Search Results for author:
Found 7 papers, 5 papers with code
MEIM: Multi-partition Embedding Interaction Beyond Block Term Format for Efficient and Expressive Link Prediction
Knowledge graph embedding aims to predict the missing relations between entities in knowledge graphs.
Ranked #1 on
Link Prediction
on YAGO3-10
Multi-Relational Embedding for Knowledge Graph Representation and Analysis
The goal of this thesis is first to study multi-relational embedding on knowledge graphs to propose a new embedding model that explains and improves previous methods, then to study the applications of multi-relational embedding in representation and analysis of knowledge graphs.
Ranked #2 on
Link Prediction
on KG20C
Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion
Knowledge graph embedding methods perform this task by representing entities and relations as embedding vectors and modeling their interactions to compute the matching score of each triple.
Ranked #1 on
Link Prediction
on KG20C
Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space
The knowledge graph can be modeled by knowledge graph embedding methods, which represent entities and relations as embedding vectors in semantic space, then model the interactions between these embedding vectors.
Analyzing Knowledge Graph Embedding Methods from a Multi-Embedding Interaction Perspective
In this paper, we propose a multi-embedding interaction mechanism as a new approach to uniting and generalizing these models.
Ranked #17 on
Link Prediction
on WN18
SciRecSys: A Recommendation System for Scientific Publication by Discovering Keyword Relationships
In this work, we propose a new approach for discovering various relationships among keywords over the scientific publications based on a Markov Chain model.
Author Name Disambiguation by Using Deep Neural Network
In this research, we evaluate the proposed method on a dataset containing Vietnamese author names.
