Embodying the Number of an Entity’s Relations for Knowledge Representation Learning

2021 ◽  
Vol 31 (10) ◽  
pp. 1495-1515
Author(s):  
Xinhua Suo ◽  
Bing Guo ◽  
Yan Shen ◽  
Wei Wang ◽  
Yaosen Chen ◽  
...  
Keyword(s):  
Knowledge Representation ◽  
Visual Information ◽  
Large Scale ◽  
Critical Role ◽  
Representation Learning ◽  
Knowledge Graph ◽  
Source Information ◽  
Structure Information ◽  
Common Information ◽  
Additional Information

Knowledge representation learning (knowledge graph embedding) plays a critical role in the application of knowledge graph construction. The multi-source information knowledge representation learning, which is one class of the most promising knowledge representation learning at present, mainly focuses on learning a large number of useful additional information of entities and relations in the knowledge graph into their embeddings, such as the text description information, entity type information, visual information, graph structure information, etc. However, there is a kind of simple but very common information — the number of an entity’s relations which means the number of an entity’s semantic types has been ignored. This work proposes a multi-source knowledge representation learning model KRL-NER, which embodies information of the number of an entity’s relations between entities into the entities’ embeddings through the attention mechanism. Specifically, first of all, we design and construct a submodel of the KRL-NER LearnNER which learns an embedding including the information on the number of an entity’s relations; then, we obtain a new embedding by exerting attention onto the embedding learned by the models such as TransE with this embedding; finally, we translate based onto the new embedding. Experiments, such as related tasks on knowledge graph: entity prediction, entity prediction under different relation types, and triple classification, are carried out to verify our model. The results show that our model is effective on the large-scale knowledge graphs, e.g. FB15K.

scholarly journals Image-embodied Knowledge Representation Learning

2017 ◽  
Author(s):  
Ruobing Xie ◽  
Zhiyuan Liu ◽  
Huanbo Luan ◽  
Maosong Sun
Keyword(s):  
Knowledge Representation ◽  
Visual Information ◽  
Representation Learning ◽  
Learning Model ◽  
Experimental Results ◽  
Knowledge Graph ◽  
Embodied Knowledge ◽  
Knowledge Representations ◽  
Conventional Methods ◽  
Image Representations

Entity images could provide significant visual information for knowledge representation learning. Most conventional methods learn knowledge representations merely from structured triples, ignoring rich visual information extracted from entity images. In this paper, we propose a novel Image-embodied Knowledge Representation Learning model (IKRL), where knowledge representations are learned with both triple facts and images. More specifically, we first construct representations for all images of an entity with a neural image encoder. These image representations are then integrated into an aggregated image-based representation via an attention-based method. We evaluate our IKRL models on knowledge graph completion and triple classification. Experimental results demonstrate that our models outperform all baselines on both tasks, which indicates the significance of visual information for knowledge representations and the capability of our models in learning knowledge representations with images.

scholarly journals Online Credit Payment Fraud Detection via Structure-Aware Hierarchical Recurrent Neural Network

2021 ◽  
Author(s):  
Wangli Lin ◽  
Li Sun ◽  
Qiwei Zhong ◽  
Can Liu ◽  
Jinghua Feng ◽  
...  
Keyword(s):  
Large Scale ◽  
Structural Information ◽  
Critical Role ◽  
Fraud Detection ◽  
Web Pages ◽  
Intrinsic Structure ◽  
Structure Information ◽  
Multi Scale ◽  
Sequential Information ◽  
Scale Behavior

Online credit payment fraud detection plays a critical role in financial institutions due to the growing volume of fraudulent transactions. Recently, researchers have shown an increased interest in capturing users’ dynamic and evolving fraudulent tendencies from their behavior sequences. However, most existing methodologies for sequential modeling overlook the intrinsic structure information of web pages. In this paper, we adopt multi-scale behavior sequence generated from different granularities of web page structures and propose a model named SAH-RNN to consume the multi-scale behavior sequence for online payment fraud detection. The SAH-RNN has stacked RNN layers in which upper layers modeling for compendious behaviors are updated less frequently and receive the summarized representations from lower layers. A dual attention is devised to capture the impacts on both sequential information within the same sequence and structural information among different granularity of web pages. Experimental results on a large-scale real-world transaction dataset from Alibaba show that our proposed model outperforms state-of-the-art models. The code is available at https://github.com/WangliLin/SAH-RNN.

scholarly journals Software Knowledge Entity Relation Extraction with Entity-Aware and Syntactic Dependency Structure Information

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Mingjing Tang ◽  
Tong Li ◽  
Wei Wang ◽  
Rui Zhu ◽  
Zifei Ma ◽  
...  
Keyword(s):  
Large Scale ◽  
Complex Structure ◽  
Relation Extraction ◽  
Knowledge Graph ◽  
Knowledge Domain ◽  
Convolutional Network ◽  
Knowledge Community ◽  
Structure Information ◽  
Entity Relation Extraction ◽  
Syntactic Dependency

