Knowledge Tree
knowledge reasoning
Facts (13)
Sources
Knowledge Graphs: Opportunities and Challenges - Springer Nature link.springer.com Apr 3, 2023 10 facts
claimKnowledge reasoning can identify erroneous knowledge by reasoning out false facts and can infer new relations between unconnected entities to form new triplets.
referenceKnowledge reasoning tasks are divided into single-hop prediction and multi-hop reasoning (Ren et al. 2022).
claimThe goal of knowledge reasoning in AI systems is to infer new knowledge, such as implicit relations between two entities, based on existing data.
referenceKnowledge reasoning aims to enrich knowledge graphs by inferring new facts based on existing data.
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.
referenceKnowledge reasoning methods for knowledge graphs are categorized into logic rule-based methods (De Meester et al. 2021), distributed representation-based methods (Chen et al. 2020b), and neural network-based methods (Xiong et al. 2017).
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.
referenceThe main methods for knowledge reasoning include logic rule-based, distributed representation-based, and neural network-based methods.
claimThe verification of inferred new knowledge is a critical issue because inferred knowledge is sometimes uncertain, and conflicts between new and existing knowledge must be detected.
referenceKnowledge reasoning enriches existing knowledge graphs and provides benefits to downstream tasks (Wan et al. 2021).
Large Language Models Meet Knowledge Graphs for Question ... arxiv.org Sep 22, 2025 1 fact
claimComplex Question Answering (QA) involves question decomposition and knowledge fusion across multiple data modalities and sources, requiring complex knowledge reasoning to generate accurate answers.
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.
Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org Feb 16, 2025 1 fact
referenceXiaojun Chen, Shengbin Jia, and Yang Xiang published 'A review: Knowledge reasoning over knowledge graph' in Expert systems with applications in 2020.