CAREA: Cotraining Attribute and Relation Embeddings for Cross-Lingual Entity Alignment in Knowledge Graphs

Knowledge graphs (KGs) are one of the most widely used techniques of knowledge organizations and have been extensively used in many application fields related to artificial intelligence, for example, web search and recommendations. Entity alignment provides a useful tool for how to integrate multilingual KGs automatically. However, most of the existing studies evaluated ignore the abundant information of entity attributes except for entity relationships. This paper sets out to investigate cross-lingual entity alignment and proposes an iterative cotraining approach (CAREA) to train a pair of independent models. The two models can extract the attribute and the relation features of multilingual KGs, respectively. In each iteration, the two models alternate to predict a new set of potentially aligned entity pairs. Besides, this method further filters through the dynamic threshold value to enhance the two models’ supervision. Experimental results on three real-world datasets demonstrate the effectiveness and superiority of the proposed method. The CAREA model improves the performance with at least an absolute increase of 3.9 % across all experiment datasets. The code is available at https://github.com/ChenBaiyang/CAREA.

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Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/209 ◽

2017 ◽

Cited By ~ 42

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.

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Learning to Explain Entity Relationships by Pairwise Ranking with Convolutional Neural Networks

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/561 ◽

2017 ◽

Cited By ~ 3

Author(s):

Jizhou Huang ◽

Wei Zhang ◽

Shiqi Zhao ◽

Shiqiang Ding ◽

Haifeng Wang

Keyword(s):

Search Engine ◽

Large Scale ◽

Web Search ◽

Training Data ◽

Data Annotation ◽

Web Search Engine ◽

Clickthrough Data ◽

Ranking Model ◽

Knowledge Graphs ◽

Entity Relationships

Providing a plausible explanation for the relationship between two related entities is an important task in some applications of knowledge graphs, such as in search engines. However, most existing methods require a large number of manually labeled training data, which cannot be applied in large-scale knowledge graphs due to the expensive data annotation. In addition, these methods typically rely on costly handcrafted features. In this paper, we propose an effective pairwise ranking model by leveraging clickthrough data of a Web search engine to address these two problems. We first construct large-scale training data by leveraging the query-title pairs derived from clickthrough data of a Web search engine. Then, we build a pairwise ranking model which employs a convolutional neural network to automatically learn relevant features. The proposed model can be easily trained with backpropagation to perform the ranking task. The experiments show that our method significantly outperforms several strong baselines.

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TransET: Knowledge Graph Embedding with Entity Types

Electronics ◽

10.3390/electronics10121407 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1407

Author(s):

Peng Wang ◽

Jing Zhou ◽

Yuzhang Liu ◽

Xingchen Zhou

Keyword(s):

Link Prediction ◽

State Of The Art ◽

Score Function ◽

Graph Embedding ◽

Vector Spaces ◽

Knowledge Graph ◽

Semantic Features ◽

Knowledge Graphs ◽

Real World Datasets ◽

Low Dimensional

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.

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Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data

Future Internet ◽

10.3390/fi13050124 ◽

2021 ◽

Vol 13 (5) ◽

pp. 124

Author(s):

Jiseong Son ◽

Chul-Su Lim ◽

Hyoung-Seop Shim ◽

Ji-Sun Kang

Keyword(s):

Artificial Intelligence ◽

Domain Knowledge ◽

Open Data ◽

Heterogeneous Data ◽

Big Data Analysis ◽

Knowledge Graph ◽

Cross Domain ◽

Disaster Data ◽

Knowledge Graphs ◽

Open Datasets

Despite the development of various technologies and systems using artificial intelligence (AI) to solve problems related to disasters, difficult challenges are still being encountered. Data are the foundation to solving diverse disaster problems using AI, big data analysis, and so on. Therefore, we must focus on these various data. Disaster data depend on the domain by disaster type and include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there are several issues, where the source and format of data are different because various data are collected by different organizations. Moreover, the vocabularies used for each domain are inconsistent. This study proposes a knowledge graph to resolve the heterogeneity among various disaster data and provide interoperability among domains. Among disaster domains, we describe the knowledge graph for flooding disasters using Korean open datasets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist, solve, and manage disaster problems.

