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Tien Duc Cao

dblp:185/4294 · DBLP profile ↗
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4ranked-venue papers
3as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 4 · 3 first-authorArtificial intelligence and machine learning · 2 · 2 first-author

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
Data integration and cleaning · 77% Query processing and optimization · 23%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data integration and cleaning
heterogeneous data source integration
0.212016
Mixed-instance querying: a lightweight integration architecture for data journalism · Proc. VLDB Endow. 2016

Methods — techniques the papers use, named apart from their topics

data linking · 0.2
YearPublicationVenuePosition
2019 BeLink: Querying Networks of Facts, Statements and Beliefs
abstract
An important class of journalistic fact-checking scenarios involves verifying the claims and knowledge of different actors at different moments in time. Claims may be about facts, or about other claims, leading to chains of hearsay. We have recently proposed a data model for (time-anchored) facts, statements and beliefs. It builds upon the W3C's RDF standard for Linked Open Data to describe connections between agents and their statements, and to trace information propagation as agents communicate. We propose to demonstrate BeLink, a prototype capable of storing such interconnected corpora, and answer powerful queries over them relying on SPARQL 1.1. The demo will showcase the exploration of a rich real-data corpus built from Twitter and mainstream media, and interconnected through extraction of statements with their sources, time, and topics.
Tien Duc Cao, Ludivine Duroyon, François Goasdoué, Ioana Manolescu, Xavier Tannier
CIKM1
2019 Extracting Statistical Mentions from Textual Claims to Provide Trusted Content
Tien Duc Cao, Ioana Manolescu, Xavier Tannier
NLDB1
2018 Searching for Truth in a Database of Statistics
abstract
The proliferation of falsehood and misinformation, in particular through the Web, has lead to increasing energy being invested into journalistic fact-checking. Fact-checking journalists typically check the accuracy of a claim against some trusted data source. Statistic databases such as those compiled by state agencies are often used as trusted data sources, as they contain valuable, high-quality information. However, their usability is limited when they are shared in a format such as HTML or spreadsheets: this makes it hard to find the most relevant dataset for checking a specific claim, or to quickly extract from a dataset the best answer to a given query.
Tien Duc Cao, Ioana Manolescu, Xavier Tannier
WebDB1
2016 Mixed-instance querying: a lightweight integration architecture for data journalism
abstract
As the world's affairs get increasingly more digital, timely production and consumption of news require to efficiently and quickly exploit heterogeneous data sources. Discussions with journalists revealed that content management tools currently at their disposal fall very short of expectations. We demonstrate T atooine , a lightweight data integration prototype, which allows to quickly set up integration queries across (very) heterogeneous data sources, capitalizing on the many data links (joins) available in this application domain. Our demonstration is based on scenarios we study in collaboration with Le Monde, France's major newspaper.
Raphaël Bonaque, Tien Duc Cao, Bogdan Cautis, François Goasdoué, Javier Letelier, Ioana Manolescu, Oscar Mendoza, Swen Ribeiro, Xavier Tannier, Michaël Thomazo
Proc. VLDB Endow.2