Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Kedar Takwane

dblp:380/1899 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0002-8071-7302ORCID · reported

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

Databases, data management, data science and information retrieval · 1 · 1 since 2021

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
Information retrieval · 100%
Artificial intelligence
1 paper
Question answering and dialogue systems · 100%
Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval
personal information management
0.812024
TextData: Save What You Know and Find What You Don't · SIGIR 2024
Information retrieval › query understanding › query intent understanding
search intent prediction
0.812024
TextData: Save What You Know and Find What You Don't · SIGIR 2024
Natural language and speech › Question answering and dialogue systems › answer generation
generative question answering
0.212024
TextData: Save What You Know and Find What You Don't · SIGIR 2024
Visualization and visual analytics › information visualization
relationship visualization
0.212024
TextData: Save What You Know and Find What You Don't · SIGIR 2024

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

markdown · 2.3large language model · 2.3
YearPublicationVenuePosition
2024 TextData: Save What You Know and Find What You Don't
abstract
In this demonstration, we present TextData, a novel online system that enables users to both "save what they know" and "find what they don't". TextData was developed based on the Community Digital Library (CDL) system. Although the CDL allowed users to bookmark webpages with plain text and provided search and recommendation, it fell short in key features. To better help users save what they know, TextData offers the addition of markdown to submissions for providing a richer method of note-taking. To better help users find what they don't, TextData provides methods for visualizing the relationships among submissions and provides in-context interactive search intent prediction with question-answering via a generative large language model. TextData is free-to-use, can be accessed online, and the source code is publicly available.
Kevin Ros, Kedar Takwane, Ashwin Patil, Rakshana Jayaprakash, ChengXiang Zhai
SIGIR2