Year: 2024
Type: conference-paper
Abstract: This paper presents a detailed methodology for extracting and analyzing data from a knowledge graph designed to store complex geological information. Our pipeline was designed after a deep understanding of the KG, focuses on browsing, querying and transforming data using curated text templates. The extraction methodology is based on graph triples, key classes, properties and relationships, which ensures the relevance and truthfulness of the data obtained. With the recent advancements in neural large language models, which perform exceptionally well on open-domain tasks, our work addresses the challenge of presenting LLMs with accurate closed-domain data—originating from graph-based sources—in a readable and accessible textual format.
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4405168342