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A Survey of Techniques for Constructing Chinese Knowledge Graphs and Their Applications

Sustainability ◽

10.3390/su10093245 ◽

2018 ◽

Vol 10 (9) ◽

pp. 3245 ◽

Cited By ~ 7

Author(s):

Tianxing Wu ◽

Guilin Qi ◽

Cheng Li ◽

Meng Wang

Keyword(s):

Artificial Intelligence ◽

Question Answering ◽

Knowledge Representation And Reasoning ◽

Knowledge Graph ◽

Development History ◽

One Belt One Road ◽

History Of ◽

Knowledge Graphs ◽

The One ◽

The Impact

With the continuous development of intelligent technologies, knowledge graph, the backbone of artificial intelligence, has attracted much attention from both academic and industrial communities due to its powerful capability of knowledge representation and reasoning. In recent years, knowledge graph has been widely applied in different kinds of applications, such as semantic search, question answering, knowledge management and so on. Techniques for building Chinese knowledge graphs are also developing rapidly and different Chinese knowledge graphs have been constructed to support various applications. Under the background of the “One Belt One Road (OBOR)” initiative, cooperating with the countries along OBOR on studying knowledge graph techniques and applications will greatly promote the development of artificial intelligence. At the same time, the accumulated experience of China in developing knowledge graphs is also a good reference to develop non-English knowledge graphs. In this paper, we aim to introduce the techniques of constructing Chinese knowledge graphs and their applications, as well as analyse the impact of knowledge graph on OBOR. We first describe the background of OBOR, and then introduce the concept and development history of knowledge graph and typical Chinese knowledge graphs. Afterwards, we present the details of techniques for constructing Chinese knowledge graphs, and demonstrate several applications of Chinese knowledge graphs. Finally, we list some examples to explain the potential impacts of knowledge graph on OBOR.

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Research on the Application of Artificial Intelligence Technology in Computer Graphics Processing

Journal of Physics Conference Series ◽

10.1088/1742-6596/2083/4/042037 ◽

2021 ◽

Vol 2083 (4) ◽

pp. 042037

Author(s):

Xia Yang

Keyword(s):

Artificial Intelligence ◽

Threshold Value ◽

Shooting Methods ◽

Colour Space ◽

Space Transformation ◽

Artificial Intelligence Technology ◽

Colour Saturation ◽

Hsv Colour Space ◽

Graphics Processing ◽

Bright Area

Abstract In structured light geometric reconstruction, due to the complexity of shooting methods and scene lighting conditions, the resulting images may be lack of image details due to uneven light. For this reason, the article proposes a Retinex algorithm with colour restoration and colour saturation correction strategy based on HSV colour space transformation based on artificial intelligence technology. Then distinguish whether it is a bright area according to the threshold value, and modify the insufficient transmittance estimation of the bright area. Finally, the intensity component and saturation value are restored in the HIS colour space, and the histogram is used to stretch the intensity component.

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Cross-lingual analysis of English and Chinese web search

International Journal of Web and Grid Services ◽

10.1504/ijwgs.2018.095663 ◽

2018 ◽

Vol 14 (4) ◽

pp. 376

Author(s):

Peiguang Lin ◽

Tong Zhang ◽

Menglong Xia ◽

Jin Zhou ◽

Peiyao Nie

Keyword(s):

Web Search ◽

Cross Lingual

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Forecasting Japan’s Solar Energy Consumption Using a Novel Incomplete Gamma Grey Model

Sustainability ◽

10.3390/su11215921 ◽

2019 ◽

Vol 11 (21) ◽

pp. 5921 ◽

Cited By ~ 2

Author(s):

Peng Zhang ◽

Xin Ma ◽

Kun She

Keyword(s):

Energy Consumption ◽

Solar Energy ◽

Nonlinear Coefficient ◽

Prediction Method ◽

Decision Makers ◽

Grey Model ◽

Correct Prediction ◽

Grey Prediction ◽

Real World Datasets ◽

Application Fields

Energy consumption is an essential basis for formulating energy policy and programming, especially in the transition of energy consumption structure in a country. Correct prediction of energy consumption can provide effective reference data for decision-makers and planners to achieve sustainable energy development. Grey prediction method is one of the most effective approaches to handle the problem with a small amount of historical data. However, there is still room to improve the prediction performance and enlarge the application fields of the traditional grey model. Nonlinear grey action quantity can effectively improve the performance of the grey prediction model. Therefore, this paper proposes a novel incomplete gamma grey model (IGGM) with a nonlinear grey input over time. The grey input of the IGGM model is a revised incomplete gamma function of time in which the nonlinear coefficient determines the performance of the IGGM model. The WOA algorithm is employed to seek for the optimal incomplete coefficient of the IGGM model. Then, the validations of IGGM are performed on four real-world datasets, and the results exhibit that the IGGM model has more advantages than the other state-of-the-art grey models. Finally, the IGGM model is applied to forecast Japan’s solar energy consumption in the next three years.

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