Основные понятия
Linked Open Data query-logs provide valuable insights when curated and analyzed with a trust-based approach.
Аннотация
Linked Open Data (LOD) query-logs offer essential information for decision-making, derived from user interactions with web sources. The complexity of LOD logs poses challenges related to Quality and Provenance, impacting their trustworthiness. To address these issues, a layered architecture is proposed for end-to-end analytics of LOD query-logs. The architecture includes layers for Raw query-logs, Preparation and Curation, Storage, and Analytics. Trust is a central concern throughout the process to ensure the reliability of the data. By profiling logs and analyzing their quality and trust aspects, meaningful insights can be extracted from LOD query-logs. Experiments conducted on real LOD logs validate the effectiveness of the proposed solution in cleansing and curating these logs for further analysis.
Статистика
LOD logs contain 5.499.797 raw queries in Scholarly Data log.
DBpedia log contains 3.193.672 raw queries with 43.284 academic queries.
Trust Degree formula: TrustDegree = 1 / NB_parameters * Σ(f(xij))
Цитаты
"Trust is a complex concept linked to risk, quality, and provenance in LOD query-logs."
"Efficient curation processes are essential to ensure the reliability of source query-logs."
"The layered architecture provides a comprehensive solution for preparing and analyzing LOD query-logs."