Learning to Refine an Automatically Extracted Knowledge Base Using Markov Logic

By Shangpu Jiang, Daniel Lowd, Dejing Dou. 2012

73 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Review of Relational Machine Learning for Knowledge Graphs (2015, methodology): “MRFs are a useful tool for modeling KGs, as shown in [108].”
Knowledge Graph Completion: A Review (2020, background): “Probabilistic graph model is a model that uses graphs to represent probabilistic relations, which provides the possibility to effectively reason and learn various types of probabilistic models [35].”
DeepDive: Declarative Knowledge Base Construction (2016, background): “While different communities place differing emphasis on the extraction, cleaning, and integration phases, all seem to be converging toward a common set of techniques that include a mix of data processing, machine learning, and engineers-in-the-loop.”
Incremental Knowledge Base Construction Using DeepDive (2015, background): “At the same time, communities such as natu-ral language processing and machine learning are attacking similar problems under the name knowledge base construction (KBC) [6, 17, 27].”
Knowledge Graph Identification (2013, methodology): “The third method we compare against is the best-performing MLN model from [9], that expresses ontological constraints, and candidate and promoted facts through logical rules similar to those in our model.”
Knowledge vault: a web-scale approach to probabilistic knowledge fusion (2014, unknown): “, [23]) or continuous relaxations thereof (e.”