scholarly journals A Knowledge-Aware Attentional Reasoning Network for Recommendation

2020 ◽  
Vol 34 (04) ◽  
pp. 6999-7006 ◽  
Author(s):  
Qiannan Zhu ◽  
Xiaofei Zhou ◽  
Jia Wu ◽  
Jianlong Tan ◽  
Li Guo
Keyword(s):  
Neural Network ◽  
Recommendation Systems ◽  
Experimental Results ◽  
Potential User ◽  
Knowledge Graph ◽  
Knowledge Graphs ◽  
Candidate Item

Knowledge-graph-aware recommendation systems have increasingly attracted attention in both industry and academic recently. Many existing knowledge-aware recommendation methods have achieved better performance, which usually perform recommendation by reasoning on the paths between users and items in knowledge graphs. However, they ignore the users' personal clicked history sequences that can better reflect users' preferences within a period of time for recommendation. In this paper, we propose a knowledge-aware attentional reasoning network KARN that incorporates the users' clicked history sequences and path connectivity between users and items for recommendation. The proposed KARN not only develops an attention-based RNN to capture the user's history interests from the user's clicked history sequences, but also a hierarchical attentional neural network to reason on paths between users and items for inferring the potential user intents on items. Based on both user's history interest and potential intent, KARN can predict the clicking probability of the user with respective to a candidate item. We conduct experiment on Amazon review dataset, and the experimental results demonstrate the superiority and effectiveness of our proposed KARN model.

scholarly journals Research of Personalized Recommendation Technology Based on Knowledge Graphs

2021 ◽  
Vol 11 (15) ◽  
pp. 7104
Author(s):  
Xu Yang ◽  
Ziyi Huan ◽  
Yisong Zhai ◽  
Ting Lin
Keyword(s):  
Neural Network ◽  
Hot Spot ◽  
Experimental Results ◽  
Graph Representation ◽  
Personalized Recommendation ◽  
Knowledge Graph ◽  
Learning Technology ◽  
Data Set ◽  
Model Based ◽  
Knowledge Graphs

Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs’ construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algorithm through a cross-training method by using the information of the neighboring feature structures of the entities in the knowledge graph. Furthermore, the negative sampling process of TransE algorithm is improved. The experimental results show that the improved TransE model can more accurately vectorize entities and relations. Finally, this paper constructs a recommendation model by combining knowledge graphs with ranking learning and neural network. We propose the Bayesian personalized recommendation model based on knowledge graphs (KG-BPR) and the neural network recommendation model based on knowledge graphs(KG-NN). The semantic information of entities and relations in knowledge graphs is embedded into vector space by using improved TransE method, and we compare the results. The item entity vectors containing external knowledge information are integrated into the BPR model and neural network, respectively, which make up for the lack of knowledge information of the item itself. Finally, the experimental analysis is carried out on MovieLens-1M data set. The experimental results show that the two recommendation models proposed in this paper can effectively improve the accuracy, recall, F1 value and MAP value of recommendation.

scholarly journals MOVIE RECOMMENDATION USING KNOWLEDGE GRAPH

2020 ◽  
pp. 99-103
Author(s):  
Navin Tatyaba Gopal ◽  
Anish Raj Khobragade
Keyword(s):  
Neural Network ◽  
Recommender Systems ◽  
Weighted Graph ◽  
Cold Start ◽  
Structured Data ◽  
Label Propagation ◽  
Knowledge Graph ◽  
Movie Recommendation ◽  
Knowledge Graphs ◽  
Prior Application

The Knowledge graphs (KGs) catches structured data and relationships among a bunch of entities and items. Generally, constitute an attractive origin of information that can advance the recommender systems. But, present methodologies of this area depend on manual element thus don’t permit for start to end training. This article proposes, Knowledge Graph along with Label Smoothness (KG-LS) to offer better suggestions for the recommender Systems. Our methodology processes user-specific entities by prior application of a function capability that recognizes key KG-relationships for a specific user. In this manner, we change the KG in a specific-user weighted graph followed by application of a graph neural network to process customized entity embedding. To give better preliminary predisposition, label smoothness comes into picture, which places items in the KG which probably going to have identical user significant names/scores. Use of, label smoothness gives regularization above the edge weights thus; we demonstrate that it is comparable to a label propagation plan on the graph. Additionally building-up a productive usage that symbolizes solid adaptability concerning the size of knowledge graph. Experimentation on 4 datasets shows that our strategy beats best in class baselines. This process likewise accomplishes solid execution in cold start situations where user-entity communications remain meager.

