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EDBT 2026 Demo / reviewers in the wild / expert

Andrei Lifchits

dblp:38/3870 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2006
—ORCID · none

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

Artificial intelligence and machine learning · 2Graphics, computer vision, multimedia, augmented reality and games · 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.

Artificial intelligence
2 papers
Knowledge representation and reasoning · 67% Information extraction and text analysis · 33%
Databases, data mining, and information retrieval
2 papers
Web and social media mining · 44% Data mining · 44% Information retrieval · 13%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge acquisition › knowledge extraction
fact extraction		 0.122006
Names and Similarities on the Web: Fact Extraction in the Fast Lane · ACL 2006
Organizing and Searching the World Wide Web of Facts - Step One: The One-Million Fact Extraction Challenge · AAAI 2006
Natural language and speech › Information extraction and text analysis
open information extraction		 0.112006
Organizing and Searching the World Wide Web of Facts - Step One: The One-Million Fact Extraction Challenge · AAAI 2006
Data mining › text mining
fact extraction		 0.112006
Names and Similarities on the Web: Fact Extraction in the Fast Lane · ACL 2006
Web and social media mining › web mining
web text mining		 0.112006
Names and Similarities on the Web: Fact Extraction in the Fast Lane · ACL 2006
Information retrieval
web search		 0.012006
Organizing and Searching the World Wide Web of Facts - Step One: The One-Million Fact Extraction Challenge · AAAI 2006

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

pattern-based extraction · 0.2distributional similarity · 0.1
YearPublicationVenuePosition
2006 Organizing and Searching the World Wide Web of Facts - Step One: The One-Million Fact Extraction Challenge
Marius Pasca, Dekang Lin, Jeffrey Bigham, Andrei Lifchits, Alpa Jain
 AAAI4
2006 Names and Similarities on the Web: Fact Extraction in the Fast Lane
abstract
In a new approach to large-scale extraction of facts from unstructured text, distributional similarities become an integral part of both the iterative acquisition of high-coverage contextual extraction patterns, and the validation and ranking of candidate facts. The evaluation measures the quality and coverage of facts extracted from one hundred million Web documents, starting from ten seed facts and using no additional knowledge, lexicons or complex tools.
Marius Pasca, Dekang Lin, Jeffrey Bigham, Andrei Lifchits, Alpa Jain
 ACL4