A survey on the construction methods and applications of sci-tech big data knowledge graph

Zhou, Yuanchun; Qiao, Ziyue; Xiao, Meng; Du, Yi

doi:10.1360/ssi-2019-0271

Cited by 17 publications

(5 citation statements)

References 46 publications

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“…DKGs are widely used in medical (Li et al, 2020 ; Gong et al, 2021 ), financial (Song et al, 2017 ; Zhan and Yin, 2018 ; Chen and Xiang, 2020 ; Mao et al, 2022 ), scholarly research (Liu J. et al, 2020 ; Zhou et al, 2020 ; Kanakaris et al, 2021 ), tourism (Gao et al, 2020 ), disaster prevention (Du et al, 2020 ), e-government (Promikyridis and Tambouris, 2020 ), and other fields to provide a structured network knowledge base for the corresponding professionals. The data source of DKGs generally comes from richly preserved professional data in the domain, including structured data, semi-structured data, and unstructured data, such as free text and images.…”

Section: Related Workmentioning

confidence: 99%

“…For example, medical knowledge graphs are usually derived from electronic medical records (Gong et al, 2021 ), drug information (Wishart et al, 2017 ), or other medical data as auxiliary systems to reduce the diagnosis burden of doctors and allow better clinical decisions to be made. Science and technology knowledge graphs (Zhou et al, 2020 ) mainly utilize multi-source data related to the field, such as scientific papers, patents, and scientific projects, to help researchers find partners and grasp trends in academic research.…”

Section: Related Workmentioning

confidence: 99%

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A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graph

Yan,

Fang,

Huang

et al. 2023

Front. Big Data

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The knowledge graph is one of the essential infrastructures of artificial intelligence. It is a challenge for knowledge engineering to construct a high-quality domain knowledge graph for multi-source heterogeneous data. We propose a complete process framework for constructing a knowledge graph that combines structured data and unstructured data, which includes data processing, information extraction, knowledge fusion, data storage, and update strategies, aiming to improve the quality of the knowledge graph and extend its life cycle. Specifically, we take the construction process of an enterprise knowledge graph as an example and integrate enterprise register information, litigation-related information, and enterprise announcement information to enrich the enterprise knowledge graph. For the unstructured text, we improve existing model to extract triples and the F1-score of our model reached 72.77%. The number of nodes and edges in our constructed enterprise knowledge graph reaches 1,430,000 and 3,170,000, respectively. Furthermore, for each type of multi-source heterogeneous data, we apply corresponding methods and strategies for information extraction and data storage and carry out a detailed comparative analysis of graph databases. From the perspective of practical use, the informative enterprise knowledge graph and its timely update can serve many actual business needs. Our proposed enterprise knowledge graph has been deployed in HuaRong RongTong (Beijing) Technology Co., Ltd. and is used by the staff as a powerful tool for corporate due diligence. The key features are reported and analyzed in the case study. Overall, this paper provides an easy-to-follow solution and practice for domain knowledge graph construction, as well as demonstrating its application in corporate due diligence.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graph

Yan,

Fang,

Huang

et al. 2023

Front. Big Data

View full text Add to dashboard Cite

show abstract

“…When the spacecraft control system is abnormal, the ground experts can locate the fault source by manual inquiry, so it is difficult to make real-time diagnosis, accurately locate complex faults, and visualize fault diagnosis. Knowledge graph [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ], as an effective tool to describe massive knowledge, knowledge attributes, and knowledge relationships, provides a new means for fault diagnosis. We can manually or automatically construct the knowledge graph of the corresponding relationship between the performance and fault of the spacecraft control system by combining various model knowledge, expert knowledge, and data, which we call the spacecraft performance-fault relationship graph.…”

Section: Introductionmentioning

confidence: 99%

An Improved DDPG and Its Application in Spacecraft Fault Knowledge Graph

Xing

Wang

Liu

2023

Sensors

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We construct a spacecraft performance-fault relationship graph of the control system, which can help space robots locate and repair spacecraft faults quickly. In order to improve the performance-fault relationship graph, we improve the Deep Deterministic Policy Gradient (DDPG) algorithm, and propose a relationship prediction method that combines representation learning reasoning with deep reinforcement learning reasoning. We take the spacecraft performance-fault relationship graph as the agent learning environment and adopt reinforcement learning to realize the optimal interaction between the agent and the environment. Meanwhile, our model uses a deep neural network to construct a complex value function and strategy function, which makes the agent have excellent perceptual decision-making ability and accurate value judgment ability. We evaluate our model on a performance-fault relationship graph of the control system. The experimental results show that our model has high prediction speed and accuracy, which can completely infer the optimal relationship path between entities to complete the spacecraft performance-fault relationship graph.

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“…Generally, event details include event types, trigger words, and the various arguments that play different roles within events. The structured representation of events facilitates many downstream tasks, such as recommendation systems [2,3], knowledge graph construction [4,5], and intelligent question-answering systems [6,7]. Particularly in food and cosmetics sentiment monitoring, researchers have identified immense potential in utilizing news and social media data [8] for event extraction in sentiment analysis.…”

Section: Introductionmentioning

confidence: 99%

Modeling Graph Neural Networks and Dynamic Role Sorting for Argument Extraction in Documents

et al. 2023

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The existing methods for document-level event extraction mainly face two challenges. The first challenge is effectively capturing event information that spans across sentences. The second challenge is using predefined orders to extract event arguments while disregarding the dynamic adjusting of the order according to the importance of argument roles. To address these issues, we propose a model based on graph neural networks which realizes the semantic interaction among documents, sentences, and entities. Additionally, our model adopts a dynamic argument detection strategy, extracting arguments depending on their number in correspondence with each role. The experimental results confirm the outperformance of our model, which surpasses previous methods by 7% and 1.9% in terms of an F1 score.

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A survey on the construction methods and applications of sci-tech big data knowledge graph

Abstract: Identify crystal structures by a new paradigm based on graph theory for building materials big data SCIENCE CHINA Chemistry 62, 982 (2019); A survey on knowledge graph-based recommender systems

Cited by 17 publications

References 46 publications

A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graph

A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graph

An Improved DDPG and Its Application in Spacecraft Fault Knowledge Graph

Modeling Graph Neural Networks and Dynamic Role Sorting for Argument Extraction in Documents

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