Survey on Embedding Models for Knowledge Graph and its Applications

• URL: http://arxiv.org/abs/2404.09167v1
• Date: Sun, 14 Apr 2024 07:15:59 GMT
• Title: Survey on Embedding Models for Knowledge Graph and its Applications
• Authors: Manita Pote,
• Abstract summary: Knowledge Graph (KG) is a graph based data structure to represent facts of the world where nodes represent real world entities or abstract concept and edges represent relation between the entities. Knowledge Graph embedding tackles the drawback by representing entities and relation in low dimensional vector space by capturing the semantic relation between them. Here, we discuss translation based and neural network based embedding models which differ based on semantic property, scoring function and architecture they use.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Knowledge Graph (KG) is a graph based data structure to represent facts of the world where nodes represent real world entities or abstract concept and edges represent relation between the entities. Graph as representation for knowledge has several drawbacks like data sparsity, computational complexity and manual feature engineering. Knowledge Graph embedding tackles the drawback by representing entities and relation in low dimensional vector space by capturing the semantic relation between them. There are different KG embedding models. Here, we discuss translation based and neural network based embedding models which differ based on semantic property, scoring function and architecture they use. Further, we discuss application of KG in some domains that use deep learning models and leverage social media data.

Related papers

• A Study on Knowledge Graph Embeddings and Graph Neural Networks for Web Of Things [0.0]
  In the future, Orange's take on a knowledge graph in the domain of the Web Of Things (WoT) is to provide a digital representation of the physical world. In this paper, we explore state-of-the-art knowledge graph embedding (KGE) methods to learn numerical representations of the graph entities. We also investigate Graph neural networks (GNN) alongside KGEs and compare their performance on the same downstream tasks.
  arXiv  Detail & Related papers  (2023-10-23T12:36:33Z)
• Connector 0.5: A unified framework for graph representation learning [5.398580049917152]
  We introduce a novel graph representation framework covering various graph embedding models, ranging from shallow to state-of-the-art models. We plan to build an efficient open-source framework that can provide deep graph embedding models to represent structural relations in graphs.
  arXiv  Detail & Related papers  (2023-04-25T23:28:38Z)
• Probing Graph Representations [77.7361299039905]
  We use a probing framework to quantify the amount of meaningful information captured in graph representations. Our findings on molecular datasets show the potential of probing for understanding the inductive biases of graph-based models. We advocate for probing as a useful diagnostic tool for evaluating graph-based models.
  arXiv  Detail & Related papers  (2023-03-07T14:58:18Z)
• I Know What You Do Not Know: Knowledge Graph Embedding via Co-distillation Learning [16.723470319188102]
  Knowledge graph embedding seeks to learn vector representations for entities and relations. Recent studies have used pre-trained language models to learn embeddings based on the textual information of entities and relations. We propose CoLE, a Co-distillation Learning method for KG Embedding that exploits the complement of graph structures and text information.
  arXiv  Detail & Related papers  (2022-08-21T07:34:37Z)
• Is There More Pattern in Knowledge Graph? Exploring Proximity Pattern for Knowledge Graph Embedding [13.17623081024394]
  We name such semantic phenomenon in knowledge graph as proximity pattern. With the original knowledge graph, we design a Chained couPle-GNN architecture to deeply merge the two patterns. Being evaluated on FB15k-237 and WN18RR datasets, CP-GNN achieves state-of-the-art results for Knowledge Graph Completion task.
  arXiv  Detail & Related papers  (2021-10-02T03:50:42Z)
• GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph [53.70520466556453]
  We propose GraphFormers, where layerwise GNN components are nested alongside the transformer blocks of language models. With the proposed architecture, the text encoding and the graph aggregation are fused into an iterative workflow. In addition, a progressive learning strategy is introduced, where the model is successively trained on manipulated data and original data to reinforce its capability of integrating information on graph.
  arXiv  Detail & Related papers  (2021-05-06T12:20:41Z)
• Learning Intents behind Interactions with Knowledge Graph for Recommendation [93.08709357435991]
  Knowledge graph (KG) plays an increasingly important role in recommender systems. Existing GNN-based models fail to identify user-item relation at a fine-grained level of intents. We propose a new model, Knowledge Graph-based Intent Network (KGIN)
  arXiv  Detail & Related papers  (2021-02-14T03:21:36Z)
• A Heterogeneous Graph with Factual, Temporal and Logical Knowledge for Question Answering Over Dynamic Contexts [81.4757750425247]
  We study question answering over a dynamic textual environment. We develop a graph neural network over the constructed graph, and train the model in an end-to-end manner.
  arXiv  Detail & Related papers  (2020-04-25T04:53:54Z)
• Tensor Graph Convolutional Networks for Multi-relational and Robust Learning [74.05478502080658]
  This paper introduces a tensor-graph convolutional network (TGCN) for scalable semi-supervised learning (SSL) from data associated with a collection of graphs, that are represented by a tensor. The proposed architecture achieves markedly improved performance relative to standard GCNs, copes with state-of-the-art adversarial attacks, and leads to remarkable SSL performance over protein-to-protein interaction networks.
  arXiv  Detail & Related papers  (2020-03-15T02:33:21Z)
• Bridging Knowledge Graphs to Generate Scene Graphs [49.69377653925448]
  We propose a novel graph-based neural network that iteratively propagates information between the two graphs, as well as within each of them. Our Graph Bridging Network, GB-Net, successively infers edges and nodes, allowing to simultaneously exploit and refine the rich, heterogeneous structure of the interconnected scene and commonsense graphs.
  arXiv  Detail & Related papers  (2020-01-07T23:35:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.