Knowledge Graph Representation with Jointly Structural and Textual Encoding

Xu, Jiacheng; Qiu, Xipeng; Chen, Kan; Huang, Xuanjing

doi:10.24963/ijcai.2017/183

Cited by 92 publications

(56 citation statements)

References 20 publications

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“…NTN [37] is the earliest model to integrate text descriptions into KG embedding learning [47], and the representations of entities are initialized by average vectors of words contained in their names. TEKE [49] defines context vectors of entities and relations, and combines context vectors into traditional models, e.g., TransE, Xie et al [51] and Xu et al [52] encode textual literals by convolutional and recurrent neural networks. LiteralE [24] replaces the original entity embeddings of conventional loss functions with literal-enriched vectors, which are defined by learnable parametrized functions.…”

Section: Kg Embedding Techniquesmentioning

confidence: 99%

Joint Embedding Learning of Educational Knowledge Graphs

Yao

Wang

Sun

et al. 2020

Advances in Analytics for Learning and Teaching

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As an efficient model for knowledge organization, the knowledge graph has been widely adopted in several fields, e.g., biomedicine, sociology, and education. And there is a steady trend of learning embedding representations of knowledge graphs to facilitate knowledge graph construction and downstream tasks. In general, knowledge graph embedding techniques aim to learn vectorized representations which preserve the structural information of the graph. And conventional embedding learning models rely on structural relationships among entities and relations. However, in educational knowledge graphs, structural relationships are not the focus. Instead, rich literals of the graphs are more valuable. In this paper, we focus on this problem and propose a novel model for embedding learning of educational knowledge graphs. Our model considers both structural and literal information and jointly learns embedding representations. Three experimental graphs were constructed based on an educational knowledge graph which has been applied in real-world teaching. We conducted two experiments on the three graphs and other common benchmark graphs. The experimental results proved the effectiveness of our model and its superiority over other baselines when processing educational knowledge graphs.

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Section: Kg Embedding Techniquesmentioning

confidence: 99%

Joint Embedding Learning of Educational Knowledge Graphs

Yao

Wang

Sun

et al. 2020

Advances in Analytics for Learning and Teaching

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“…Compared with these studies, we are the first to incorporate multi-head graph attention (Sukhbaatar et al, 2015;Madotto et al, 2018;Veličković et al, 2018) to encourage the model to capture multi-aspect relevance among nodes. Similar to (Wang and Li, 2016;Xu et al, 2017), we enrich entity representation by combining the contextual sentences that include the target entity and its neighbors from the graph structure. This is the first work to incorporate new idea creation via link prediction into automatic paper writing.…”

Section: Related Workmentioning

confidence: 99%

PaperRobot: Incremental Draft Generation of Scientific Ideas

Wang¹,

Huang²,

Jiang³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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We present a PaperRobot who performs as an automatic research assistant by (1) conducting deep understanding of a large collection of human-written papers in a target domain and constructing comprehensive background knowledge graphs (KGs); (2) creating new ideas by predicting links from the background KGs, by combining graph attention and contextual text attention; (3) incrementally writing some key elements of a new paper based on memory-attention networks: from the input title along with predicted related entities to generate a paper abstract, from the abstract to generate conclusion and future work, and finally from future work to generate a title for a follow-on paper. Turing Tests, where a biomedical domain expert is asked to compare a system output and a human-authored string, show PaperRobot generated abstracts, conclusion and future work sections, and new titles are chosen over human-written ones up to 30%, 24% and 12% of the time, respectively. 1

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“…Because the TransE model represents a one-to-one relationship between two entities [7], the relationship amongst many entities must be continuously incorporated into texts and knowledge graph [44]. This model combines a convolutional neural network with textual information extraction, which fully exploits the semantic information in a knowledge graph and text [45,46]. In Figure 4, knowledge graph contains rich semantics in entity description texts, but it is not fully utilized in feature extraction.…”

Section: Combined Featuresmentioning

confidence: 99%

Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach

et al. 2019

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An increasing number of the renowned company's investors are turning attention to stock prediction in the search for new efficient ways of hypothesizing about markets through the application of behavioral finance. Accordingly, research on stock prediction is becoming a popular direction in academia and industry. In this study, the goal is to establish a model for predicting stock price movement through knowledge graph from the financial news of the renowned companies. In contrast to traditional methods of stock prediction, our approach considers the effects of event tuple characteristics on stocks on the basis of knowledge graph and deep learning. The proposed model and other feature selection models were used to perform feature extraction on the websites of Thomson Reuters and Cable News Network. Numerous experiments were conducted to derive evidence of the effectiveness of knowledge graph embedding for classification tasks in stock prediction. A comparison of the average accuracy with which the same feature combinations were extracted over six stocks indicated that the proposed method achieves better performance than that exhibited by an approach that uses only stock data, a bag-of-words method, and convolutional neural network. Our work highlights the usefulness of knowledge graph in implementing business activities and helping practitioners and managers make business decisions.Hindawi Complexity Volume 2019, Article ID 9202457, 15 pages https://doi.

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Knowledge Graph Representation with Jointly Structural and Textual Encoding

Cited by 92 publications

References 20 publications

Joint Embedding Learning of Educational Knowledge Graphs

Joint Embedding Learning of Educational Knowledge Graphs

PaperRobot: Incremental Draft Generation of Scientific Ideas

Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach

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