Knowledge Tree
Relations (1)
related 2.32 — strongly supporting 4 facts
Knowledge graphs and machine learning are deeply interconnected, as machine learning techniques are used to refine knowledge graphs [1] and generate graph embeddings for analysis [2]. Furthermore, knowledge graphs serve as essential data sources for machine learning systems [3], and their intersection is a formal area of research in AI development [4].
Facts (4)
Sources
Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org 2 facts
claimMachine learning systems benefit from knowledge graphs by using them as sources of labeled training data or other input data, which supports the development of knowledge- and data-driven AI approaches.
claimGraph data models for knowledge graphs should provide comprehensive query languages and advanced analysis capabilities, such as clustering similar entities or determining graph embeddings for machine learning tasks.
The construction and refined extraction techniques of knowledge ... nature.com 1 fact
referenceB. Subagdja et al. published 'Machine learning for refining knowledge graphs: A Survey' in ACM Computing Surveys, Volume 56, Issue 6, pages 1–38, in 2024.
Call for Papers: KR meets Machine Learning and Explanation kr.org 1 fact
claimThe KR 2026 special track 'KR meets Machine Learning and Explanation' invites research on the intersection of Knowledge Representation and Machine Learning, specifically covering topics such as learning symbolic knowledge (ontologies, knowledge graphs, action theories), KR-driven plan computation, logic-based learning, neural-symbolic learning, statistical relational learning, symbolic reinforcement learning, and the mutual use of KR techniques and LLMs.