DSKRL:A Dissimilarity-Support-aware Knowledge Representation Learning Framework on Noisy Knowledge Graph

2021 ◽  
Author(s):  
Tianyang Shao ◽  
Xinyi Li ◽  
Xiang Zhao ◽  
Hao Xu ◽  
Weidong Xiao
Keyword(s):  
Knowledge Representation ◽  
Representation Learning ◽  
Knowledge Graph ◽  
Learning Framework

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.

LkeRec: Toward Lightweight End-to-End Joint Representation Learning for Building Accurate and Effective Recommendation

2022 ◽  
Vol 40 (3) ◽  
pp. 1-28
Author(s):  
Surong Yan ◽  
Kwei-Jay Lin ◽  
Xiaolin Zheng ◽  
Haosen Wang
Keyword(s):  
Side Information ◽  
Dimensional Space ◽  
Representation Learning ◽  
Knowledge Graph ◽  
Learning Framework ◽  
Relation Type ◽  
End To End ◽  
Low Dimensional ◽  
Item Knowledge ◽  
Explicit And Implicit Knowledge

Explicit and implicit knowledge about users and items have been used to describe complex and heterogeneous side information for recommender systems (RSs). Many existing methods use knowledge graph embedding (KGE) to learn the representation of a user-item knowledge graph (KG) in low-dimensional space. In this article, we propose a lightweight end-to-end joint learning framework for fusing the tasks of KGE and RSs at the model level. Our method proposes a lightweight KG embedding method by using bidirectional bijection relation-type modeling to enable scalability for large graphs while using self-adaptive negative sampling to optimize negative sample generating. Our method further generates the integrated views for users and items based on relation-types to explicitly model users’ preferences and items’ features, respectively. Finally, we add virtual “recommendation” relations between the integrated views of users and items to model the preferences of users on items, seamlessly integrating RS with user-item KG over a unified graph. Experimental results on multiple datasets and benchmarks show that our method can achieve a better accuracy of recommendation compared with existing state-of-the-art methods. Complexity and runtime analysis suggests that our method can gain a lower time and space complexity than most of existing methods and improve scalability.

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 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 Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition

2018 ◽  
Author(s):  
Tianshui Chen ◽  
Liang Lin ◽  
Riquan Chen ◽  
Yang Wu ◽  
Xiaonan Luo
Keyword(s):  
Neural Network ◽  
Knowledge Representation ◽  
Image Recognition ◽  
Feature Learning ◽  
Representation Learning ◽  
Feature Representation ◽  
Image Feature ◽  
Knowledge Graph ◽  
Feature Maps ◽  
Fine Grained

Humans can naturally understand an image in depth with the aid of rich knowledge accumulated from daily lives or professions. For example, to achieve fine-grained image recognition (e.g., categorizing hundreds of subordinate categories of birds) usually requires a comprehensive visual concept organization including category labels and part-level attributes. In this work, we investigate how to unify rich professional knowledge with deep neural network architectures and propose a Knowledge-Embedded Representation Learning (KERL) framework for handling the problem of fine-grained image recognition. Specifically, we organize the rich visual concepts in the form of knowledge graph and employ a Gated Graph Neural Network to propagate node message through the graph for generating the knowledge representation. By introducing a novel gated mechanism, our KERL framework incorporates this knowledge representation into the discriminative image feature learning, i.e., implicitly associating the specific attributes with the feature maps. Compared with existing methods of fine-grained image classification, our KERL framework has several appealing properties: i) The embedded high-level knowledge enhances the feature representation, thus facilitating distinguishing the subtle differences among subordinate categories. ii) Our framework can learn feature maps with a meaningful configuration that the highlighted regions finely accord with the nodes (specific attributes) of the knowledge graph. Extensive experiments on the widely used Caltech-UCSD bird dataset demonstrate the superiority of our KERL framework over existing state-of-the-art methods.

scholarly journals Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion

2021 ◽  
Author(s):  
Bo Wang ◽  
Tao Shen ◽  
Guodong Long ◽  
Tianyi Zhou ◽  
Ying Wang ◽  
...  
Keyword(s):  
Representation Learning ◽  
Knowledge Graph ◽  
Text Representation

W-MMP2Vec: Topic-driven network embedding model for link prediction in content-based heterogeneous information network

2021 ◽  
Vol 25 (3) ◽  
pp. 711-738
Author(s):  
Phu Pham ◽  
Phuc Do
Keyword(s):  
Link Prediction ◽  
Representation Learning ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Learning Framework ◽  
Novel Approach ◽  
Proposed Model ◽  
Meta Path

Link prediction on heterogeneous information network (HIN) is considered as a challenge problem due to the complexity and diversity in types of nodes and links. Currently, there are remained challenges of meta-path-based link prediction in HIN. Previous works of link prediction in HIN via network embedding approach are mainly focused on exploiting features of node rather than existing relations in forms of meta-paths between nodes. In fact, predicting the existence of new links between non-linked nodes is absolutely inconvincible. Moreover, recent HIN-based embedding models also lack of thorough evaluations on the topic similarity between text-based nodes along given meta-paths. To tackle these challenges, in this paper, we proposed a novel approach of topic-driven multiple meta-path-based HIN representation learning framework, namely W-MMP2Vec. Our model leverages the quality of node representations by combining multiple meta-paths as well as calculating the topic similarity weight for each meta-path during the processes of network embedding learning in content-based HINs. To validate our approach, we apply W-TMP2Vec model in solving several link prediction tasks in both content-based and non-content-based HINs (DBLP, IMDB and BlogCatalog). The experimental outputs demonstrate the effectiveness of proposed model which outperforms recent state-of-the-art HIN representation learning models.

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