Demo Data from SIGIR 2026 / reviewers in the wild / expert
Alexandre Michetti Manduca
dblp:148/4227
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Knowledge graphs · 67% Data integration and cleaning · 33% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge graphs
knowledge graph construction |
0.4 | 1 | 2020 | AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types · KDD 2020 |
Knowledge graphs › domain-specific knowledge graph
product knowledge graph |
0.4 | 1 | 2020 | AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types · KDD 2020 |
Methods — techniques the papers use, named apart from their topics
self-supervised learning · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of TypesabstractCan one build a knowledge graph (KG) for all products in the world? Knowledge graphs have firmly established themselves as valuable sources of information for search and question answering, and it is natural to wonder if a KG can contain information about products offered at online retail sites. There have been several successful examples of generic KGs, but organizing information about products poses many additional challenges, including sparsity and noise of structured data for products, complexity of the domain with millions of product types and thousands of attributes, heterogeneity across large number of categories, as well as large and constantly growing number of products. Xin Dong 0001, Xiang He 0007, Andrey Kan, Yan Liang 0004, Jun Ma 0029, Yifan Ethan Xu, Tong Zhao 0002, Gabriel Blanco Saldana, Saurabh Deshpande, Alexandre Michetti Manduca, Jay Ren, Surender Pal Singh, Fan Xiao 0001, Haw-Shiuan Chang, Giannis Karamanolakis, Yuning Mao, Yaqing Wang 0001, Christos Faloutsos, Andrew McCallum, Jiawei Han 0001 |
KDD | 12 |