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 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 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 Entity Alignment between Knowledge Graphs Using Attribute Embeddings

2019 ◽  
Vol 33 ◽  
pp. 297-304 ◽  
Author(s):  
Bayu Distiawan Trisedya ◽  
Jianzhong Qi ◽  
Rui Zhang
Keyword(s):  
Real World ◽  
Graph Embedding ◽  
Knowledge Bases ◽  
Knowledge Graph ◽  
World Knowledge ◽  
Large Numbers ◽  
Proposed Model ◽  
Alignment Task ◽  
Transitivity Rule ◽  
Knowledge Graphs

The task of entity alignment between knowledge graphs aims to find entities in two knowledge graphs that represent the same real-world entity. Recently, embedding-based models are proposed for this task. Such models are built on top of a knowledge graph embedding model that learns entity embeddings to capture the semantic similarity between entities in the same knowledge graph. We propose to learn embeddings that can capture the similarity between entities in different knowledge graphs. Our proposed model helps align entities from different knowledge graphs, and hence enables the integration of multiple knowledge graphs. Our model exploits large numbers of attribute triples existing in the knowledge graphs and generates attribute character embeddings. The attribute character embedding shifts the entity embeddings from two knowledge graphs into the same space by computing the similarity between entities based on their attributes. We use a transitivity rule to further enrich the number of attributes of an entity to enhance the attribute character embedding. Experiments using real-world knowledge bases show that our proposed model achieves consistent improvements over the baseline models by over 50% in terms of hits@1 on the entity alignment task.

scholarly journals Predicting the relationships between gut microbiota and mental disorders with knowledge graphs

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Ting Liu ◽  
Xueli Pan ◽  
Xu Wang ◽  
K. Anton Feenstra ◽  
Jaap Heringa ◽  
...  
Keyword(s):  
Mental Disorders ◽  
Gut Microbiota ◽  
Research Effort ◽  
Sparql Query ◽  
Knowledge Graph ◽  
Test Cases ◽  
Relevant Research ◽  
Structured Knowledge ◽  
Knowledge Graphs ◽  
The Relationship

AbstractGut microbiota produce and modulate the production of neurotransmitters which have been implicated in mental disorders. Neurotransmitters may act as ‘matchmaker’ between gut microbiota imbalance and mental disorders. Most of the relevant research effort goes into the relationship between gut microbiota and neurotransmitters and the other between neurotransmitters and mental disorders, while few studies collect and analyze the dispersed research results in systematic ways. We therefore gather the dispersed results that in the existing studies into a structured knowledge base for identifying and predicting the potential relationships between gut microbiota and mental disorders. In this study, we propose to construct a gut microbiota knowledge graph for mental disorder, which named as MiKG4MD. It is extendable by linking to future ontologies by just adding new relationships between existing information and new entities. This extendibility is emphasized for the integration with existing popular ontologies/terminologies, e.g. UMLS, MeSH, and KEGG. We demonstrate the performance of MiKG4MD with three SPARQL query test cases. Results show that the MiKG4MD knowledge graph is an effective method to predict the relationships between gut microbiota and mental disorders.

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 Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment

2017 ◽  
Author(s):  
Muhao Chen ◽  
Yingtao Tian ◽  
Mohan Yang ◽  
Carlo Zaniolo
Keyword(s):  
Knowledge Base ◽  
Knowledge Bases ◽  
Loss Functions ◽  
Knowledge Graph ◽  
Graph Embeddings ◽  
Linear Transformations ◽  
Human Labor ◽  
Knowledge Graphs ◽  
Cross Lingual ◽  
Entity Relationships

Many recent works have demonstrated the benefits of knowledge graph embeddings in completing monolingual knowledge graphs. Inasmuch as related knowledge bases are built in several different languages, achieving cross-lingual knowledge alignment will help people in constructing a coherent knowledge base, and assist machines in dealing with different expressions of entity relationships across diverse human languages. Unfortunately, achieving this highly desirable cross-lingual alignment by human labor is very costly and error-prone. Thus, we propose MTransE, a translation-based model for multilingual knowledge graph embeddings, to provide a simple and automated solution. By encoding entities and relations of each language in a separated embedding space, MTransE provides transitions for each embedding vector to its cross-lingual counterparts in other spaces, while preserving the functionalities of monolingual embeddings. We deploy three different techniques to represent cross-lingual transitions, namely axis calibration, translation vectors, and linear transformations, and derive five variants for MTransE using different loss functions. Our models can be trained on partially aligned graphs, where just a small portion of triples are aligned with their cross-lingual counterparts. The experiments on cross-lingual entity matching and triple-wise alignment verification show promising results, with some variants consistently outperforming others on different tasks. We also explore how MTransE preserves the key properties of its monolingual counterpart.

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.

scholarly journals Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs

2019 ◽  
Author(s):  
Yuting Wu ◽  
Xiao Liu ◽  
Yansong Feng ◽  
Zheng Wang ◽  
Rui Yan ◽  
...  
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Dual Graph ◽  
Knowledge Graph ◽  
Convolutional Network ◽  
Complex Relation ◽  
Dual Relation ◽  
Knowledge Graphs ◽  
Cross Lingual ◽  
Heterogeneous Knowledge

Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods. Such approaches work by learning KG representations so that entity alignment can be performed by measuring the similarities between entity embeddings. While promising, prior works in the field often fail to properly capture complex relation information that commonly exists in multi-relational KGs, leaving much room for improvement. In this paper, we propose a novel Relation-aware Dual-Graph Convolutional Network (RDGCN) to incorporate relation information via attentive interactions between the knowledge graph and its dual relation counterpart, and further capture neighboring structures to learn better entity representations. Experiments on three real-world cross-lingual datasets show that our approach delivers better and more robust results over the state-of-the-art alignment methods by learning better KG representations.

scholarly journals Improved Knowledge Graph Embedding Using Background Taxonomic Information

2019 ◽  
Vol 33 ◽  
pp. 3526-3533 ◽  
Author(s):  
Bahare Fatemi ◽  
Siamak Ravanbakhsh ◽  
David Poole
Keyword(s):  
Lower Bound ◽  
Graph Embedding ◽  
Experimental Results ◽  
Knowledge Graph ◽  
Public Knowledge ◽  
Tensor Factorization ◽  
Relational Information ◽  
Universal Models ◽  
Taxonomic Information ◽  
Knowledge Graphs

Knowledge graphs are used to represent relational information in terms of triples. To enable learning about domains, embedding models, such as tensor factorization models, can be used to make predictions of new triples. Often there is background taxonomic information (in terms of subclasses and subproperties) that should also be taken into account. We show that existing fully expressive (a.k.a. universal) models cannot provably respect subclass and subproperty information. We show that minimal modifications to an existing knowledge graph completion method enables injection of taxonomic information. Moreover, we prove that our model is fully expressive, assuming a lower-bound on the size of the embeddings. Experimental results on public knowledge graphs show that despite its simplicity our approach is surprisingly effective.

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