scholarly journals Relational Graph Neural Network with Hierarchical Attention for Knowledge Graph Completion

2020 ◽  
Vol 34 (05) ◽  
pp. 9612-9619
Author(s):  
Zhao Zhang ◽  
Fuzhen Zhuang ◽  
Hengshu Zhu ◽  
Zhiping Shi ◽  
Hui Xiong ◽  
...  
Keyword(s):  
Neural Network ◽  
Missing Values ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Knowledge Graph ◽  
Incomplete Knowledge ◽  
Neighborhood Information ◽  
Local Neighborhood ◽  
Proposed Model ◽  
Knowledge Graphs

The rapid proliferation of knowledge graphs (KGs) has changed the paradigm for various AI-related applications. Despite their large sizes, modern KGs are far from complete and comprehensive. This has motivated the research in knowledge graph completion (KGC), which aims to infer missing values in incomplete knowledge triples. However, most existing KGC models treat the triples in KGs independently without leveraging the inherent and valuable information from the local neighborhood surrounding an entity. To this end, we propose a Relational Graph neural network with Hierarchical ATtention (RGHAT) for the KGC task. The proposed model is equipped with a two-level attention mechanism: (i) the first level is the relation-level attention, which is inspired by the intuition that different relations have different weights for indicating an entity; (ii) the second level is the entity-level attention, which enables our model to highlight the importance of different neighboring entities under the same relation. The hierarchical attention mechanism makes our model more effective to utilize the neighborhood information of an entity. Finally, we extensively validate the superiority of RGHAT against various state-of-the-art baselines.

scholarly journals Metastatic Site Prediction in Breast Cancer using Omics Knowledge Graph and Pattern Mining with Kirchhoff’s Law Traversal

2020 ◽  
Author(s):  
Alokkumar Jha ◽  
Yasar Khan ◽  
Ratnesh Sahay ◽  
Mathieu d’Aquin
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Pattern Mining ◽  
Knowledge Graph ◽  
Omics Data ◽  
Metastatic Site ◽  
Decision Network ◽  
Knowledge Graphs ◽  
Metastatic Sites ◽  
Kirchhoff's Law

AbstractPrediction of metastatic sites from the primary site of origin is a impugn task in breast cancer (BRCA). Multi-dimensionality of such metastatic sites - bone, lung, kidney, and brain, using large-scale multi-dimensional Poly-Omics (Transcriptomics, Proteomics and Metabolomics) data of various type, for example, CNV (Copy number variation), GE (Gene expression), DNA methylation, path-ways, and drugs with clinical associations makes classification of metastasis a multi-faceted challenge. In this paper, we have approached the above problem in three steps; 1) Applied Linked data and semantic web to build Poly-Omics data as knowledge graphs and termed them as cancer decision network; 2) Reduced the dimensionality of data using Graph Pattern Mining and explained gene rewiring in cancer decision network by first time using Kirchhoff’s law for knowledge or any graph traversal; 3) Established ruled based modeling to understand the essential -Omics data from poly-Omics for breast cancer progression 4) Predicted the disease’s metastatic site using Kirchhoff’s knowledge graphs as a hidden layer in the graph convolution neural network(GCNN). The features (genes) extracted by applying Kirchhoff’s law on knowledge graphs are used to predict disease relapse site with 91.9% AUC (Area Under Curve) and performed detailed evaluation against the state-of-the-art approaches. The novelty of our approach is in the creation of RDF knowledge graphs from the poly-omics, such as the drug, disease, target(gene/protein), pathways and application of Kirchhoff’s law on knowledge graph to and the first approach to predict metastatic site from the primary tumor. Further, we have applied the rule-based knowledge graph using graph convolution neural network for metastasis site prediction makes the even classification novel.

