LIMES - A Time-Efficient Approach for Large-Scale Link Discovery on the Web of Data

By Axel-Cyrille Ngonga Ngomo, Sören Auer. International Joint Conference on Artificial Intelligence, 2011

485 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.

Aligning Cross-Lingual Entities with Multi-Aspect Information (2019, methodology): “As for monolingual entity alignment, main approaches align two entities by computing string similarity of entity labels (Scharffe et al., 2009; Volz et al., 2009; Ngomo and Auer, 2011) or graph similarity (Raimond et al., 2008; Pershina et al., 2015; Azmy et al., 2019).”
LinkedGeoData: A core for a web of spatial open data (2012, methodology): “The interlinking is performed using the tools SILK [24] and LIMES [17], which use the static SPARQL endpoint as the backend.”
DBpedia SPARQL Benchmark - Performance Assessment with Real Queries on Real Data (2011, methodology): “To reduce the runtime of the benchmark compilation, we use the LIMES framework [13]4.”
Linked Data: Evolving the Web into a Global Data Space (2011, methodology): “l LIMES - Link Discovery Framework for Metric Spaces [44].”