Corroborating Information from Disagreeing Views

Alban Galland (1) , Serge Abiteboul (1) , Amélie Marian (2) , Pierre Senellart (3)

1 LEO - Distributed and heterogeneous data and knowledge
2 RU - Rutgers, The State University of New Jersey [New Brunswick]
3 Télécom ParisTech

We consider a set of views stating possibly conflicting facts. Negative facts in the views may come, e.g., from functional dependencies in the underlying database schema. We want to predict the truth values of the facts. Beyond simple methods such as voting (typically rather accurate), we explore techniques based on ``corroboration'', i.e., taking into account trust in the views. We introduce three fixpoint algorithms corresponding to different levels of complexity of an underlying probabilistic model. They all estimate both truth values of facts and trust in the views. We present experimental studies on synthetic and real-world data. This analysis illustrates how and in which context these methods improve corroboration results over baseline methods. We believe that corroboration can serve in a wide range of applications such as source selection in the semantic Web, data quality assessment or semantic annotation cleaning in social networks. This work sets the bases for a wide range of techniques for solving these more complex problems.

Mots clés: Corroboration; view; confidence; probabilistic model; fixpoint; contradiction

Domaines: Base de données [cs.DB]

Alban Galland, Serge Abiteboul, Amélie Marian, Pierre Senellart. Corroborating Information from Disagreeing Views. International Conference on Web Search and Data Mining (WSDM), Feb 2010, New York City, United States. ⟨inria-00429546⟩