scholarly journals Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment

2018 ◽  
Author(s):  
Muhao Chen ◽  
Yingtao Tian ◽  
Kai-Wei Chang ◽  
Steven Skiena ◽  
Carlo Zaniolo
Keyword(s):  
Experimental Results ◽  
Knowledge Graph ◽  
Semantic Representations ◽  
Alignment Task ◽  
Structured Knowledge ◽  
Knowledge Graphs ◽  
Cross Lingual ◽  
Low Coverage

Multilingual knowledge graph (KG) embeddings provide latent semantic representations of entities and structured knowledge with cross-lingual inferences, which benefit various knowledge-driven cross-lingual NLP tasks. However, precisely learning such cross-lingual inferences is usually hindered by the low coverage of entity alignment in many KGs. Since many multilingual KGs also provide literal descriptions of entities, in this paper, we introduce an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions. Our approach performs co-training of two embedding models, i.e. a multilingual KG embedding model and a multilingual literal description embedding model. The models are trained on a large Wikipedia-based trilingual dataset where most entity alignment is unknown to training. Experimental results show that the performance of the proposed approach on the entity alignment task improves at each iteration of co-training, and eventually reaches a stage at which it significantly surpasses previous approaches. We also show that our approach has promising abilities for zero-shot entity alignment, and cross-lingual KG completion.

scholarly journals Multipointer Coattention Recommendation with Gated Neural Fusion between ID Embedding and Reviews

2022 ◽  
Vol 12 (2) ◽  
pp. 594
Author(s):  
Jianjie Shao ◽  
Jiwei Qin ◽  
Wei Zeng ◽  
Jiong Zheng
Keyword(s):  
Neural Network ◽  
Recommendation Systems ◽  
Experimental Results ◽  
Interaction Information ◽  
Word Level ◽  
Learning Scheme ◽  
Proposed Model ◽  
Sparsity Problem

Recently, the interaction information from reviews has been modeled to acquire representations between users and items and improve the sparsity problem in recommendation systems. Reviews are more responsive to information about users’ preferences for the different aspects and attributes of items. However, how to better construct the representation of users (items) still needs further research. Inspired by the interaction information from reviews, auxiliary ID embedding information is used to further enrich the word-level representation in the proposed model named MPCAR. In this paper, first, a multipointer learning scheme is adopted to extract the most informative reviews from user and item reviews and represent users (items) in a word-by-word manner. Then, users and items are embedded to extract the ID embedding that can reveal the identity of users (items). Finally, the review features and ID embedding are input to the gated neural network for effective fusion to obtain richer representations of users and items. We randomly select ten subcategory datasets from the Amazon dataset to evaluate our algorithm. The experimental results show that our algorithm can achieve the best results compared to other recommendation approaches.

scholarly journals Neighborhood Aggregation Collaborative Filtering Based on Knowledge Graph

2020 ◽  
Vol 10 (11) ◽  
pp. 3818 ◽  
Author(s):  
Dehai Zhang ◽  
Linan Liu ◽  
Qi Wei ◽  
Yun Yang ◽  
Po Yang ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Recommendation Systems ◽  
Knowledge Graph ◽  
Complementary Information ◽  
Potential Interest ◽  
Knowledge Graphs ◽  
Public Datasets ◽  
Click Through Rate

In recent years, the research of combining a knowledge graph with recommendation systems has caused widespread concern. By studying the interconnections in knowledge graphs, potential connections between users and items can be discovered, which provides abundant and complementary information for recommendation of items. However, most existing studies have not effectively established the relation between entities and users. Therefore, the recommendation results may be affected by some unrelated entities. In this paper, we propose a neighborhood aggregation collaborative filtering (NACF) based on knowledge graph. It uses the knowledge graph to spread and extract the user’s potential interest, and iteratively injects them into the user features with attentional deviation. We conducted a large number of experiments on three public datasets; we verifyied that NACF is ahead of the most advanced models in top-k recommendation and click-through rate (CTR) prediction.

scholarly journals I Know the Relationships: Zero-Shot Action Recognition via Two-Stream Graph Convolutional Networks and Knowledge Graphs

