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Knowledge Information Extraction Research

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 June 2024 | Viewed by 7679

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Special Issue Editors

Dr. Lanting Fang

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Guest Editor

School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
Interests: data mining; natural language processing; graph neural network
Special Issues, Collections and Topics in MDPI journals

Dr. Yubo Song

E-Mail Website
Guest Editor

School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
Interests: data mining; data privacy; internet of things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of "Knowledge Information Extraction Research" is characterized by its focus on advancing methods and techniques that extract structured knowledge from diverse data sources. While this area has explored various aspects of knowledge extraction, there is a continued need to address complex challenges and enhance the practical applications of these methods.

This Special Issue aims to contribute to the evolution of knowledge information extraction by fostering innovative research. It invites researchers to explore novel methodologies and tools that can improve the precision and efficiency of knowledge extraction processes. The scope includes interdisciplinary collaboration, drawing on expertise from fields such as natural language processing, machine learning, data mining, and information retrieval.

The main goals of this Special Issue are to advance the collective knowledge in the field, introduce methodological innovations, and emphasize the practical relevance of knowledge extraction. Research within this issue is not limited to academic exploration, but also seeks to bridge the gap between theory and real-world problem solving in healthcare, finance, and e-commerce industries, among others.

Topics of interest include, but are not limited to, the following:

Advanced knowledge extraction techniques;
Interdisciplinary approaches;
Practical applications in diverse domains;
Methodological innovations;
Bridging the gap between academia and industry;
Collaborative efforts to enhance knowledge extraction.

The goal of this Special Issue is to contribute to the existing literature by consolidating knowledge, introducing new methodologies, and promoting the application of information extraction techniques to real-world challenges. It provides a platform for researchers to share their insights and advancements, shaping the future of knowledge information extraction.

We invite contributions that expand the boundaries of knowledge extraction and encourage researchers to explore the potential of these techniques in practical contexts. Your submissions will play a crucial role in advancing the field and addressing the ever-evolving demands for structured knowledge from a wide range of data sources.

Dr. Lanting Fang
Dr. Yubo Song
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

data mining
information retrieval
machine learning
information extraction
techniques
knowledge graphs

Published Papers (12 papers)

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Research

21 pages, 3422 KiB

Open AccessArticle

Optimizing Network Service Continuity with Quality-Driven Resource Migration

by Chaofan Chen, Yubo Song, Yu Jiang and Mingming Zhang

Electronics 2024, 13(9), 1666; https://doi.org/10.3390/electronics13091666 - 25 Apr 2024

Viewed by 352

Abstract

Despite advances in security technology, it is impractical to entirely prevent intrusion threats. Consequently, developing effective service migration strategies is crucial to maintaining the continuity of network services. Current service migration strategies initiate the migration process only upon detecting a loss of service functionality in the nodes, which increases the risk of service interruptions. Moreover, the migration decision-making process has not adequately accounted for the alignment between tasks and node resources, thereby amplifying the risk of system overload. To address these shortcomings, we introduce a Quality-Driven Resource Migration Strategy (QD-RMS). Specifically, QD-RMS initiates the migration process at an opportune moment, determined through an analysis of service quality. Subsequently, it employs a method combining Pareto optimality and the simulated annealing algorithm to identify the node most suitable for migration. This approach not only guarantees seamless service continuity but also ensures optimal resource distribution and load balancing. The experiments demonstrate that, in comparison with conventional migration strategies, QD-RMS achieves superior service quality and an approximate 20% increase in maximum task capacity. This substantiates the strategic superiority and technological advancement of the proposed strategy. Full article

(This article belongs to the Special Issue Knowledge Information Extraction Research)

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17 pages, 3909 KiB

Open AccessArticle

Design and Development of Knowledge Graph for Industrial Chain Based on Deep Learning

by Yue Li, Yutian Lei, Yiting Yan, Chang Yin and Jiale Zhang

Electronics 2024, 13(8), 1539; https://doi.org/10.3390/electronics13081539 - 18 Apr 2024

