Knowledge Tree
knowledge graph embeddings
Also known as: Knowledge Graph Embedding, knowledge graph embedding methods, knowledge graph embedding models, Knowledge graph embedding, KGE
Facts (50)
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Knowledge Graphs: Opportunities and Challenges - Springer Nature link.springer.com Apr 3, 2023 21 facts
claimKnowledge graph embeddings aim to represent knowledge graphs in a low-dimensional vector space while preserving semantics.
claimConvKB, introduced by Nguyen et al. in 2017, utilizes a convolutional neural network (CNN) to conduct knowledge graph embeddings by representing each triplet (h, r, t) as a three-row matrix A, which is input to a convolution layer to obtain feature maps.
referenceZhang et al. (2019b) utilized knowledge graph embeddings and graph convolution networks to extract long-tail relations.
referenceKnowledge graph embedding maps entities and relations into a low-dimensional vector space to efficiently capture the semantics and structure of the graph, allowing the resulting feature vectors to be learned by machine learning models (Dai et al. 2020b).
claimSaxena et al. (2020) proposed EmbedKGQA to perform multi-hop question answering over sparse knowledge graphs by utilizing knowledge graph embeddings to reduce sparsity, creating embeddings for entities, selecting the embedding of a given question, and combining them to predict the answer.
claimThe performance of traditional knowledge graph embedding methods that do not consider additional information like entity types or relation paths is often unsatisfactory.
claimMany established methods for generating knowledge graph embeddings suffer from limitations because they only consider surface facts (triplets) and ignore additional information such as entity types and relation paths, which could otherwise improve embedding accuracy.
claimThe challenges in developing knowledge graphs are categorized into the limitations of five topical technologies: knowledge graph embeddings, knowledge acquisition, knowledge graph completion, knowledge fusion, and knowledge reasoning.
claimNeural network-based methods for knowledge graph embeddings employ deep learning to represent triplets, with representative works including SME, ConvKB, and R-GCN (Dai et al. 2020a).
claimImproving knowledge graph embedding performance requires incorporating multivariate information, such as hierarchical relation descriptions and combined entity types and textual descriptions, into triplet features.
referenceMohamed SK, Nounu A, Nováček V published 'Biological applications of knowledge graph embedding models' in Briefings in Bioinformatics in 2021.
claimUtilizing rich additional information to improve the accuracy of knowledge graph embeddings remains a significant challenge.
claimYao L, Zhang Y, Wei B et al published the paper 'Incorporating knowledge graph embeddings into topic modeling' in 2017.
referenceTensor factorization-based methods for knowledge graph embeddings operate by transforming triplets into a 3D tensor, as described by Balažević et al. (2019).
claimSignificant technical challenges in knowledge graph development involve limitations in five representative technologies: knowledge graph embeddings, knowledge acquisition, knowledge graph completion, knowledge fusion, and knowledge reasoning.
referenceTranslation-based methods for knowledge graph embeddings utilize scoring functions based on translation invariance, a concept derived from interpreting the distance between word vectors as their semantic relationship, as noted by Mikolov et al. (2013).
procedureThe process of creating knowledge graph embeddings involves embedding entities and relations into a dense dimensional space, defining a scoring function to measure the plausibility of each fact (triplet), and maximizing the plausibility of the facts to obtain the embeddings.
referenceThe paper 'Knowledge Graphs: Opportunities and Challenges' elaborates on limitations concerning five representative knowledge graph technologies, including knowledge graph embeddings.
claimXiao H, Huang M, Hao Y et al published the paper 'Transg: a generative mixture model for knowledge graph embedding' as an arXiv preprint in 2015.
claimWang Z, Zhang J, Feng J et al published the paper 'Knowledge graph embedding by translating on hyperplanes' in the Proceedings of the AAAI Conference on Artificial Intelligence in 2014.
claimRossi et al. (2021) categorize triplet fact-based knowledge graph embedding approaches into three main types: tensor factorization-based, translation-based, and neural network-based methods.
Combining large language models with enterprise knowledge graphs frontiersin.org Aug 26, 2024 7 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.
perspectiveThe authors advocate for Pre-trained Language Model (PLM)-based Knowledge Graph Embedding (KGE) approaches that treat the Large Language Model (LLM) as a modular component, allowing for easy replacement to integrate context-specific models trained on domain-specific knowledge to enhance system relevance and accuracy.
claimMethodological frameworks for Pre-trained Language Model (PLM)-based Knowledge Graph Embedding (KGE) techniques generally fall into two categories: model finetuning and prompting.
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.
claimFinetuning PLM-based KGE models is generally costly and requires large amounts of annotated data, whereas prompting is more cost-effective but introduces privacy-related risks.
Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org 5 facts
claimKGClean is a knowledge graph-driven cleaning framework that utilizes knowledge graph embeddings.
referenceJ. Portisch, M. Hladik, and H. Paulheim authored 'RDF2Vec Light - A Lightweight Approach for Knowledge Graph Embeddings,' a technical report published in 2020.
referenceThe paper 'Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models under a Unified Framework' by M. Ali, M. Berrendorf, C.T. Hoyt, L. Vermue, M. Galkin, S. Sharifzadeh, A. Fischer, V. Tresp, and J. Lehmann, published in IEEE Transactions on Pattern Analysis and Machine Intelligence in 2021, provides a large-scale evaluation of knowledge graph embedding models.
claimRecent approaches to entity resolution for knowledge graphs utilize multi-source big data techniques, Deep Learning, or knowledge graph embeddings.
claimKnowledge graph embeddings encode entities and relations as low-dimensional vectors in an embedding space to facilitate link prediction.
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 4 facts
referencePretrain-KGE, introduced by Zhang Z. et al. in 2020, is a training framework that incorporates world knowledge from pre-trained models into entity and relation embeddings to enhance the performance of any Knowledge Graph Embedding (KGE) model.
claimKnowledge Graph Embedding (KGE) is the process of learning low-dimensional representations of entities and relations within knowledge graphs.
claimThere are two primary approaches for Knowledge Graph Embedding: structure-based and description-based.
procedureThe process of Knowledge Graph Embedding consists of three stages: (1) entity and relation representation, (2) scoring function definition, and (3) representation learning.
A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Dec 9, 2025 2 facts
claimKnowledge graph embeddings and graph neural networks exemplify the unified approach in neuro-symbolic AI by geometrizing logical relations and enabling end-to-end trainability via gradient-based optimization.
referenceThe paper 'Knowledge graph embedding: a survey of approaches and applications' was authored by Wang, Q., Mao, Z., Wang, B., and Guo, L., and published in IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 in 2017.
Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org Feb 16, 2025 2 facts
referenceAlexander I Cowen-Rivers, Pasquale Minervini, Tim Rocktaschel, Matko Bosnjak, Sebastian Riedel, and Jun Wang authored the paper 'Neural variational inference for estimating uncertainty in knowledge graph embeddings', published as an arXiv preprint in 2019.
referenceQingyao Cui, Yanquan Zhou, and Mingming Zheng authored the paper 'Sememes-based framework for knowledge graph embedding with comprehensive-information', published in the proceedings of the 14th International Conference, KSEM 2021, in Tokyo, Japan, August 14–16, 2021, by Springer.
[PDF] Injecting Knowledge Graph Embeddings into RAG Architectures ceur-ws.org 1 fact
referenceThe research paper titled 'Injecting Knowledge Graph Embeddings into RAG Architectures' addresses the problem of fact-checking by injecting Knowledge Graph Embedding (KGE) vector representations into Large Language Models (LLMs) using a Retrieval Augmented Generation (RAG) framework.
Construction of intelligent decision support systems through ... - Nature nature.com Oct 10, 2025 1 fact
claimKnowledge graph embeddings integrate symbolic knowledge through statistical learning.
Knowledge Graphs: Opportunities and Challenges dl.acm.org 1 fact
claimThe authors of the paper 'Knowledge Graphs: Opportunities and Challenges' identify knowledge graph embeddings as a severe technical challenge in the field of knowledge graphs.
Medical Hallucination in Foundation Models and Their ... medrxiv.org Mar 3, 2025 1 fact
claimGong et al. (2021) utilize knowledge graph embeddings to mitigate risks associated with incorrect prescriptions, focusing on safe medicine recommendations.
Knowledge Graphs: Opportunities and Challenges - arXiv arxiv.org Mar 24, 2023 1 fact
claimThe technical challenges in the field of knowledge graphs include knowledge graph embeddings, knowledge acquisition, knowledge graph completion, knowledge fusion, and knowledge reasoning.
The construction and refined extraction techniques of knowledge ... nature.com Feb 10, 2026 1 fact
referenceXiong, C., Power, R. & Callan, J. published 'Explicit semantic ranking for academic search via knowledge graph embedding' in the Proceedings of the 26th international conference on world wide web, pp. 1271–1279 (2017).
LLM-Powered Knowledge Graphs for Enterprise Intelligence and ... arxiv.org Mar 11, 2025 1 fact
referenceWang, Q., Mao, Z., Wang, B., Guo, L. (2022) published 'Knowledge graph embedding: A survey of approaches and applications' in IEEE Transactions on Big Data.
Medical Hallucination in Foundation Models and Their Impact on ... medrxiv.org Nov 2, 2025 1 fact
claimGong et al. (reference 70) utilize Knowledge Graph embeddings to mitigate risks associated with incorrect prescriptions in medicine recommendations.
Opportunities and Challenges with Knowledge Graphs briancartergroup.com Oct 5, 2024 1 fact
claimKnowledge graph development faces technical challenges, specifically regarding knowledge graph embeddings and knowledge acquisition, according to the article 'Opportunities and Challenges with Knowledge Graphs'.