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Michel Tourn

dblp:45/587 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Knowledge graphs › knowledge graph management
knowledge base curation
0.212013
Building, maintaining, and using knowledge bases: a report from the trenches · SIGMOD Conference 2013
Information retrieval
query understanding
0.212013
Building, maintaining, and using knowledge bases: a report from the trenches · SIGMOD Conference 2013
Data models and query languages
semistructured data
0.012001
Navigating large-scale semi-structured data in business portals · VLDB 2001
Information retrieval › document retrieval › structured document retrieval
semi-structured retrieval
0.012001
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
YearPublicationVenuePosition
2013 Building, maintaining, and using knowledge bases: a report from the trenches
abstract
A 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 Conference3
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
VLDB7