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AYNEC-DataGen: a tool for generating evaluation datasets for Knowledge Graphs completion

In the context of knowledge graphs, the task of completion of relations consists in adding missing triples to a knowledge graph, usually by classifying potential candidates as true of false. Creating an evaluation dataset for these techniques is not trivial, since there is a large amount of variables to consider which, if not taken into account, may cause misleading results. So far, there is not a well defined workflow that identifies the variation points when creating a dataset, and what are the possible strategies that can be followed in each step. Furthermore, there are no tools that help create such datasets in an easy way. To address this need, we have created AYNEC-DataGen, a customisable tool for the generation of datasets with multiple variation points related to the preprocessing of the original knowledge graph, the splitting of triples into training and testing sets, and the generation of negative examples. The output of our tool includes the evaluation dataset, an optional export in an open format for its visualisation, and additional files with metadata. Our tool is freely available online.

Autores: Daniel Ayala / Agustin Borrego / Inma Hernandez / Carlos R. Rivero / David Ruiz / 
Palabras Clave: Evaluation - Graph refinement - Knowledge Graph - Tool

DataGenCARS: A generator of synthetic data for the evaluation of context-aware recommendation systems

Este artículo se presenta a JISBD como trabajo relevante y ha sido publicado en la revista Pervasive and Mobile Computing en el año 2017.María del Carmen Rodríguez-Hernández, Sergio Ilarri, Ramón Hermoso, Raquel Trillo-Lado, «DataGenCARS: A Generator of Synthetic Data for the Evaluation of Context-Aware Recommendation Systems», Pervasive and Mobile Computing, ISSN 1574-1192, volume 38, part 2, pp. 516-541, Elsevier, July 2017. Special Issue on Context-aware Mobile Recommender Systems.DOI: 10.1016/j.pmcj.2016.09.020.JCR 2016 (última edición del JCR publicada): factor de impacto: 2,349; 53/146 en Computer Science, Information Systems (Q2, T2); 34/89 en Telecommunications (Q2, T2). Revista en el top 36,3% (considerando la mejor categoría del JCR).

Autores: María Del Carmen Rodríguez Hernández / Sergio Ilarri / Ramon Hermoso / Raquel Trillo-Lado / 
Palabras Clave: Context-aware recommendation systems - Dataset generation - Evaluation - Mobile recommendations

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