About the Book
The volume of natural language text data has been rapidly increasing over the past two decades, due to factors such as the growth of the Web, the low cost associated with publishing, and the progress on the digitization of printed texts. This growth combined with the proliferation of natural language systems for search and retrieving information provides tremendous opportunities for studying some of the areas where database systems and natural language processing systems overlap.
This book explores two interrelated and important areas of overlap: (1) managing natural language data and (2) developing natural language interfaces to databases. It presents relevant concepts and research questions, state-of-the-art methods, related systems, and research opportunities and challenges covering both areas. Relevant topics discussed on natural language data management include data models, data sources, queries, storage and indexing, and transforming natural language text. Under natural language interfaces, it presents the anatomy of these interfaces to databases, the challenges related to query understanding and query translation, and relevant aspects of user interactions. Each of the challenges is covered in a systematic way: first starting with a quick overview of the topics, followed by a comprehensive view of recent techniques that have been proposed to address the challenge along with illustrative examples. It also reviews some notable systems in details in terms of how they address different challenges and their contributions. Finally, it discusses open challenges and opportunities for natural language management and interfaces.
The goal of this book is to provide an introduction to the methods, problems, and solutions that are used in managing natural language data and building natural language interfaces to databases. It serves as a starting point for readers who are interested in pursuing additional work on these exciting topics in both academic and industrial environments.
About the Author :
Yunyao Li is a Senior Research Manager and Research Staff Member with IBM Research - Almaden. She is also a Master Inventor and a member of IBM Academy of Technology. Her expertise is in the interdisciplinary areas of databases, natural language processing, human-computer interaction, machine learning, and information retrieval. Her contributions in these areas have led to over 50 research publications, more than 20 patents granted or filed, multiple graduatelevel courses (including 2 Massive Open Online Courses), and billions of revenue generated from technology transfer. She is widely recognized in these areas both within IBM and by the external research community, and regularly serves on prestigious program committees, editorial boards, and review panels. Her current research interest focuses on taming unstructured and semi-structured content to enable the building of new generations of AI applications for the enterprise. Yunyao is also passionate about improving the diversity for the STEM field. She has been actively mentoring women and under-represented minorities for over ten years. She received her Ph.D. and master's degrees from the University of Michigan, Ann Arbor and undergraduate degrees from Tsinghua University, Beijing, China. Davood Rafiei did his undergrad work at the Sharif University of Technology, his M.Sc. at the University of Waterloo, and his Ph.D. at the University of Toronto before joining the University of Alberta, where he is now Associate Professor of Computer Science and member of the Database Systems Research Group. His areas of expertise, which span over databases and the Web, is on managing large complex data. In particular, his research interest includes natural language data management and integration, spatial analysis and mining of web content, similarity-based queries and indexing, and web information retrieval. Davood regularly serves in the program committees of major database and data mining conferences (such as SIGMOD, VLDB, KDD, ICDM, CIKM) and Web conferences (such as WWW). Davood has spent time, as a visiting scientist, at Google (Mountain View), Kyoto University, and the University of Paris Descartes. H. V. Jagadish is Bernard A Galler Collegiate Professor of Electrical Engineering and Computer Science, and Distinguished Scientist at the Institute for Data Science, at the University of Michigan in Ann Arbor. Prior to 1999, he was Head of the Database Research Department at AT&T Labs, Florham Park, NJ.
Professor Jagadish is well known for his broad-ranging research on information management, and has approximately 200 major papers and 37 patents. He is a fellow of the ACM, The First Society in Computing, (since 2003) and serves on the board of the Computing Research Association (since 2009). He has been an Associate Editor for the ACM Transactions on Database Systems (1992-1995), Program Chair of the ACM SIGMOD annual conference (1996), Program Chair of the ISMB conference (2005), a trustee of the VLDB (Very Large DataBase) foundation (2004-2009), Founding Editor-in-Chief of the Proceedings of the VLDB Endowment (2008-2014), and Program Chair of the VLDB Conference (2014). Among his many awards, he won the ACM SIGMOD Contributions Award in 2013 and the David E Liddle Research Excellence Award (at the University of Michigan) in 2008.
