Trust-Aware Curation Of Linked Open Data Logs

Trust was widely discussed and formalized in the literature. In the context of Big Data and Connected World, it becomes crucial for developing data-driven solutions. Trusted data increase the quality of decision support systems. Recently, companies are racing towards Linked Open Data (LOD) and Knowledge Bases (KB) to improve their added value, but ignore their SPARQL query-logs. If well cured, these logs can present an asset for analysts. A naive and direct use of these logs is too risky because their provenance and quality are highly questionable. Users of these logs in a trusted way have to be assisted by providing them with in-depth knowledge of the whole LOD environment and tools to cure these logs. In this paper, we propose an ontology-based model inspired by the recent developments in < trust, risk, value >-ontology engineering. Then, a trust-aware curation approach is presented, composed of enriched ETL-like operators integrating trust metrics that keep only trustworthy queries. Finally, experiments are conducted to study the effectiveness and efficiency of our proposal.