pre-trained language models ↔ Knowledge Graph

Relations (1)

related 2.00 — strongly supporting 3 facts

Knowledge Graphs and pre-trained language models are related through the development of hybrid embedding solutions, as discussed in [1] and [2], and the shared challenges of implementing these technologies in enterprise environments as noted in [3].

Facts (3)

Sources

Combining large language models with enterprise knowledge graphs frontiersin.org Frontiers 3 facts

perspectiveA hybrid approach that combines Pre-trained Language Models (PLMs), Knowledge Graph (KG) structure understanding, and domain expertise is recommended to ensure privacy compliance in industrial settings.

claimThe primary challenges of implementing corporate Knowledge Graph Embedding (KGE) solutions are categorized into four areas: (i) the quality and quantity of public or automatically annotated data, (ii) developing sustainable solutions regarding computational resources and longevity, (iii) adaptability of PLM-based KGE systems to evolving language and knowledge, and (iv) creating models capable of efficiently learning the Knowledge Graph (KG) structure.

claimThe main challenges for enterprise Large Language Model (LLM)-based solutions for Knowledge Graph Embedding (KGE) include the high cost and resource intensity of creating tailored Pre-trained Language Model (PLM)-based KGE solutions, the mismatch between public benchmark datasets and enterprise use cases due to structural differences, the need for robust methods to combine automated novelty detection with human-curated interventions, and the requirement for a shift from classification to representation learning to accommodate novelty and encode Knowledge Graph (KG) features.