Search Results for author: Mojtaba Nayyeri

Found 24 papers, 8 papers with code

Hybrid Reasoning Based on Large Language Models for Autonomous Car Driving

1 code implementation21 Feb 2024 Mehdi Azarafza, Mojtaba Nayyeri, Charles Steinmetz, Steffen Staab, Achim Rettberg

Large Language Models (LLMs) have garnered significant attention for their ability to understand text and images, generate human-like text, and perform complex reasoning tasks.

Autonomous Driving Common Sense Reasoning +1

HGE: Embedding Temporal Knowledge Graphs in a Product Space of Heterogeneous Geometric Subspaces

1 code implementation21 Dec 2023 Jiaxin Pan, Mojtaba Nayyeri, Yinan Li, Steffen Staab

Temporal knowledge graphs may exhibit static temporal patterns at distinct points in time and dynamic temporal patterns between different timestamps.

Knowledge Graphs

NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning

1 code implementation14 Dec 2023 Bo Xiong, Mojtaba Nayyeri, Linhao Luo, ZiHao Wang, Shirui Pan, Steffen Staab

NestE represents each atomic fact as a $1\times3$ matrix, and each nested relation is modeled as a $3\times3$ matrix that rotates the $1\times3$ atomic fact matrix through matrix multiplication.

Knowledge Graphs Link Prediction

Geometric Relational Embeddings: A Survey

no code implementations24 Apr 2023 Bo Xiong, Mojtaba Nayyeri, Ming Jin, Yunjie He, Michael Cochez, Shirui Pan, Steffen Staab

Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for structured/relational reasoning, typically in low dimensions.

Hierarchical Multi-label Classification Knowledge Graph Completion +1

Modeling Relational Patterns for Logical Query Answering over Knowledge Graphs

no code implementations21 Mar 2023 Yunjie He, Mojtaba Nayyeri, Bo Xiong, Evgeny Kharlamov, Steffen Staab

However, the role of such patterns in answering FOL queries by query embedding models has not been yet studied in the literature.

Inductive Bias Knowledge Graphs

Integrating Knowledge Graph embedding and pretrained Language Models in Hypercomplex Spaces

1 code implementation4 Aug 2022 Mojtaba Nayyeri, ZiHao Wang, Mst. Mahfuja Akter, Mirza Mohtashim Alam, Md Rashad Al Hasan Rony, Jens Lehmann, Steffen Staab

In our approach, we build on existing strong representations of single modalities and we use hypercomplex algebra to represent both, (i), single-modality embedding as well as, (ii), the interaction between different modalities and their complementary means of knowledge representation.

Knowledge Graph Embedding Knowledge Graphs +2

Ultrahyperbolic Knowledge Graph Embeddings

no code implementations1 Jun 2022 Bo Xiong, Shichao Zhu, Mojtaba Nayyeri, Chengjin Xu, Shirui Pan, Chuan Zhou, Steffen Staab

Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry due to its superior capability for representing hierarchies.

Knowledge Graph Embeddings

Geometric Algebra based Embeddings for Static and Temporal Knowledge Graph Completion

no code implementations18 Feb 2022 Chengjin Xu, Mojtaba Nayyeri, Yung-Yu Chen, Jens Lehmann

In this work, we strive to move beyond the complex or hypercomplex space for KGE and propose a novel geometric algebra based embedding approach, GeomE, which uses multivector representations and the geometric product to model entities and relations.

Knowledge Graph Embeddings Link Prediction +2

Multiple Run Ensemble Learning with Low-Dimensional Knowledge Graph Embeddings

no code implementations11 Apr 2021 Chengjin Xu, Mojtaba Nayyeri, Sahar Vahdati, Jens Lehmann

For example, instead of training a model one time with a large embedding size of 1200, we repeat the training of the model 6 times in parallel with an embedding size of 200 and then combine the 6 separate models for testing while the overall numbers of adjustable parameters are same (6*200=1200) and the total memory footprint remains the same.

Ensemble Learning Knowledge Graph Completion +3

TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation

2 code implementations COLING 2020 Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, Jens Lehmann

We show our proposed model overcomes the limitations of the existing KG embedding models and TKG embedding models and has the ability of learning and inferringvarious relation patterns over time.

Knowledge Graph Embedding Link Prediction +1

Knowledge Graph Embeddings in Geometric Algebras

no code implementations COLING 2020 Chengjin Xu, Mojtaba Nayyeri, Yung-Yu Chen, Jens Lehmann

Knowledge graph (KG) embedding aims at embedding entities and relations in a KG into a lowdimensional latent representation space.

Knowledge Graph Embeddings Knowledge Graphs +1

5* Knowledge Graph Embeddings with Projective Transformations

no code implementations8 Jun 2020 Mojtaba Nayyeri, Sahar Vahdati, Can Aykul, Jens Lehmann

Most of the embedding models designed in Euclidean geometry usually support a single transformation type - often translation or rotation, which is suitable for learning on graphs with small differences in neighboring subgraphs.

Knowledge Graph Embedding Knowledge Graph Embeddings +2

Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition

2 code implementations18 Nov 2019 Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, Jens Lehmann

Moreover, considering the temporal uncertainty during the evolution of entity/relation representations over time, we map the representations of temporal KGs into the space of multi-dimensional Gaussian distributions.

Knowledge Graph Completion Knowledge Graph Embedding +5

Adaptive Margin Ranking Loss for Knowledge Graph Embeddings via a Correntropy Objective Function

no code implementations9 Jul 2019 Mojtaba Nayyeri, Xiaotian Zhou, Sahar Vahdati, Hamed Shariat Yazdi, Jens Lehmann

To tackle this problem, several loss functions have been proposed recently by adding upper bounds and lower bounds to the scores of positive and negative samples.

Knowledge Graph Embeddings Knowledge Graphs +2

Soft Marginal TransE for Scholarly Knowledge Graph Completion

no code implementations27 Apr 2019 Mojtaba Nayyeri, Sahar Vahdati, Jens Lehmann, Hamed Shariat Yazdi

In this work, the TransE embedding model is reconciled for a specific link prediction task on scholarly metadata.

Link Prediction Question Answering

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