Entity Resolution with Markov Logic

By Parag Singla, Pedro M. Domingos. 2006

434 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

How researchers see this paper

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

In Artificial Intelligence

In Information Systems

In Management Science and Operations Research

Citations in the corpus

A semantic matching energy function for learning with multi-relational data (2013, background): “A relation is denoted by a triplet ( lhs , rel , rhs ), where lhs is the left entity, rhs the right one and rel the type of relation between them.”
A Review of Relational Machine Learning for Knowledge Graphs (2015, background): “In a relational setting, the decisions about which objects are assumed to be identical can propagate through the graph, so that matching decisions are made collectively for all objects in a domain rather than independently for each object pair (see, for example, [51]–[53]).”
Frameworks for entity matching: A comparison (2010, background): “Examples for numerical combination approaches can be found in [26,41,52,32].”
Joint Entity Resolution (2012, methodology): “Among the existing works on joint ER [1], [2], [3], [4], [5], [6], few have focused on scalability, which is crucial in resolving large data (e.”
AIDA (2011, methodology): “Recently,collective learning methods have been used for jointly mapping all mentions to entities in one step, based on probabilistic graphical models [14, 10, 16].”
Knowledge Graph Embedding: A Survey of Approaches and Applications (2017, background): “, in the Cora dataset [124] which contains citations with the fields of author, title, and venue, the name of an author or a venue can be written in different ways.”
Gradient-based boosting for statistical relational learning: The relational dependency network case (2011, methodology): “[44] and available on the Alchemy website to compare against the boosted and non-boosted versions of RDN.”
Learning Markov Logic Networks via Functional Gradient Boosting (2011, neutral): “[26].”
Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco (2018, background): “Draco can capture rich attribute relations using a small number of expert-defined rules due to the expressive and compact nature of MLNs [45, 55].”
Large-Scale Collective Entity Matching (2011, methodology): “For evaluation, we use the collective entity matching algorithm of Singla et al. [18], which is a state of the art algorithm, and uses Markov Logic Networks.”
Large-Scale Deduplication with Constraints Using Dedupalog (2009, methodology): “There are prior frameworks such as Markov Logic Networks [12] or Probabilistic Relational Models [13] that can express deduplication tasks [7], [14].”
Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS (2011, background): “We present an extensive experimental study on a diverse set of MLN testbeds to demonstrate that our system Tuffy is able to get better result quality more quickly and work over larger datasets than the state-of-the-art approaches.”
LINDA: distributed web-of-data-scale entity matching (2012, background): “The purest approaches employ collective relational learning, based on probabilistic graphical models [12, 16].”
Probabilistic models with unknown objects (2006, background): “There has been work on such multi-type coreference resolution in other probabilistic frameworks [Culotta and McCallum, 2005; Singla and Domingos, 2006].”
Quantitative Data Cleaning for Large Databases (2008, background): “, [Singla and Domingos, 2006], [Bhattacharya and Getoor, 2007], [Dong et al.”
Joint Inference in Information Extraction (2007, methodology): “In both datasets we also compared with a “standard” Fellegi-Sunter model (see Singla & Domingos (2006) ), learned using logistic regression, and with oracle segmentation as the input.”