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Relations (1)
related 3.46 — strongly supporting 10 facts
Machine learning and knowledge representation are increasingly integrated to solve complex computational challenges, as evidenced by their joint focus in academic literature reviews [1] and dedicated research tracks like 'KR meets Machine Learning and Explanation' {fact:2, fact:3, fact:8}. These fields are viewed as synergistic, with KR providing technologies to enhance ML pipelines [2] and both fields being combined to address mutual challenges in modeling and reasoning {fact:5, fact:7}.
Facts (10)
Sources
Call for Papers: KR meets Machine Learning and Explanation kr.org 6 facts
perspectiveThe field of Knowledge Representation (KR) provides a repertoire of technologies for leveraging knowledge in both Machine Learning (ML) and explanation pipelines.
procedureThe KR 2026 special track 'KR meets Machine Learning and Explanation' mandates that all submissions must fall into the intersection of Knowledge Representation and either Machine Learning or explanation; papers that do not meet this criterion will be desk-rejected before the review process begins.
claimThe KR 2026 special track on 'KR meets Machine Learning and Explanation' aims to focus on the synergistic interactions between Knowledge Representation (KR) and the fields of Machine Learning (ML) and explanation.
claimThe KR 2026 special track invites contributions that use Knowledge Representation (KR) methods to solve Machine Learning (ML) challenges, use ML methods to solve KR challenges, or integrate learning and reasoning for better modeling, solving, or explaining tasks.
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.
claimThe KR 2026 special track welcomes papers focusing on evaluation protocols and benchmarking of hybrid solutions that combine Knowledge Representation (KR) with Machine Learning (ML) or explanation.
Call for Papers: Special Session on KR and Machine Learning kr.org 3 facts
procedureSubmissions to the Special Session on KR and Machine Learning are peer-reviewed by Program Committee members who are active in both Knowledge Representation and Machine Learning fields.
claimThe Special Session on KR and Machine Learning at KR2022 invites submissions that integrate knowledge representation (KR) and machine learning (ML), specifically focusing on using KR methods to solve ML challenges (such as knowledge-guided or explainable learning), using ML methods to solve KR challenges (such as efficient inference or knowledge base completion), integrating learning and reasoning, and applying combined approaches to real-world problems.
claimThe Special Session on KR and Machine Learning requires submissions to be at the intersection of Knowledge Representation and Machine Learning, meaning submissions focused exclusively on either KR or ML will not be accepted.
A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com 1 fact
accountThe authors conducted a systematic literature review of NLP, machine learning, and knowledge representation research from the last decade to understand approaches for integrating knowledge graphs (KGs) and large language models (LLMs).