IDIR Lab at UT-Arlington

IDIR Lab at UT-Arlington The IDIR Lab in the CSE Department of UT Arlington conducts research in areas of big data and data science.

IDIR lab picnic, Fall 2021
10/24/2021

IDIR lab picnic, Fall 2021

Congratulations to the IDIR Lab's member, Israa Jaradat on receiving NCWIT Collegiate Honorable Mention, for the second ...
04/28/2021

Congratulations to the IDIR Lab's member, Israa Jaradat on receiving NCWIT Collegiate Honorable Mention, for the second time!

NCWIT Aspirations in Computing (AiC) provides technical girls and women with ongoing engagement, visibility, and encouragement for their computing-related interests and achievements. THIS PROMOTION IS IN NO WAY SPONSORED, ENDORSED OR ADMINISTERED BY FACEBOOK, INSTAGRAM OR ANY PARTY OTHER THAN NCWIT....

04/28/2021

The Innovative Data Intelligence Research Laboratory
presented FactWatcher, a system that falls under the umbrella of a young field called "computational journalism".

FactWatcher helps journalists identify data-backed, attention-seizing facts, leads to news stories.

Congratulations to the IDIR Lab's members, Israa Jaradat and Fatma Arslan, as the finalists for NCWIT collegiate awards!...
01/06/2021

Congratulations to the IDIR Lab's members, Israa Jaradat and Fatma Arslan, as the finalists for NCWIT collegiate awards!

“We’re excited to announce the 82 finalists for the Collegiate Award! The NCWIT Collegiate Award honors outstanding technical contributions to projects by women, genderqueer, and nonbinary undergrad and graduate students. See the full list: https://t.co/yMzbgmqWRP.”

04/02/2020

The IDIR Lab's member, Israa Jaradat, has been awarded the prestigious NSF Graduate Research Fellowship! We are proud of her and can't wait to see all the amazing things she will continue to do. Go Israa!

Check out our Medium article "Re-evaluation of Knowledge Graph Completion Methods", written by Farahnaz Akrami, about he...
03/26/2020

Check out our Medium article "Re-evaluation of Knowledge Graph Completion Methods", written by Farahnaz Akrami, about her recently accepted SIGMOD2020 paper.

The study shows that data redundancy and test leakage in widely-used benchmark datasets caused overestimation of the accuracy of many knowledge graph completion models, as large as 175% in some case. Moreover, many of the test instances used for evaluating the models are unrealistic and nonexistent in real-world scenarios. The accuracy of these models, much weaker than we used to perceive, renders link prediction a task without truly effective automated solution. It thus calls for re-investigation of possible effective approaches to this important, challenging problem.

Chengkai Li
Sami Ul Saeef








written by Farahnaz Akrami

03/06/2020
Don't miss our new article about "Finding Factual Claims with BERT" written by Damian Jimenez!
03/05/2020

Don't miss our new article about "Finding Factual Claims with BERT" written by Damian Jimenez!

Around 5 years ago our group at the IDIR Lab began the research project which is now known as ClaimBuster. Today we are presenting updates…

02/06/2019

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