A self-training approach for resolving object coreference on the semantic web

123 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.

SIGMa: simple greedy matching for aligning large knowledge bases (2012, methodology): “In this experiment, we test the performance of SiGMa on the three benchmark datasets and compare them with the best published results so far that we are aware of: PARIS [28] for the Person-Restaurants datasets (which compared favorably over ObjectCoref [15]); and RiMoM [20] for Rexa-DBPL.”
LINDA: distributed web-of-data-scale entity matching (2012, background): “This paper makes the following technical contributions: (1) A new optimization model for joint evidence and consistency of sameAs linkage between LOD entities.”
A benchmarking study of embedding-based entity alignment for knowledge graphs (2020, unknown): “Recent studies also use statistical machine learning [16, 32, 34] and crowdsourcing [33, 105] to improve the accuracy.”
PARIS: Probabilistic Alignment of Relations, Instances, and Schema (2011, methodology): “There have been many works on entity resolution, i.e., on what is traditionally known as the “A-Box” [1, 4, 12, 17, 18,25,27,28,31].”