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The quality of Wikipedia articles is debatable. On the one hand, existing research indicates that not only are people willing to contribute articles but the quality of these articles is close to that found in conventional encyclopedias. On the other hand, the public has never stopped criticizing the quality of Wikipedia articles, and critics never have(More)
Establishing semantic interoperability among heterogeneous information sources has been a critical issue in the database community for the past two decades. Despite the critical importance, current approaches to semantic interoperability of heterogeneous databases have not been sufficiently effective. We propose a common ontology called semantic conflict(More)
The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise(More)
Interoperability is the most critical issue facing businesses that need to access information from multiple information systems. Our objective in this research is to develop a comprehensive framework and methodology to facilitate semantic interoperability among distributed and heterogeneous information systems. A comprehensive framework for managing various(More)
Entity identification, i.e., detecting semantically corresponding records from heterogeneous data sources, is a critical step in integrating the data sources. The objective of this research is to develop and evaluate a novel multiple classifier system approach that improves entity identification accuracy. We apply various classification techniques drawn(More)
Data Provenance refers to the “origin”, “lineage”, and “source” of data. In this work, we examine provenance from a semantics perspective and present the W7 model, an ontological model of data provenance. In the W7 model, provenance is conceptualized as a combination of seven interconnected elements including “what”, “when”, “where”, “how”, “who”, “which”(More)
Data Provenance refers to the lineage of data including its origin, key events that occur over the course of its lifecycle, and other details associated with data creation, processing, and archiving. We believe that tracking provenance enables users to share, discover, and reuse the data, thus streamlining collaborative activities, reducing the possibility(More)