2019 ◽  
Vol 33 ◽  
pp. 8303-8311 ◽  
Author(s):  
Junyu Gao ◽  
Tianzhu Zhang ◽  
Changsheng Xu
Keyword(s):  
Action Recognition ◽  
Visual Cues ◽  
Feature Space ◽  
Experimental Results ◽  
Knowledge Graph ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Semantic Embedding ◽  
Structured Knowledge ◽  
Knowledge Graphs

Recently, with the ever-growing action categories, zero-shot action recognition (ZSAR) has been achieved by automatically mining the underlying concepts (e.g., actions, attributes) in videos. However, most existing methods only exploit the visual cues of these concepts but ignore external knowledge information for modeling explicit relationships between them. In fact, humans have remarkable ability to transfer knowledge learned from familiar classes to recognize unfamiliar classes. To narrow the knowledge gap between existing methods and humans, we propose an end-to-end ZSAR framework based on a structured knowledge graph, which can jointly model the relationships between action-attribute, action-action, and attribute-attribute. To effectively leverage the knowledge graph, we design a novel Two-Stream Graph Convolutional Network (TS-GCN) consisting of a classifier branch and an instance branch. Specifically, the classifier branch takes the semantic-embedding vectors of all the concepts as input, then generates the classifiers for action categories. The instance branch maps the attribute embeddings and scores of each video instance into an attribute-feature space. Finally, the generated classifiers are evaluated on the attribute features of each video, and a classification loss is adopted for optimizing the whole network. In addition, a self-attention module is utilized to model the temporal information of videos. Extensive experimental results on three realistic action benchmarks Olympic Sports, HMDB51 and UCF101 demonstrate the favorable performance of our proposed framework.

scholarly journals Solicitation of Knowledge Graph Enhanced Neural Network Objects Detection by Sentiment Analysis

2021 ◽  
Vol 04 (11) ◽  
Author(s):  
Yacouba Conde ◽  
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Knowledge Graph ◽  
Machine Learning Technique ◽  
Negative Attitudes ◽  
Learning Technique ◽  
Structured Knowledge ◽  
Knowledge Graphs ◽  
N Gram ◽  
Objects Detection

In the machine learning technique, the knowledge graph is advancing swiftly; however, the basic models are not able to grasp all the affluence of the script that comes from the different personal web graphics, social media, ads, and diaries, etc., ignoring the semantic of the basic text identification. The knowledge graph provides a real way to extract structured knowledge from the texts and desire images of neural network, to expedite their semantics examination. In this study, we propose a new hybrid analytic approach for sentiment evaluation based on knowledge graphs, to identify the polarity of sentiment with positive and negative attitudes in short documents, particularly in 4 chirps. We used the tweets graphs, then the similarity of graph highlighted metrics and algorithm classification pertain sentimentality pre-dictions. This technique facilitates the explicability and clarifies the results in the knowledge graph. Also, we compare our differentiate the embeddings n-gram based on sentiment analysis and the result is indicated that our study can outperform classical n-gram models, with an F1-score of 89% and recall up to 90%.

scholarly journals BERT-INT:A BERT-based Interaction Model For Knowledge Graph Alignment

2020 ◽  
Author(s):  
Xiaobin Tang ◽  
Jing Zhang ◽  
Bo Chen ◽  
Yang Yang ◽  
Hong Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
State Of The Art ◽  
Side Information ◽  
Interaction Model ◽  
Experimental Results ◽  
Knowledge Graph ◽  
Fine Grained ◽  
Graph Alignment ◽  
Knowledge Graphs ◽  
Graph Neural Networks

Knowledge graph alignment aims to link equivalent entities across different knowledge graphs. To utilize both the graph structures and the side information such as name, description and attributes, most of the works propagate the side information especially names through linked entities by graph neural networks. However, due to the heterogeneity of different knowledge graphs, the alignment accuracy will be suffered from aggregating different neighbors. This work presents an interaction model to only leverage the side information. Instead of aggregating neighbors, we compute the interactions between neighbors which can capture fine-grained matches of neighbors. Similarly, the interactions of attributes are also modeled. Experimental results show that our model significantly outperforms the best state-of-the-art methods by 1.9-9.7% in terms of HitRatio@1 on the dataset DBP15K.

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