for deriving business intelligence with use‐cases from various domains.This article is categorized under: Algorithmic Development > Text Mining Application Areas > Business and Industry

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Section: Knowledge Graphs and Large Language Models—an Emerging Areamentioning

confidence: 99%

Knowledge graph‐driven data processing for business intelligence

Dey

2024

WIREs Data Min & Knowl

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“…A variety of applications have integrated LLMs and knowledge graphs, including assisting in the development or curation of KGs by extracting entities and relationships from free text [14][15][16][17][18].…”

Section: Related Workmentioning

confidence: 99%

Phenomics Assistant: An Interface for LLM-based Biomedical Knowledge Graph Exploration

O’Neil,

Schaper,

Elsarboukh

et al. 2024

Preprint

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We introduce Phenomics Assistant, a prototype chat-based interface for querying the Monarch knowledge graph (KG), a comprehensive biomedical database. While unaided Large Large Language models (LLMs) are prone to mistakes in factual recall, their strong abilities in summarization and tool use suggest new opportunities to help non-expert users query and interact with complex data, while drawing on the KG to improve reliability of the answers. Leveraging the ability of LLMs to interpret queries in natural language, Phenomics Assistant enables a wide range of users to interactively discover relationships between diseases, genes, and phenotypes.To assess the reliability of our approach and compare the accuracy of different LLMs, we evaluated Phenomics Assistant answers on benchmark tasks for gene-disease association and gene alias queries. While comparisons across tested LLMs revealed differences in their ability to interpret KG-provided information, we found that even basic KG access markedly boosts the reliability of standalone LLMs. By enabling users to pose queries in natural language and summarizing results in familiar terms, Phenomics Assistant represents a new approach for navigating the Monarch KG.

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“…Kan et al (2023) demonstrate how knowledge-aware prompts can help improve the performance of vision-language models. Baek, Aji, and Saffari (2023) use prompting to augment LLMs for domain-specific question answering using a knowledge graph. We find prompting techniques, generalized as prompt templates 6 , appropriate for ChEdBot as they can be intuitively grasped by lecturers.…”

Section: Common Sense and Domain Knowledge In Llmsmentioning

confidence: 99%

ChEdBot: Designing a Domain-Specific Conversational Agent in a Simulational Learning Environment Using LLMs

Martin,

Pande,

Witschel

et al. 2024

AAAI-SS

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We propose conversational agents as a means to simulate expert interviews, integrated into a simulational learning environment: ChEdventure. Designing and developing conversational agents using the existing tools and frameworks requires technical knowledge and a considerable learning curve. Recently, LLMs are being leveraged for their adaptability to different domains and their ability to perform various tasks in a natural, human-like conversational style. In this work, we explore if LLMs can help educators easily create conversational agents for their individual teaching goals. We propose a generalized template-based approach using LLMs that can instantiate conversational agents as an integrable component of teaching and learning activities. We evaluate our approach using prototypes generated from this template and identify guidelines to improve the experience of educators.

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Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering

Cited by 18 publications

References 0 publications

Knowledge graph‐driven data processing for business intelligence

Knowledge graph‐driven data processing for business intelligence

Phenomics Assistant: An Interface for LLM-based Biomedical Knowledge Graph Exploration

ChEdBot: Designing a Domain-Specific Conversational Agent in a Simulational Learning Environment Using LLMs

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