Knowledge Graph ↔ knowledge graph embeddings

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related 2.81 — strongly supporting 6 facts

Knowledge graph embeddings are a technical method used to represent and process the structural data contained within a Knowledge Graph, as evidenced by their application in entity resolution [1], cleaning frameworks [2], and link prediction tasks [3].

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Combining large language models with enterprise knowledge graphs frontiersin.org Frontiers 4 facts

claimModeling Knowledge Graph Embedding (KGE) as a classification problem prevents the correct handling of Knowledge Graphs (KGs) where multiple relations connect two entities, negatively affecting both disambiguation and link prediction.

claimKnowledge Graph Embedding (KGE) relying solely on Distant Supervision (DS) is inadequate for predicting new types because weak annotations are limited to existing Knowledge Graph entities and relations.

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.

Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org arXiv 2 facts

claimKGClean is a knowledge graph-driven cleaning framework that utilizes knowledge graph embeddings.

claimRecent approaches to entity resolution for knowledge graphs utilize multi-source big data techniques, Deep Learning, or knowledge graph embeddings.