Chen Ye - Knowledge Discovery from Multi-Sourced Data

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With the rapid development of information technology, all areas have ushered in the era of big data. One big challenge in analyzing the overwhelming generated data is the veracity of the data. Data, even describing the same object or event, can come from a variety of sources such as crowd workers and social media users. However, noisy pieces of data or information are unavoidable. Facing the daunting scale of data, it is unrealistic to expect humans to label or tell which data source is more reliable. Hence, it is crucial to identify trustworthy information from multiple noisy information sources, referring to the task of knowledge discovery.

At present, the knowledge discovery research for multi-sourced data mainly faces two challenges. On the structural level, it is essential to consider the different characteristics of data composition and application scenarios and define the knowledge discovery problem on different occasions. On the algorithm level, the knowledge discovery task needs to consider different levels of information conflicts and design efficient algorithms to mine more valuable information using multiple clues. Existing knowledge discovery methods have defects on both the structural level and the algorithm level, making the knowledge discovery problem far from totally solved.

In this book, the theories, techniques, and methods in data cleaning, data mining, and natural language processing are synthetically used to study the knowledge discovery problem on multi-source data. This book mainly focuses on three data models: the first is multi-source isomorphic data, which has a clear and significant entity-attribute-source structure; the second is multi-source heterogeneous data, where the entities and attributes from different sources may have various representations; and the third is text data, which does not intuitively reflect the entity-attribute-source structure and contains a lot of irrelevant words. On the basis of three data models, this book studies the knowledge discovery problems including truth discovery, pattern discovery, and fact discovery on multi-source data from four important properties: relevance, inconsistency, sparseness, and heterogeneity. We hope the proposed ideas in this book can inspire researchers in both academics and industry, and further prompt them to join the field of knowledge discovery.