Fast rule mining in ontological knowledge bases with AMIE+\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy}…

By Luis Galárraga, Christina Teflioudi, Katja Hose, Fabian M. Suchanek. 2015

560 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Graph Learning, Knowledge Graphs & Representation

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Citations in the corpus

RuleBERT: Teaching Soft Rules to Pre-Trained Language Models (2021, methodology): “Rules are annotated with a measure of this likelihood, either manually or with a computed confidence (Galárraga et al., 2015).”
A Review of Relational Machine Learning for Knowledge Graphs (2015, background): “[108] L.”
YAGO: A Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames (2016, methodology): “We have already developed methods for mining Horn clauses [8,10], but more general forms of rules need to be tackled [9].”
DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs (2019, methodology): “Recent rule mining methods such as AMIE+ [15] and Ontological Pathﬁnding (OP) [5] use predeﬁned metrics such as conﬁdence and support and take advantage of various parallelization and partitioning techniques to speed up the counting process.”
Neural, symbolic and neural-symbolic reasoning on knowledge graphs (2021, unknown): “Illustration of rule scoring in KALE (Guo et al., 2016).”
Learning from positive and unlabeled data: a survey (2018, background): “, true statements), so there are no negative examples and the truth value of any tuple not in the KB should be considered unknown [28,117].”
Knowledge Graph Embedding for Link Prediction (2020, methodology): “Some are based on observable features, relying on techniques such as Rule Mining [1, 18, 19, 42] or the Path Ranking Algorithm [35, 36].”
Explainable Fact Checking with Probabilistic Answer Set Programming (2019, methodology): “tunately, two issues make the generation of such explanations hard. First, in general KGs do not come with the rich rules we need in our task. To address this issue, we exploit rule mining approaches [7, 16], which automatically learn logical rules for a given KG (e.g., Table 1). Second, KGs have da”
Knowledge Graph Embedding: A Survey of Approaches and Applications (2017, methodology): “There are also systems such as WARMR [106], ALEPH [107], and AMIE [108], [109] which can extract logical rules automatically from KGs.”
Robust Discovery of Positive and Negative Rules in Knowledge Bases (2018, methodology): “We study the effect of the LCWA assumption for the generation of negative examples.”
MMKG: Multi-Modal Knowledge Graphs (2019, methodology): “One example of retrieved rule by AMIE+ is: (x, father_ofDB15k, w), (w, SameAs, z), (z, children_ofFB15k, y) ⇒ (x, SameAs, y) In this case both father_ofDB15k and children_ofFB15k are (almost) functional relationships.”
Multi-Channel Graph Neural Network for Entity Alignment (2019, methodology): “Since the acquirement of rule knowledge is not our focus in this paper, we utilize AMIE+ (Gal´arraga et al., 2015), a modern rule mining system, to efﬁ-ciently ﬁnd Horn rules from large-scale KG, such as marriedTo ( x, y ) ∧ liveIn ( x, z ) ⇒ liveIn ( y, z ) .”