Knowledge Tree
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related 0.80 — strongly supporting 8 facts
Knowledge acquisition is a fundamental process for constructing and populating knowledge graphs, as it involves extracting data from structured and unstructured sources [1], [2]. Furthermore, the acquisition of knowledge is widely recognized as a primary technical challenge in the development and maintenance of high-quality knowledge graphs [3], [4], [5].
Facts (8)
Sources
Knowledge Graphs: Opportunities and Challenges - Springer Nature link.springer.com 5 facts
referenceKnowledge acquisition involves modeling and constructing knowledge graphs by importing data from structured sources using mapping languages like R2RML (Rodriguez-Muro and Rezk 2015) or by extracting knowledge from unstructured documents like news, research papers, and patents using relation, entity, or attribute extraction methods (Liu et al. 2020; Yu et al. 2020; Yao et al. 2019).
claimKnowledge acquisition, which involves extracting knowledge from structured and unstructured data, is a critical step in generating knowledge graphs.
claimExisting knowledge acquisition methods suffer from low accuracy, which results in incomplete or noisy knowledge graphs that hinder downstream AI tasks.
claimMost existing knowledge acquisition methods focus on constructing knowledge graphs using only one specific language.
claimResearch on knowledge graphs faces technical challenges, specifically regarding the acquisition of knowledge from multiple sources and the integration of that knowledge into a typical knowledge graph.
KR 2026 : 23rd International Conference on Principles of ... - WikiCFP wikicfp.com 1 fact
claimThe 23rd International Conference on Principles of Knowledge Representation and Reasoning (KR 2026) covers research topics including argumentation, belief change, common-sense reasoning, computational aspects of knowledge representation, description logics, ethical considerations in knowledge representation, explanation, abduction and diagnosis, geometric, spatial, and temporal reasoning, inconsistency- and exception-tolerant reasoning, knowledge acquisition, knowledge compilation, automated reasoning, satisfiability and model counting, knowledge representation languages, logic programming, answer set programming, model learning for diagnosis and planning, modeling and reasoning about preferences, modeling constraints and constraint solving, multi- and order-sorted representations and reasoning, non-monotonic logics, ontologies and knowledge-enriched data management, philosophical foundations of knowledge representation, qualitative reasoning, reasoning about actions and change, action languages, reasoning about knowledge, beliefs, and other mental attitudes, reasoning in knowledge graphs, reasoning in multi-agent systems, semantic web, similarity-based and contextual reasoning, and uncertainty and vagueness.
Call for Papers: Main Track - KR 2026 kr.org 1 fact
claimThe KR 2026 conference accepts submissions on topics including argumentation, belief change, common-sense reasoning, computational aspects of knowledge representation, description logics, ethical considerations in KR, explanation/abduction/diagnosis, geometric/spatial/temporal reasoning, inconsistency- and exception-tolerant reasoning, knowledge acquisition, knowledge compilation/automated reasoning/satisfiability/model counting, knowledge representation languages, logic programming/answer set programming, model learning for diagnosis and planning, modeling and reasoning about preferences, modeling constraints and constraint solving, multi- and order-sorted representations and reasoning, non-monotonic logics, ontologies and knowledge-enriched data management, philosophical foundations of KR, qualitative reasoning, reasoning about actions and change/action languages, reasoning about knowledge/beliefs/mental attitudes, reasoning in knowledge graphs, reasoning in multi-agent systems, semantic web, similarity-based and contextual reasoning, and uncertainty and vagueness.
Knowledge Graphs: Opportunities and Challenges dl.acm.org 1 fact
claimThe authors of the paper 'Knowledge Graphs: Opportunities and Challenges' identify knowledge acquisition as a severe technical challenge in the field of knowledge graphs.