KG-CF: Knowledge Graph Completion with Context Filtering under the Guidance of Large Language Models
Year: 2024
Type: conference-paper
Abstract: Large Language Models (LLMs) have shown impressive performance in various tasks, including knowledge graph completion (KGC). However, current studies mostly apply LLMs to classification tasks, like identifying missing triplets, rather than ranking-based tasks, where the model ranks candidate entities based on plausibility. This focus limits the practical use of LLMs in KGC, as real-world applications prioritize highly plausible triplets. Additionally, while graph paths can help infer the existence of missing triplets and improve completion accuracy, they often contain redundant information. To address these issues, we propose KG-CF, a framework tailored for ranking-based KGC tasks. KG-CF leverages LLMs’ reasoning abilities to filter out irrelevant contexts, achieving superior results on real-world datasets. The code and datasets are available at https://anonymous.4open.science/r/KG-CF.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: SCC-IRG Track 1: Community-Responsive Electrified and Adaptive Transit Ecosystem (CREATE): Planning, Operations, and Management, Collaborative Research: III: Small: Graph-Oriented Usable Interpretation, CAREER: Toward A Knowledge-Guided Framework for Personalized Decision Making, Collaborative Research: SAI-R: Dynamical Coupling of Physical and Social Infrastructures: Evaluating the Impacts of Social Capital on Access to Safe Well Water, SaTC: CORE: Small: Empowering Network Attack Detection with Complex Graph Modeling, III: Small: Collaborative Research: Demystifying Deep Learning on Graphs: From Basic Operations to Applications
OpenAlex ID: W4406458271