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From databases to artificial intelligence

This habilitation thesis synthesizes our research efforts in the area of relational databases and artificial intelligence. In particular, we present our work on various data matching problems, where we need to establish connections between different pieces of information, such that these correspondences reflect our human understanding of the relation among them. Entity resolution aims to identify entity references (person or company names, geographic locations) such that they refer to the same real word entity. We summarize here our work on this problem in the context of Web documents. Our methods rely on supervised machine learning techniques. We discuss our work on database schema matching. Our work addressed a specific setting of this problem, where we need to match a set of schemas based on a network of their pairwise interactions. Even if available schema matching tools can obtain a set of good quality attribute correspondences, if we would like to use them for data integration, we need to eliminate the remaining errors. This phase still requires the involvement of human experts. We model this post-matching phase and we propose new techniques to reduce the necessary human efforts and to guide the work of the experts. Our methods rely on (probabilistic) reasoning methods that exploit the relevant consistency constraints. Crowdsourcing is a model where one can request a service that is realized by human workers form from a large crowd of users, in exchange for a small payment. Crowdsourcing platforms enable human-in-the-loop algorithms and they were also used to construct large labelled datasets. A central issue in this setting is the quality of the obtained results, in particular in the context of knowledge-intensive crowdsourcing, where workers need specific skills to complete the requested task. Affecting the tasks to workers who do not have these skills could lead to poor quality results. We propose new methods which can improve the expected quality of the results through the use of simple forms of reasoning on human skills. We also discuss our perspectives for future research directions.