Abstract

For decades, multiple research communities—including Databases, Information Retrieval, Natural Language Processing, Data Mining, and AI—have pursued the mission of delivering the right information at the right time. These efforts span web search, data integration, knowledge graphs, and question answering. Recent advancements in Large Language Models (LLMs) have brought remarkable progress in language understanding and generation, reshaping approaches across all these fronts. Yet, limitations such as factual inaccuracies and hallucinations restrict their suitability for building knowledgeable and trustworthy assistants.

About the speaker

Xin Luna Dong is a Principal Scientist at Meta Wearables AI, where she leads the Agentic AI efforts for building trustworthy and personalized assistants on wearable devices. Previously, she spent over a decade advancing knowledge graph technology, including the Amazon Product Graph and the Google Knowledge Graph. She is co-author of Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases and Big Data Integration. She is an ACM Fellow and IEEE Fellow, recognized for “significant contributions to knowledge graph construction and data integration.” She was named an ACM Fellow and an IEEE Fellow for "significant contributions to knowledge graph construction and data integration", awarded the VLDB Women in Database Research Award and VLDB Early Career Research Contribution Award, and invited as an ACM Distinguished Speaker. She serves in the PVLDB advisory committee, was a member of the VLDB endowment, a PC co-chair for KDD’2022 ADS track, WSDM’2022, VLDB’2021, and Sigmod’2018.