Viewed by 415

Abstract

This paper aims to structure and semantically describe the information within the industrial chain by constructing an Industry Chain Knowledge Graph (ICKG), enabling more efficient and intelligent information management and analysis. In more detail, this paper constructs a multi-domain industrial chain dataset and proposes a method that combines the top-down establishment of a semantic expression framework with the bottom-up establishment of a data layer to build an ICKG. In the data layer, a deep learning algorithm based on BERT-BiLSTM-CRF is used to extract industry chain entities from relevant literature and reports. The results indicate that the model can effectively identify industry chain entities. These entities and relationships populate a Neo4j graph database, creating a large-scale ICKG for visual display and aiding cross-domain applications. Full article

(This article belongs to the Special Issue Knowledge Information Extraction Research)

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13 pages, 715 KiB

Open AccessArticle

Knowledge Graph Multi-Hop Question Answering Based on Dependent Syntactic Semantic Augmented Graph Networks

by Songtao Cai, Qicheng Ma, Yupeng Hou and Guangping Zeng

Electronics 2024, 13(8), 1436; https://doi.org/10.3390/electronics13081436 - 11 Apr 2024

Cited by 1 | Viewed by 516

Abstract

In the rapidly evolving domain of question answering systems, the ability to integrate machine comprehension with relational reasoning stands paramount. This paper introduces a novel architecture, the Dependent Syntactic Semantic Augmented Graph Network (DSSAGN), designed to address the intricate challenges of multi-hop question answering. By ingeniously leveraging the synergy between syntactic structures and semantic relationships within knowledge graphs, DSSAGN offers a breakthrough in interpretability, scalability, and accuracy. Unlike previous models that either fall short in handling complex relational paths or lack transparency in reasoning, our framework excels by embedding a sophisticated mechanism that meticulously models multi-hop relations and dynamically prioritizes the syntactic–semantic context. Full article

(This article belongs to the Special Issue Knowledge Information Extraction Research)

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19 pages, 621 KiB

Open AccessArticle

Incorporating Entity Type-Aware and Word–Word Relation-Aware Attention in Generative Named Entity Recognition

by Ying Mo and Zhoujun Li

Electronics 2024, 13(7), 1407; https://doi.org/10.3390/electronics13071407 - 8 Apr 2024

Viewed by 556

Abstract

Named entity recognition (NER) is a critical subtask in natural language processing. It is particularly valuable to gain a deeper understanding of entity boundaries and entity types when addressing the NER problem. Most previous sequential labeling models are task-specific, while recent years have witnessed the rise of generative models due to the advantage of tackling NER tasks in the encoder–decoder framework. Despite achieving promising performance, our pilot studies demonstrate that existing generative models are ineffective at detecting entity boundaries and estimating entity types. In this paper, a multiple attention framework is proposed which introduces the attention of entity-type embedding and word–word relation into the named entity recognition task. To improve the accuracy of entity-type mapping, we adopt an external knowledge base to calculate the prior entity-type distributions and then incorporate the information input to the model via the encoder’s self-attention. To enhance the contextual information, we take the entity types as part of the input. Our method obtains the other attention from the hidden states of entity types and utilizes it in self- and cross-attention mechanisms in the decoder. We transform the entity boundary information in the sequence into word–word relations and extract the corresponding embedding into the cross-attention mechanism. Through word–word relation information, the method can learn and understand more entity boundary information, thereby improving its entity recognition accuracy. We performed experiments on extensive NER benchmarks, including four flat and two long entity benchmarks. Our approach significantly improves or performs similarly to the best generative NER models. The experimental results demonstrate that our method can substantially enhance the capabilities of generative NER models. Full article

(This article belongs to the Special Issue Knowledge Information Extraction Research)

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20 pages, 466 KiB

Open AccessArticle

Advancing User Privacy in Virtual Power Plants: A Novel Zero-Knowledge Proof-Based Distributed Attribute Encryption Approach

by Ruxia Yang, Hongchao Gao, Fangyuan Si and Jun Wang

Electronics 2024, 13(7), 1283; https://doi.org/10.3390/electronics13071283 - 29 Mar 2024