Software knowledge community contains a large scale of software knowledge entities with complex structure and rich semantic relations. Semantic relation extraction of software knowledge entities is a critical task for software knowledge graph construction, which has an important impact on knowledge graph based tasks such as software document generation and software expert recommendation. Due to the problems of entity sparsity, relation ambiguity, and the lack of annotated dataset in user-generated content of software knowledge community, it is difficult to apply existing methods of relation extraction in the software knowledge domain. To address these issues, we propose a novel software knowledge entity relation extraction model which incorporates entity-aware information with syntactic dependency information. Bidirectional Gated Recurrent Unit (Bi-GRU) and Graph Convolutional Networks (GCN) are used to learn the features of contextual semantic representation and syntactic dependency representation, respectively. To obtain more syntactic dependency information, a weight graph convolutional network based on Newton’s cooling law is constructed by calculating a weight adjacency matrix. Specifically, an entity-aware attention mechanism is proposed to integrate the entity information and syntactic dependency information to improve the prediction performance of the model. Experiments are conducted on a dataset which is constructed based on texts of the StackOverflow and show that the proposed model has better performance than the benchmark models.

scholarly journals Knowledge Graph Representation Fusion Framework for Fine-Grained Object Recognition in Smart Cities

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yang He ◽  
Ling Tian ◽  
Lizong Zhang ◽  
Xi Zeng
Keyword(s):  
Knowledge Representation ◽  
Image Classification ◽  
Visual Information ◽  
Smart Cities ◽  
Image Features ◽  
Graph Representation ◽  
Knowledge Graph ◽  
Fine Grained ◽  
High Level ◽  
Traditional Approaches

Autonomous object detection powered by cutting-edge artificial intelligent techniques has been an essential component for sustaining complex smart city systems. Fine-grained image classification focuses on recognizing subcategories of specific levels of images. As a result of the high similarity between images in the same category and the high dissimilarity in the same subcategories, it has always been a challenging problem in computer vision. Traditional approaches usually rely on exploring only the visual information in images. Therefore, this paper proposes a novel Knowledge Graph Representation Fusion (KGRF) framework to introduce prior knowledge into fine-grained image classification task. Specifically, the Graph Attention Network (GAT) is employed to learn the knowledge representation from the constructed knowledge graph modeling the categories-subcategories and subcategories-attributes associations. By introducing the Multimodal Compact Bilinear (MCB) module, the framework can fully integrate the knowledge representation and visual features for learning the high-level image features. Extensive experiments on the Caltech-UCSD Birds-200-2011 dataset verify the superiority of our proposed framework over several existing state-of-the-art methods.

scholarly journals TransPath: Representation Learning for Heterogeneous Information Networks via Translation Mechanism

2018 ◽  
Author(s):  
Yang Fang ◽  
Xiang Zhao ◽  
Zhen Tan
Keyword(s):  
Large Scale ◽  
Representation Learning ◽  
Information Networks ◽  
Heterogeneous Information ◽  
Structure Information ◽  
Heterogeneous Information Networks ◽  
Network Representation ◽  
Meta Path ◽  
Translation Mechanism ◽  
Real World Datasets

In this paper, we propose a novel network representation learning model TransPath to encode heterogeneous information networks (HINs). Traditional network representation learning models aim to learn the embeddings of a homogeneous network. TransPath is able to capture the rich semantic and structure information of a HIN via meta-paths. We take advantage of the concept of translation mechanism in knowledge graph which regards a meta-path, instead of an edge, as a translating operation from the first node to the last node. Moreover, we propose a user-guided meta-path sampling strategy which takes users' preference as a guidance, which could explore the semantics of a path more precisely, and meanwhile improve model efficiency via the avoidance of other noisy and meaningless meta-paths. We evaluate our model on two large-scale real-world datasets DBLP and YELP, and two benchmark tasks similarity search and node classification. We observe that TransPath outperforms other state-of-the-art baselines consistently and significantly.

scholarly journals A Survey on Knowledge Graph Embedding: Approaches, Applications and Benchmarks

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 750
Author(s):  
Yuanfei Dai ◽  
Shiping Wang ◽  
Neal N. Xiong ◽  
Wenzhong Guo
Keyword(s):  
Large Scale ◽  
Question Answering ◽  
Graph Embedding ◽  
Feature Space ◽  
Future Research ◽  
Knowledge Graph ◽  
Yield Model ◽  
Additional Information ◽  
Advantages And Disadvantages ◽  
Issue Knowledge

