2015/03/01 by Ravali Pochampally, Anish Das Sarma, Pochampally, Ravali +7
Computer Science · Decision Sciences · #Data Quality and Management #Databases (cs.DB) #FOS: Computer and information sciences #Semantic Web and Ontologies #Web Data Mining and Analysis
paper · pdf · doi:10.48550/arxiv.1503.00306
openalex publication_date 2015/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many applications rely on Web data and extraction systems to accomplish knowledge-driven tasks. Web information is not curated, so many sources provide inaccurate, or conflicting information. Moreover, extraction systems introduce additional noise to the data. We wish to automatically distinguish correct data and erroneous data for creating a cleaner set of integrated data. Previous work has shown that a naïve voting strategy that trusts data provided by the majority or at least a certain number of sources may not work well in the presence of copying between the sources. However, correlation between sources can be much broader than copying: sources may provide data from complementary domains (negative correlation), extractors may focus on different types of information (negative correlation), and extractors may apply common rules in extraction (positive correlation, without copying). In this paper we present novel techniques modeling correlations between sources and applying it in truth finding.