Stochastic Optimization for Market Return Prediction Using Financial Knowledge Graph

Fu, Xiaoyi; Ren, Xinqi; Mengshoel, Ole J.; Wu, Xindong

doi:10.1109/icbk.2018.00012

Cited by 18 publications

(12 citation statements)

References 2 publications

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“…The experimental results show that our model in ''side1'' (i.e., predicting head entities in 1-1 and 1-M; predicting tail entities in 1-1 and M-1) and ''sideM'' (i.e., predicting head entities in M-1 and M-M; predicted tail entities in 1-M and M-M) have good performance. The main reasons are: (1) We provide an embedding representation for each triple, making the representation of the triple more precise, and the discrimination between the triples is higher. so compared to other models, our model has better performance; (2) The proposed attenuated attention mechanism makes the relation learned by each triple differently, so when making predictions, whether (r k , e j ) or (e i , r k ), they can still learn different representations, so the results are more accurate when ranking.…”

Section: Results and Analysismentioning

confidence: 99%

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Knowledge Graph Embedding via Graph Attenuated Attention Networks

Wang

et al. 2020

IEEE Access

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Knowledge graphs contain a wealth of real-world knowledge that can provide strong support for artificial intelligence applications. Much progress has been made in knowledge graph completion, stateof-the-art models are based on graph convolutional neural networks. These models automatically extract features, in combination with the features of the graph model, to generate feature embeddings with a strong expressive ability. However, these methods assign the same weights on the relation path in the knowledge graph and ignore the rich information presented in neighbor nodes, which result in incomplete mining of triple features. To this end, we propose Graph Attenuated Attention networks(GAATs), a novel representation method, which integrates an attenuated attention mechanism to assign different weight in different relation path and acquire the information from the neighborhoods. As a result, entities and relations can be learned in any neighbors. Our empirical research provides insight into the effectiveness of the attenuated attentionbased models, and we show significant improvement compared to the state-of-the-art methods on two benchmark datasets WN18RR and FB15k-237. INDEX TERMS Knowledge graph embedding, attenuated attention mechanism, neighbor nodes, translational model, graph attention networks.

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Section: Results and Analysismentioning

confidence: 99%

“…Knowledge graphs are used to describe concepts, entities, and the rich relations between them in the real world. At present, knowledge graphs have been widely used in finance [1], medical [2], semantic search [3], and other fields.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Graph Embedding via Graph Attenuated Attention Networks

Wang

et al. 2020

IEEE Access

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“…Knowledge Graph (KG), as one of the most important infrastructures of artificial intelligence, has received much attention in both academia [1]- [4] and industrial fields [5]- [8]. The mainstream large-scale knowledge graphs are all publicly available on the web, such as Wikidata [9], DBpedia [10], YAGO [11], [12], LinkMDB [13].…”

Section: Introductionmentioning

confidence: 99%

“…1 , o 1 ), (s, p 1 , o 3 ), (s, p 2 , o 4 ), (s, p 3 , o 2 ), (s, p 2 , o 3 ), and the newly added 2 triples: (s, p 4 , o 1 ), (s, p 5 , o 4 ), we can obtain len(entity) = 7. According to the Equation (5), the values of uniqueness for all triples are calculated as follows:uniqueness(s, p 2 , o 4 ) = 3, uniqueness(s, p 5 , o 4 ) = 7 uniqueness(s, p 2 , o 3 ) = 3, uniqueness(s, p 1 , o 1 ) = 3 uniqueness(s, p 3 , o 2 ) = 7, uniqueness(s, p 4 , o 1 ) = 7 uniqueness(s, p 1 , o 3 ) = 3Concretely, because the number of predicates p 2 and p 5 in all triples is 2 and 1, respectively, uniqueness(s, p 2 , o 4 ) = 3 and uniqueness(s, p 5 , o 4 ) = 7 by the Equation(5). When assigning the scores to triples, we traverse all concepts and calculate the scores of triples ranking(s, p, o) according to the hierarchy of the re-ranked concepts.…”

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confidence: 99%

Incremental Entity Summarization With Formal Concept Analysis

Yang

Hao

Yang

et al. 2022

IEEE Trans. Serv. Comput.

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Knowledge graph describes entities by numerous RDF data (subject-predicate-object triples), which has been widely applied in various fields, such as artificial intelligence, Semantic Web, entity summarization. With time elapses, the continuously increasing RDF descriptions of entity lead to information overload and further cause people confused. With this backdrop, automatic entity summarization has received much attention in recent years, aiming to select the most concise and most typical facts that depict an entity in brief from lengthy RDF data. As new descriptions of entity are continually coming, creating a compact summary of entity quickly from a lengthy knowledge graph is challenging. To address this problem, this paper firstly formulates the problem and proposes a novel approach of Incremental Entity Summarization by leveraging Formal Concept Analysis (FCA), called IES-FCA. Additionally, we not only prove the rationality of our suggested method mathematically, but also carry out extensive experiments using two real-world datasets. The experimental results demonstrate that the proposed method IES-FCA can save about 8.7% of time consumption for all entities than the non-incremental entity summarization approach KAFCA at best. As for the effectiveness, IES-FCA outperforms the stateof-the-art algorithms in terms of F 1 − measure, M AP , and N DCG.

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“…The relationship among any entity is represented by the edges in the graph. The real world concepts are described via rich relations in the graph and it is extensively used in the fields of semantic search (1) , medical (2) and finance (3) . The insufficiency of information in the KG leads to the incompleteness of data that makes the data processing as incomplete.…”

Section: Introductionmentioning

confidence: 99%

Knowledge graph embedding via the properties of mapping and dynamic matrix

Devi¹,

Kalaivani²

2021

IJST

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Objective:The main objective of this research is constructing a knowledge graph using a superior model that leverage the knowledge through relational mapping properties and the matrix constructed dynamically based on the changes in the relational mapping. Methods: This research work proposes symbolic objects in the knowledge graph that are signified by two vectors, the first vector denotes the entity/relation and the vector denote the matrix which is constructed with the help of mapping properties. The proposed methodology uses only minimum parameters and avoids multiplication processes which make the proposed scheme effective in the large scale graphs. The matrix representation with the assistance of mapping properties reduces the computational complication and the time requires to attain the knowledge is also minimized. Findings: The empirical research on knowledge graph mining gives significant insights and it is applied on benchmark datasets namely FB15K-237 and WN18RR. The performance are evaluated in terms of precision and recall in percentage.

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Stochastic Optimization for Market Return Prediction Using Financial Knowledge Graph

Cited by 18 publications

References 2 publications

Knowledge Graph Embedding via Graph Attenuated Attention Networks

Knowledge Graph Embedding via Graph Attenuated Attention Networks

Incremental Entity Summarization With Formal Concept Analysis

Knowledge graph embedding via the properties of mapping and dynamic matrix

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