Keynote Speakers
Eugene Agichtein, Emory University (USA)
Modeling Explicit and Implicit User Behavior for Finding Answers on the Web
Abstract: Billions of people interact with Web search engines and online forums. These data can be used to derive models of searcher intent, attention, and satisfaction, and, in turn to improve question answering tasks, such as candidate passage retrieval, answer selection, and presentation. I will describe how explicit interactions captured using lightweight instrumentation of both search- and landing pages can be converted to attention and satisfaction signals. Then, I will discuss how these signals can be used to improve selection of relevant documents, passages, and answers, and our initial work on adapting these ideas to new interaction modalities.Speaker Bio
Dr. Eugene Agichtein is a Winship Associate Professor of Computer Science at Emory University in Atlanta, USA, where he leads the Intelligent Information Access Laboratory (IR Lab). Since January 2019, he has been a part-time "Amazon Scholar” at Amazon. Eugene's research spans the areas of information retrieval, natural language processing, data mining, and human computer interaction, most recently focusing on conversational search. Together with great colleagues and students, Eugene published over 100 papers, and has been recognized by multiple awards, including the A.P. Sloan fellowship and the 2013 Karen Spark Jones Award from the British Computer Society, and "test of time" awards from the SIGIR and WSDM conference. He was Program Co-Chair of the WSDM 2012 and WWW 2017 conferences.
Xin Luna Dong, Amazon (USA)
Building a Broad Knowledge Graph for Products
Abstract: Knowledge graphs have been used to support a wide range of applications and enhance search results for multiple major search engines, such as Google and Bing. At Amazon we are building a Product Graph, an authoritative knowledge graph for all products in the world. The thousands of product verticals we need to model, the vast number of data sources we need to extract knowledge from, the huge volume of new products we need to handle every day, and the various applications in Search, Discovery, Personalization, Voice, that we wish to support, all present big challenges in constructing such a graph.In this talk we describe our efforts in building a broad product graph, a graph that starts shallow with core entities and relationships, and allows easily adding verticals and relationships in a pay-as-you-go fashion. We describe our efforts on knowledge extraction, linkage, and cleaning to significantly improve the coverage and quality of product knowledge. We also present our progress towards our moon-shot goals including harvesting knowledge from the web, hands-off-the-wheel knowledge integration and cleaning, human-in-the-loop knowledge learning, and graph mining and graph-enhanced search.
Speaker Bio
Dr. Xin Luna Dong is a Principal Scientist at Amazon, leading the efforts of constructing Amazon Product Knowledge Graph. She was one of the major contributors to the Google Knowledge Vault project, and has led the Knowledge-based Trust project, which is called the “Google Truth Machine” by Washington’s Post. She has co-authored book “Big Data Integration”, was awarded ACM Distinguished Member, VLDB Early Career Research Contribution Award for "advancing the state of the art of knowledge fusion", and Best Demo award in Sigmod 2005. She serves in VLDB endowment and PVLDB advisory committee, and is a PC co-chair for VLDB 2021, ICDE Industry 2019, VLDB Tutorial 2019, Sigmod 2018 and WAIM 2015.
