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- ArticleNovember 2023
Integrating Knowledge Graph Embeddings and Pre-trained Language Models in Hypercomplex Spaces
- Mojtaba Nayyeri,
- Zihao Wang,
- Mst. Mahfuja Akter,
- Mirza Mohtashim Alam,
- Md Rashad Al Hasan Rony,
- Jens Lehmann,
- Steffen Staab
AbstractKnowledge graphs comprise structural and textual information to represent knowledge. To predict new structural knowledge, current approaches learn representations using both types of information through knowledge graph embeddings and language ...
- research-articleOctober 2023
Retention is All You Need
- Karishma Mohiuddin,
- Mirza Ariful Alam,
- Mirza Mohtashim Alam,
- Pascal Welke,
- Michael Martin,
- Jens Lehmann,
- Sahar Vahdati
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge ManagementPages 4752–4758https://doi.org/10.1145/3583780.3615497Skilled employees are the most important pillars of an organization. Despite this, most organizations face high attrition and turnover rates. While several machine learning models have been developed to analyze attrition and its causal factors, the ...
- research-articleJune 2023
LogicENN: A Neural Based Knowledge Graphs Embedding Model With Logical Rules
IEEE Transactions on Pattern Analysis and Machine Intelligence (ITPM), Volume 45, Issue 6Pages 7050–7062https://doi.org/10.1109/TPAMI.2021.3121646Knowledge graph embedding models have gained significant attention in AI research. The aim of knowledge graph embedding is to embed the graphs into a vector space in which the structure of the graph is preserved. Recent works have shown that the inclusion ...
- ArticleMay 2022
Dihedron Algebraic Embeddings for Spatio-Temporal Knowledge Graph Completion
- Mojtaba Nayyeri,
- Sahar Vahdati,
- Md Tansen Khan,
- Mirza Mohtashim Alam,
- Lisa Wenige,
- Andreas Behrend,
- Jens Lehmann
AbstractMany knowledge graphs (KG) contain spatial and temporal information. Most KG embedding models follow triple-based representation and often neglect the simultaneous consideration of the spatial and temporal aspects. Encoding such higher dimensional ...
- ArticleMay 2021
Loss-Aware Pattern Inference: A Correction on the Wrongly Claimed Limitations of Embedding Models
- Mojtaba Nayyeri,
- Chengjin Xu,
- Yadollah Yaghoobzadeh,
- Sahar Vahdati,
- Mirza Mohtashim Alam,
- Hamed Shariat Yazdi,
- Jens Lehmann
Advances in Knowledge Discovery and Data MiningPages 77–89https://doi.org/10.1007/978-3-030-75768-7_7AbstractKnowledge graph embedding models (KGEs) are actively utilized in many of the AI-based tasks, especially link prediction. Despite achieving high performances, one of the crucial aspects of KGEs is their capability of inferring relational patterns, ...
- ArticleMarch 2021
Pattern-Aware and Noise-Resilient Embedding Models
- Mojtaba Nayyeri,
- Sahar Vahdati,
- Emanuel Sallinger,
- Mirza Mohtashim Alam,
- Hamed Shariat Yazdi,
- Jens Lehmann
AbstractKnowledge Graph Embeddings (KGE) have become an important area of Information Retrieval (IR), in particular as they provide one of the state-of-the-art methods for Link Prediction. Recent work in the area of KGEs has shown the importance of ...
- research-articleMarch 2019
Statistical Analysis and Identification of Important Factors of Liver Disease using Machine Learning and Deep Learning Architecture
ICIAI '19: Proceedings of the 2019 3rd International Conference on Innovation in Artificial IntelligencePages 131–137https://doi.org/10.1145/3319921.3319929One of the most prominent and metabolically the most active organ of human body is liver, whose formation is built upon subtle organic compounds and responsible for storing the energy of a living body. Damaging this organ could lead to liver failure and ...
- research-articleMarch 2018
A Reduced feature based neural network approach to classify the category of students
- Mirza Mohtashim Alam,
- Karishma Mohiuddin,
- Amit Kishor Das,
- Md. Kabirul Islam,
- Md. Shamsul Kaonain,
- Md. Haider Ali
ICIAI '18: Proceedings of the 2nd International Conference on Innovation in Artificial IntelligencePages 28–32https://doi.org/10.1145/3194206.3194218To ensure more effectiveness in the learning process in educational institutions, categorization of students is a very interesting method to enhance student's learning capabilities by identifying the factors that affect their performance and use their ...