activity
20132021
most citedA Labeled Graph Kernel for Relationship Extraction

4 citations · 5 across the 3 of their papers we have counts for

collaborators

5 papers

cs.DB20211 cited

Empowering Investigative Journalism with Graph-based Heterogeneous Data Management

Angelos-Christos Anadiotis, Oana Balalau, Theo Bouganim +6

Investigative Journalism (IJ, in short) is staple of modern, democratic societies. IJ often necessitates working with large, dynamic sets of heterogeneous, schema-less data sources…

cs.DB2020

Graph integration of structured, semistructured and unstructured data for data journalism

Angelos-Christos Anadiotis, Oana Balalau, Catarina Conceicao +5

Digital data is a gold mine for modern journalism. However, datasets which interest journalists are extremely heterogeneous, ranging from highly structured (relational databases),…

cs.DB2020

Graph integration of structured, semistructured and unstructured data for data journalism

Oana Balalau, Catarina Conceiç{ã}o, Helena Galhardas +4

Nowadays, journalism is facilitated by the existence of large amounts of digital data sources, including many Open Data ones. Such data sources are extremely heterogeneous, ranging…

cs.DB2018

On-Demand Big Data Integration: A Hybrid ETL Approach for Reproducible Scientific Research

Pradeeban Kathiravelu, Ashish Sharma, Helena Galhardas +2

Scientific research requires access, analysis, and sharing of data that is distributed across various heterogeneous data sources at the scale of the Internet. An eager ETL process…

cs.CL20134 cited

A Labeled Graph Kernel for Relationship Extraction

Gonçalo Simões, Helena Galhardas, David Matos

In this paper, we propose an approach for Relationship Extraction (RE) based on labeled graph kernels. The kernel we propose is a particularization of a random walk kernel that exp…