A knowledge graph (KG), also known as a knowledge base, is a particular kind of network structure in which the node indicates entity and the edge represent relation. However, with the explosion of network volume, the problem of data sparsity that causes large-scale KG systems to calculate and manage difficultly has become more significant. For alleviating the issue, knowledge graph embedding is proposed to embed entities and relations in a KG to a low-, dense and continuous feature space, and endow the yield model with abilities of knowledge inference and fusion. In recent years, many researchers have poured much attention in this approach, and we will systematically introduce the existing state-of-the-art approaches and a variety of applications that benefit from these methods in this paper. In addition, we discuss future prospects for the development of techniques and application trends. Specifically, we first introduce the embedding models that only leverage the information of observed triplets in the KG. We illustrate the overall framework and specific idea and compare the advantages and disadvantages of such approaches. Next, we introduce the advanced models that utilize additional semantic information to improve the performance of the original methods. We divide the additional information into two categories, including textual descriptions and relation paths. The extension approaches in each category are described, following the same classification criteria as those defined for the triplet fact-based models. We then describe two experiments for comparing the performance of listed methods and mention some broader domain tasks such as question answering, recommender systems, and so forth. Finally, we collect several hurdles that need to be overcome and provide a few future research directions for knowledge graph embedding.

scholarly journals A Novel Encoder-Decoder Knowledge Graph Completion Model for Robot Brain

2021 ◽  
Vol 15 ◽  
Author(s):  
Yichen Song ◽  
Aiping Li ◽  
Hongkui Tu ◽  
Kai Chen ◽  
Chenchen Li
Keyword(s):  
Link Prediction ◽  
Large Scale ◽  
Rapid Development ◽  
Representation Learning ◽  
High Tech ◽  
Knowledge Graph ◽  
World Knowledge ◽  
Human Beings ◽  
Benchmark Datasets ◽  
The Brain

With the rapid development of artificial intelligence, Cybernetics, and other High-tech subject technology, robots have been made and used in increasing fields. And studies on robots have attracted growing research interests from different communities. The knowledge graph can act as the brain of a robot and provide intelligence, to support the interaction between the robot and the human beings. Although the large-scale knowledge graphs contain a large amount of information, they are still incomplete compared with real-world knowledge. Most existing methods for knowledge graph completion focus on entity representation learning. However, the importance of relation representation learning is ignored, as well as the cross-interaction between entities and relations. In this paper, we propose an encoder-decoder model which embeds the interaction between entities and relations, and adds a gate mechanism to control the attention mechanism. Experimental results show that our method achieves better link prediction performance than state-of-the-art embedding models on two benchmark datasets, WN18RR and FB15k-237.

scholarly journals Rule-Guided Compositional Representation Learning on Knowledge Graphs with Hierarchical Types

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1978
Author(s):  
Yanying Mao ◽  
Honghui Chen
Keyword(s):  
Knowledge Representation ◽  
Representation Learning ◽  
Knowledge Graph ◽  
Path Information ◽  
Different Types ◽  
Rich Information ◽  
The Rich ◽  
Horn Rules ◽  
Low Dimensional ◽  
Type Information

The representation learning of the knowledge graph projects the entities and relationships in the triples into a low-dimensional continuous vector space. Early representation learning mostly focused on the information contained in the triplet itself but ignored other useful information. Since entities have different types of representations in different scenarios, the rich information in the types of entity levels is helpful for obtaining a more complete knowledge representation. In this paper, a new knowledge representation frame (TRKRL) combining rule path information and entity hierarchical type information is proposed to exploit interpretability of logical rules and the advantages of entity hierarchical types. Specifically, for entity hierarchical type information, we consider that entities have multiple representations of different types, as well as treat it as the projection matrix of entities, using the type encoder to model entity hierarchical types. For rule path information, we mine Horn rules from the knowledge graph to guide the synthesis of relations in paths. Experimental results show that TRKRL outperforms baselines on the knowledge graph completion task, which indicates that our model is capable of using entity hierarchical type information, relation paths information, and logic rules information for representation learning.

scholarly journals Representation Learning for Scene Graph Completion via Jointly Structural and Visual Embedding

2018 ◽  
Author(s):  
Hai Wan ◽  
Yonghao Luo ◽  
Bo Peng ◽  
Wei-Shi Zheng
Keyword(s):  
Case Studies ◽  
Visual Information ◽  
Link Prediction ◽  
Representation Learning ◽  
Experimental Results ◽  
Knowledge Graph ◽  
Scene Graph ◽  
Bounding Box ◽  
End To End

This paper focuses on scene graph completion which aims at predicting new relations between two entities utilizing existing scene graphs and images. By comparing with the well-known knowledge graph, we first identify that each scene graph is associated with an image and each entity of a visual triple in a scene graph is composed of its entity type with attributes and grounded with a bounding box in its corresponding image. We then propose an end-to-end model named Representation Learning via Jointly Structural and Visual Embedding (RLSV) to take advantages of structural and visual information in scene graphs. In RLSV model, we provide a fully-convolutional module to extract the visual embeddings of a visual triple and apply hierarchical projection to combine the structural and visual embeddings of a visual triple. In experiments, we evaluate our model on two scene graph completion tasks: link prediction and visual triple classification, and further analyze by case studies. Experimental results demonstrate that our model outperforms all baselines in both tasks, which justifies the significance of combining structural and visual information for scene graph completion.

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