III: EAGER: Data Integration as a Dialogue with the User

这项工作建立了一种新的方法来提供需要集成和结构化的特别（“发现”）查询：这种查询帮助科学家了解主题之间可能的关系，并帮助决策者或消费者探索选项。这项工作开发了一个基于迭代过程的新系统和底层架构，其中系统和用户进行对话，直到用户得到满足他或她的信息需求的答案。生成的系统获取Web上的资源，发现它们之间的语义关系，并允许用户提出发现查询。它利用现有的提取、匹配和推荐算法作为证据来源来生成假设和相应的查询，并根据用户对查询结果的反馈来调整这些假设。创新包括用于组合特征和学习重新加权假设的可扩展模型；查询和源推荐技术；以及推广二元反馈来支持或反驳假设的方法。研究影响是最终用户数据集成的新范式，可扩展地结合机器学习和数据库概念。更广泛的影响包括为科学用户和其他迫切需要它们的用户提供更好的发现工具；改进现有Web数据资源的集成；还有新的教育材料，说明数据网络如何与系统和人的网络一样重要。PI正在将研究概念纳入宾夕法尼亚大学新开设的市场和社会系统工程项目的课程中，重点关注互联网上的人、协议和系统之间的接口，特别是通过社会和数据网络以及市场。有关该项目的更多信息可在项目网站http://www.cis.upenn.edu/~zives/dialogue/上找到

This work establishes a new approach to providing ad hoc ("discovery") queries requiring integration and structuring: such queries help scientists learn possible relationships between topics, and help decision-makers or consumers explore options. The work develops a new system and underlying architecture based on an iterative process, where the system and user engage in a dialogue until the user has answers meeting his or her information need. The resulting system takes sources on the Web, discovers semantic relationships among them, and allows users to pose discovery queries. It leverages existing extraction, matching, and recommendation algorithms as sources of evidence to generate hypotheses and corresponding queries, and adjusts these hypotheses based on user feedback over the query results. Innovations include scalable models for combining features and learning to re weight hypotheses; query and source recommendation techniques; and means of generalizing tuple-based feedback to support or refute hypotheses. The research impact is a new paradigm for data integration by end users, which scalably combines machine learning and database concepts. The broader impact includes better discovery tools for scientific users and other users who sorely need them; improved integration of existing Web data resources; and new educational material on how networks of data can be as important as networks of systems and people. The PI is incorporating the research concepts into courses in the University of Pennsylvania's new Market and Social Systems Engineering Program, focused on the interface between people, protocols, and systems on the Internet, especially through social and data networks, as well as markets. More information on the project can be found on the project website at http://www.cis.upenn.edu/~zives/dialogue/