Co-located with SIGIR 2018, Ann Arbor, Michigan, USA -- 12 July 2018
Professional search in specific domains has been addressed in IR research over the last decades. Although each domain (e.g. legal, medical, academic, governmental) has its own idiosyncrasies, professional search tasks have specific requirements in common that are different from requirements of generic web search engines. These requirements follow directly from the context and needs of professional searchers: Searchers in different domains often exhibit particular search behavior different from general Web search. These unique behavioral patterns can be both a nature of the profession as well as a result of using a particular professional search tool.
This workshop will address the specific requirements for professional search from multiple angles; covering many different facets of professional search in an interactive setting where researchers work with input from information professionals to their mutual benefit. The workshop will deliver a roadmap of research directions for the years to come.
We invite submissions of research papers and position papers with a maximum length of four pages ACM style. Please anonymize your paper; the reviewing process will be double blind. The selection of papers is based on relevance, quality and diversity. The workshop is also open for papers presenting work in progress and challenges of new projects. The proceedings will be published as CEUR workshop proceedings.
List of topics:
|9:15 - 9:30||Introduction|
|9:30 - 10:00||Presentation of survey results|
|10:00 - 10:30||Coffee Break I|
|10:30 - 11:15||Keynote 1: Paul Bennett|
|11:15 - 12:00||3 oral presentations|
|12:00 - 13:30||Lunch|
|13:30 - 14:15||Keynote 2: David D. Lewis|
|14:15 - 15:00||3 oral presentations|
|15:00 - 15:30||Coffee Break II|
|15:30 - 16:30||Breakout session|
|16:30 - 17:00||Reports from the breakout sessions & conclusions|
Paul Bennett is the Principal Research Manager of the Information & Data Sciences group in Microsoft Research AI. His published research has focused on a variety of topics surrounding the use of machine learning in information retrieval - including ensemble methods and the combination of information sources, calibration, consensus methods for noisy supervision labels, active learning and evaluation, supervised classification and ranking, crowdsourcing, behavioral modeling and analysis, and personalization. Some of his work has been recognized with awards at SIGIR, CHI, and ACM UMAP as well as an ECIR Test of Time Honorable Mention award. Prior to joining MSR in 2006, he completed his dissertation in the Computer Science Department at Carnegie Mellon with Jaime Carbonell and John Lafferty. While at CMU, he also acted as the Chief Learning Architect on the RADAR project from 2005-2006 while a postdoctoral fellow in the Language Technologies Institute.
David D. Lewis, Ph.D. is Chief Data Scientist at Brainspace, A Cyxtera Business. He leads the data science team developing new information retrieval, machine learning, and natural language processing technologies for legal, investigatory, and intelligence applications. He is a Fellow of the American Society for the Advancement of Science, and won a Test of Time Award from SIGIR in 2017 for his 1994 paper introducing the uncertainty sampling algorithm for active learning.
Allan Hanbury, Charlie Hull, David Elsweiler, Diane Kelly, Elaine Toms, Farhad Shokraneh, Jaap Kamps, Julie Glanville, Krisztian Balog, Laura Dietz, Martin White, Michail Salampasis, Mihai Lupu, Norbert Fuhr, Peter Cotroneo, Ray Daley, Rene Spijker.