Portal de Periódicos da CAPES

Roomba: An Extensible Framework to Validate and Build Dataset Profiles

2015; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-319-25639-9_46

1611-3349

Ahmad Assaf, Raphaël Troncy, Aline Senart,

Advanced Database Systems and Queries

Linked Open Data (LOD) has emerged as one of the largest collections of interlinked datasets on the web. In order to benefit from this mine of data, one needs to access to descriptive information about each dataset (or metadata). This information can be used to delay data entropy, enhance dataset discovery, exploration and reuse as well as helping data portal administrators in detecting and eliminating spam. However, such metadata information is currently very limited to a few data portals where they are usually provided manually, thus being often incomplete and inconsistent in terms of quality. To address these issues, we propose a scalable automatic approach for extracting, validating, correcting and generating descriptive linked dataset profiles. This approach applies several techniques in order to check the validity of the metadata provided and to generate descriptive and statistical information for a particular dataset or for an entire data portal.