1 code implementation • 27 Nov 2023 • Simone Conia, Min Li, Daniel Lee, Umar Farooq Minhas, Ihab Ilyas, Yunyao Li
Recent work in Natural Language Processing and Computer Vision has been using textual information -- e. g., entity names and descriptions -- available in knowledge graphs to ground neural models to high-quality structured data.
no code implementations • 20 Sep 2023 • Ali Mousavi, Xin Zhan, He Bai, Peng Shi, Theo Rekatsinas, Benjamin Han, Yunyao Li, Jeff Pound, Josh Susskind, Natalie Schluter, Ihab Ilyas, Navdeep Jaitly
Guided by these observations, we construct a new, improved dataset called LAGRANGE using heuristics meant to improve equivalence between KG and text and show the impact of each of the heuristics on cyclic evaluation.
no code implementations • 10 Oct 2018 • Shrinu Kushagra, Shai Ben-David, Ihab Ilyas
In this work, we view de-duplication as a clustering problem where the goal is to put records corresponding to the same physical entity in the same cluster and putting records corresponding to different physical entities into different clusters.
1 code implementation • COLING 2018 • Michael Azmy, Peng Shi, Jimmy Lin, Ihab Ilyas
To address this problem, we present SimpleDBpediaQA, a new benchmark dataset for simple question answering over knowledge graphs that was created by mapping SimpleQuestions entities and predicates from Freebase to DBpedia.