AMIE: association rule mining under incomplete evidence in ontological knowledge bases

By Luis Galárraga, Christina Teflioudi, Katja Hose, Fabian M. Suchanek. The Web Conference, 2013

770 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/87245d2460b3545a161db78bf0a450422b66f90f

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Citations in the corpus

Knowledge graph refinement: A survey of approaches and evaluation methods (2016, background): “In [22], the use of association rule mining to find property chains (e.”
Embedding Entities and Relations for Learning and Inference in Knowledge Bases (2014, background): “Gal ´ arraga et al. (2013) suggested to identify incorrect predictions based on the functional property of relations.”
YAGO3: A Knowledge Base from Multilingual Wikipedias (2015, neutral): “This yields, e.g.,”
Knowledge Graph Embedding for Link Prediction (2020, methodology): “Some methods are based on observable features and employ techniques such as Rule Mining [17][16][37][24] or the Path Ranking Algorithm [31][32] to identify missing triples in the graph.”
Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction (2021, methodology): “The negative samples are generated according to Partial Completeness Assumption (PCA) [14], which corrupts one of the entities in a positive triplet to create a negative sample.”
Knowledge vault: a web-scale approach to probabilistic knowledge fusion (2014, methodology): “, [15]).”
Knowledge Graphs (2020, neutral): “In lieu of PCA, the CARL system [101] uses knowledge of the cardinalities of relations to find negative edges, while d’Amato et al. [25] use ontologically entailed negative edges for measuring the confidence of rules generated by an evolutionary algorithm.”
A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal (2022, background): “Based on the above reviews, we can further get the following observations, which may indicate the scope for different temporal KGR models and reveal the future trend in temporal KGR. (1) RNN-agnostic models, i.e., time-vector guided models and time-operation guided models, generally treat the…”
Debiasing Knowledge Graphs: Why Female Presidents are not like Female Popes (2018, background): “Consider for example, the use of association rule mining to infer new rules from knowledge graphs[2].”
ProjE: Embedding Projection for Knowledge Graph Completion (2016, background): “4 show that ProjE outperforms existing fact checking and link prediction models [2, 6, 17, 9, 15, 22] in all but one question type.”
A Review of Relational Machine Learning for Knowledge Graphs (2015, background): “) Another option to generate negative examples for training is to make a local-closed world assumption (LCWA) [28], [107], in which we assume that a KG is only locally complete.”
Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps (2020, methodology): “Based on the results of the AMIE model (Galárraga et al., 2013), we manually checked and verified all logical rules to make it suitable for the Wikidata relations.”
Neural, symbolic and neural-symbolic reasoning on knowledge graphs (2021, methodology): “Illustration of rule scoring in KALE (Guo et al., 2016).”