III: Small: Towards Resolving Ad-hoc Concept Queries with Table Answers via Multi-source Data Mining

用户经常对某些概念发出查询，以收集信息并做出决定。此类查询中涉及的概念通常是特别的，不太可能直接包含在预定义的模式中。对查询的理想响应应该是一个表，其中属于被查询概念的实体作为行，相关属性作为列。然而，在大多数情况下，没有这样的表格随时可用，用户必须自己收集相关信息，这是一个痛苦的过程，特别是当用户正在探索不熟悉的概念，不知道哪些信息对他们的决策至关重要的时候。该项目构建了首个使用表格答案解决即席概念查询的框架，以节省用户大量的信息收集工作；待开发的框架挖掘包括知识库、文本和表格在内的多个互补数据源，并提出系统的方法结合相关知识来解决特定的查询。这个项目有可能通过关注现实的特别问题而不是简单的百科全书式的问题来建立一个实用的和变革性的问题回答系统。它可以进一步指导医疗、社会、教育、管理等各个领域的专业化答疑系统的建设。它将开启一系列工作，根据查询需要组合多个源，并以特殊方式为许多其他任务进行组合。这在大数据时代是至关重要的，因为许多领域，如医疗保健，都有新兴的互补数据源，如文本、数据库、表格和人际网络。该项目将积极参与外展教育项目，例如，那些接待未被充分代表的高中生作为暑期实习生的项目。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Users often issue queries about certain concepts to gather information and make decisions. The concepts concerned in such queries are usually ad-hoc and less likely to be directly covered in a predefined schema. An ideal response to the query would be a table with entities belonging to the queried concept as the rows and relevant attributes being the columns. However, in most cases, no such tables are readily available, and users have to collect relevant information by themselves, which is a painful process, especially when users are exploring unfamiliar concepts and do not know what information is critical for their decision making. This project builds the first-of-its-kind framework to resolve ad-hoc concept queries with table answers to save users tremendous efforts on information gathering.The framework to be developed mines multiple complementary data sources including knowledge bases, texts, and tables, and proposes systematic methods to combine relevant knowledge to solve a specific query. This project has the potential to build a practical and transformative question answering system, by focusing on realistic ad-hoc queries rather than simple encyclopedic questions. It can further guide the construction of specialized question answering systems in various domains including medical, social, education, and management. It will open up a series of work on combining multiple sources based on the query need and in an ad-hoc manner for many other tasks. This is critical in the big data age, when many domains like healthcare have emerging complementary data sources such as texts, databases, tables, and human networks. The project will actively participate in outreach education programs, e.g., those to host underrepresented high school students as summer interns.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.