61 citations · 133 across the 16 of their papers we have counts for
18 papers
SCI-IDEA: Context-Aware Scientific Ideation Using Token and Sentence Embeddings
Farhana Keya, Gollam Rabby, Prasenjit Mitra +3
Every scientific discovery starts with an idea inspired by prior work, interdisciplinary concepts, and emerging challenges. Recent advancements in large language models (LLMs) trai…
SelfCheck-Eval: A Multi-Module Framework for Zero-Resource Hallucination Detection in Large Language Models
Diyana Muhammed, Giusy Giulia Tuccari, Gollam Rabby +2
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse applications, from open-domain question answering to scientific writing, medical decision supp…
Retention Is All You Need
Karishma Mohiuddin, Mirza Ariful Alam, Mirza Mohtashim Alam +4
Skilled employees are the most important pillars of an organization. Despite this, most organizations face high attrition and turnover rates. While several machine learning models…
ProjB: An Improved Bilinear Biased ProjE model for Knowledge Graph Completion
Mojtaba Moattari, Sahar Vahdati, Farhana Zulkernine
Knowledge Graph Embedding (KGE) methods have gained enormous attention from a wide range of AI communities including Natural Language Processing (NLP) for text generation, classifi…
LEMON: LanguagE ModeL for Negative Sampling of Knowledge Graph Embeddings
Md Rashad Al Hasan Rony, Mirza Mohtashim Alam, Semab Ali +2
Knowledge Graph Embedding models have become an important area of machine learning.Those models provide a latent representation of entities and relations in a knowledge graph which…
Trans4E: Link Prediction on Scholarly Knowledge Graphs
Mojtaba Nayyeri, Gokce Muge Cil, Sahar Vahdati +8
The incompleteness of Knowledge Graphs (KGs) is a crucial issue affecting the quality of AI-based services. In the scholarly domain, KGs describing research publications typically…
Multiple Run Ensemble Learning with Low-Dimensional Knowledge Graph Embeddings
Chengjin Xu, Mojtaba Nayyeri, Sahar Vahdati +1
Among the top approaches of recent years, link prediction using knowledge graph embedding (KGE) models has gained significant attention for knowledge graph completion. Various embe…
Motif Learning in Knowledge Graphs Using Trajectories Of Differential Equations
Mojtaba Nayyeri, Chengjin Xu, Jens Lehmann +1
Knowledge Graph Embeddings (KGEs) have shown promising performance on link prediction tasks by mapping the entities and relations from a knowledge graph into a geometric space (usu…
5* Knowledge Graph Embeddings with Projective Transformations
Mojtaba Nayyeri, Sahar Vahdati, Can Aykul +1
Performing link prediction using knowledge graph embedding models has become a popular approach for knowledge graph completion. Such models employ a transformation function that ma…
Adaptive Margin Ranking Loss for Knowledge Graph Embeddings via a Correntropy Objective Function
Mojtaba Nayyeri, Xiaotian Zhou, Sahar Vahdati +2
Translation-based embedding models have gained significant attention in link prediction tasks for knowledge graphs. TransE is the primary model among translation-based embeddings a…
Soft Marginal TransE for Scholarly Knowledge Graph Completion
Mojtaba Nayyeri, Sahar Vahdati, Jens Lehmann +1
Knowledge graphs (KGs), i.e. representation of information as a semantic graph, provide a significant test bed for many tasks including question answering, recommendation, and link…
Unveiling Scholarly Communities over Knowledge Graphs
Sahar Vahdati, Guillermo Palma, Rahul Jyoti Nath +3
Knowledge graphs represent the meaning of properties of real-world entities and relationships among them in a natural way. Exploiting semantics encoded in knowledge graphs enables…
Towards a Cloud-Based Service for Maintaining and Analyzing Data About Scientific Events
Andreas Behrend, Sahar Vahdati, Christoph Lange +1
We propose the new cloud-based service OpenResearch for managing and analyzing data about scientific events such as conferences and workshops in a persistent and reliable way. This…
A Semi-Automatic Approach for Detecting Dataset References in Social Science Texts
Behnam Ghavimi, Philipp Mayr, Christoph Lange +2
Today, full-texts of scientific articles are often stored in different locations than the used datasets. Dataset registries aim at a closer integration by making datasets citable b…
Identifying and Improving Dataset References in Social Sciences Full Texts
Behnam Ghavimi, Philipp Mayr, Sahar Vahdati +1
Scientific full text papers are usually stored in separate places than their underlying research datasets. Authors typically make references to datasets by mentioning them for exam…
Semantic Publishing Challenge - Assessing the Quality of Scientific Output by Information Extraction and Interlinking
Angelo Di Iorio, Christoph Lange, Anastasia Dimou +1
The Semantic Publishing Challenge series aims at investigating novel approaches for improving scholarly publishing using Linked Data technology. In 2014 we had bootstrapped this ef…
Mapping Large Scale Research Metadata to Linked Data: A Performance Comparison of HBase, CSV and XML
Sahar Vahdati, Farah Karim, Jyun-Yao Huang +1
OpenAIRE, the Open Access Infrastructure for Research in Europe, comprises a database of all EC FP7 and H2020 funded research projects, including metadata of their results (publica…
OpenCourseWare Observatory -- Does the Quality of OpenCourseWare Live up to its Promise?
Sahar Vahdati, Christoph Lange, Sören Auer
A vast amount of OpenCourseWare (OCW) is meanwhile being published online to make educational content accessible to larger audiences. The awareness of such courses among users and…