Demo Data from EDBT 2026 / reviewers in the wild / expert
Michel Tourn
dblp:45/587
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 2
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
2 papers |
Information retrieval · 47% Knowledge graphs · 44% Data models and query languages · 8% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge graphs › knowledge graph management
knowledge base curation |
0.2 | 1 | 2013 | Building, maintaining, and using knowledge bases: a report from the trenches · SIGMOD Conference 2013 |
Information retrieval
query understanding |
0.2 | 1 | 2013 | Building, maintaining, and using knowledge bases: a report from the trenches · SIGMOD Conference 2013 |
Data models and query languages
semistructured data |
0.0 | 1 | 2001 | Navigating large-scale semi-structured data in business portals · VLDB 2001 |
Information retrieval › document retrieval › structured document retrieval
semi-structured retrieval |
0.0 | 1 | 2001 | Navigating large-scale semi-structured data in business portals · VLDB 2001 |
Methods — techniques the papers use, named apart from their topics
case study · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | Building, maintaining, and using knowledge bases: a report from the trenchesabstractA knowledge base (KB) contains a set of concepts, instances, and relationships. Over the past decade, numerous KBs have been built, and used to power a growing array of applications. Despite this flurry of activities, however, surprisingly little has been published about the end-to-end process of building, maintaining, and using such KBs in industry. In this paper we describe such a process. In particular, we describe how we build, update, and curate a large KB at Kosmix, a Bay Area startup, and later at WalmartLabs, a development and research lab of Walmart. We discuss how we use this KB to power a range of applications, including query understanding, Deep Web search, in-context advertising, event monitoring in social media, product search, social gifting, and social mining. Finally, we discuss how the KB team is organized, and the lessons learned. Our goal with this paper is to provide a real-world case study, and to contribute to the emerging direction of building, maintaining, and using knowledge bases for data management applications. Omkar Deshpande, Digvijay S. Lamba, Michel Tourn, Sanjib Das, Sri Subramaniam, Anand Rajaraman, Venky Harinarayan, AnHai Doan |
SIGMOD Conference | 3 |
| 2001 | Navigating large-scale semi-structured data in business portals
Mani Abrol, Neil Latarche, Uma Mahadevan, Jianchang Mao, Rajat Mukherjee, Prabhakar Raghavan, Michel Tourn, Grace Zhang |
VLDB | 7 |