Statistical predicate invention

By Stanley Kok, Pedro M. Domingos. 2007

336 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Irreflexive and Hierarchical Relations as Translations (2013, methodology): “…(2006) considered a non-parametric Bayesian extension of the stochastic block-model allowing to automatically infer the number of latent clusters; Kok & Domingos (2007) introduced clustering in Markov-Logic networks; Sutskever et al. (2009) used a non-parametric Bayesian clustering of entities…”
A semantic matching energy function for learning with multi-relational data (2013, methodology): “Hence, MRC, for Multiple Relation Clustering (Kok and Domingos 2007), performs clustering of entities through several relation types simultaneously.”
End-to-end Differentiable Proving (2017, methodology): “Kinship, Nations & UMLS We use the Nations, Alyawarra kinship (Kinship) and Unified Medical Language System (UMLS) KBs from [10].”
A Review of Relational Machine Learning for Knowledge Graphs (2015, neutral): “For instance, [63] showed that RESCAL provides comparable or better relationship prediction results on a number of small benchmark data sets compared to Markov logic networks (with structure learning) [70], the infinite (hidden) relational model [71], [72], and Bayesian clustered tensor factorizatio”
RelNN: A Deep Neural Model for Relational Learning (2017, background): “Predicate invention approaches (Kemp et al. 2006; Kok and Domingos 2007) cluster the objects, their properties, and relationships such that the values of the target(s) depends on their clustering.”
A Three-Way Model for Collective Learning on Multi-Relational Data (2011, methodology): “(Kok & Domingos, 2007) states that IRM and MRC have been run at least ten hours per fold on the IRM datasets, while both tensor decompositions show training times below 2 minutes per fold.”
Modelling Relational Data using Bayesian Clustered Tensor Factorization (2009, methodology): “To facilitate comparison with [9], we conducted our experim ents the following way.”
A latent factor model for highly multi-relational data (2012, methodology): “Table 1: Comparisons of the performance obtained by our approach, RESCAL [16], MRC [10] and SME [2] over three standard datasets.”
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text (2019, methodology): “…an NLU benchmark that explicitly tests inductive reasoning and systematic generalization, we build upon the classic ILP task of inferring family (i.e., kinship) relations (Hinton et al., 1986; Muggleton, 1991; Lavrac and Dze-roski, 1994; Kok and Domingos, 2007; Rockt¨aschel and Riedel, 2017).”
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs (2019, background): “To evaluate our proposed method, we use ﬁve benchmark datasets: WN18RR (Dettmers et al., 2018), FB15k-237 (Toutanova et al., 2015), NELL-995 (Xiong et al., 2017), Uniﬁed Medical Language Systems (UMLS) (Kok and Domin-gos, 2007) and Alyawarra Kinship (Lin et al., 2018).”
StatSnowball: a statistical approach to extracting entity relationships (2009, background): “able discovery in statistical learning, is studied in [13], which can generate and select new predicates that are expressed in terms of existing ones via iterative clustering.”
A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal (2022, background): “Most of them have great expressive ability but suffer from explainability.”
Factorizing YAGO: scalable machine learning for linked data (2012, background): “Expressing data in terms of newly invented latent components is often referred to as predicate invention in statistical relational learning and considered a powerful asset [19].”
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning (2017, methodology): “Symbolic representations have also been integrated with machine learning especially in statistical relational learning [24, 13, 19, 20], but due to poor generalization performance, these approaches have largely been superceded by distributed vector representations.”
Multi-Hop Knowledge Graph Reasoning with Reward Shaping (2018, neutral): “(Kok and Domingos, 2007).”
Learning Relational Representations with Auto-encoding Logic Programs (2019, background): “An exception to this is the work by Kok and Domingos [2007]; however, it does not provide novel language constructs to an SRL model, but only compresses the existing data by identifying entities that are identical .”
Differentiable Learning of Logical Rules for Knowledge Base Reasoning (2017, methodology): “Often the underlying logic is a probabilistic logic, such as Markov Logic Networks [22] or ProPPR [26].”
Differentiable Reasoning on Large Knowledge Bases and Natural Language (2019, background): “…to b , a borders b , a was butted against b , a neighbours with b , a was a neighbor of b , a was a neighboring country of b , a was a neighboring state to b Nations and UMLS Furthermore, we consider the Nations, and the Uniﬁed Medical Language System (UMLS) datasets (Kok and Domingos 2007).”