Viewed by 416

Abstract

In virtual power plants, diverse business scenarios involving user data, such as queries, transactions, and sharing, pose significant privacy risks. Traditional attribute-based encryption (ABE) methods, while supporting fine-grained access, fall short of fully protecting user privacy as they require attribute input, leading to potential data leaks. Addressing these limitations, our research introduces a novel privacy protection scheme using zero-knowledge proof and distributed attribute-based encryption (DABE). This method innovatively employs Merkel trees for aggregating user attributes and constructing commitments for zero-knowledge proof verification, ensuring that user attributes and access policies remain confidential. Our solution not only enhances privacy but also fortifies security against man-in-the-middle and replay attacks, offering attribute indistinguishability and tamper resistance. A comparative performance analysis demonstrates that our approach outperforms existing methods in efficiency, reducing time, cost, and space requirements. These advancements mark a significant step forward in ensuring robust user privacy and data security in virtual power plants. Full article

(This article belongs to the Special Issue Knowledge Information Extraction Research)

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15 pages, 664 KiB

Open AccessArticle

A Malicious Program Behavior Detection Model Based on API Call Sequences

by Nige Li, Ziang Lu, Yuanyuan Ma, Yanjiao Chen and Jiahan Dong

Electronics 2024, 13(6), 1092; https://doi.org/10.3390/electronics13061092 - 15 Mar 2024

Viewed by 566

Abstract

To address the issue of low accuracy in detecting malicious program behaviors in new power system edge-side applications, we present a detection model based on API call sequences that combines rule matching and deep learning techniques in this paper. We first use the PrefixSpan algorithm to mine frequent API call sequences in different threads of the same program within a malicious program dataset to create a rule base for malicious behavior sequences. The API call sequences to be examined are then matched using the malicious behavior sequence matching model, and those that do not match are fed into the TextCNN deep learning detection model for additional detection. The two models collaborate to accomplish program behavior detection. Experimental results demonstrate that the proposed detection model can effectively identify malicious samples and discern malicious program behaviors. Full article

(This article belongs to the Special Issue Knowledge Information Extraction Research)

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13 pages, 1671 KiB

Open AccessArticle

Signal Separation Method for Radiation Sources Based on a Parallel Denoising Autoencoder

by Xusheng Tang and Mingfeng Wei

Electronics 2024, 13(6), 1029; https://doi.org/10.3390/electronics13061029 - 9 Mar 2024

Viewed by 498

Abstract

Radiation source signal sorting in complex environments is currently a hot issue in the field of electronic countermeasures. The pulse repetition interval (PRI) can provide stable and obvious parametric features in radiation source identification, which is an important parameter relying on the signal sorting problem. To solve the problem linked to the difficulties in sorting the PRI in complex environments using the traditional method, a signal sorting method based on a parallel denoising autoencoder is proposed. This method implements the binarized preprocessing of known time-of-arrival (TOA) sequences and then constructs multiple parallel denoising autoencoder models using fully connected layers to achieve the simultaneous sorting of multiple signal types in the overlapping signals. The experimental results show that this method maintains high precision in scenarios prone to large error and can efficiently filter out noise and highlight the original features of the signal. In addition, the present model maintains its performance and some robustness in the sorting of different signal types. Compared with the traditional algorithm, this method improves the precision of sorting. The algorithm presented in this study still maintains above 90% precision when the pulse loss rate reaches 50%. Full article

(This article belongs to the Special Issue Knowledge Information Extraction Research)

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20 pages, 1052 KiB

Open AccessArticle

Secure Device-to-Device Communication in IoT: Fuzzy Identity from Wireless Channel State Information for Identity-Based Encryption

by Bo Zhang, Tao Zhang, Zesheng Xi, Ping Chen, Jin Wei and Yu Liu

Electronics 2024, 13(5), 984; https://doi.org/10.3390/electronics13050984 - 5 Mar 2024

Viewed by 750

Abstract

With the rapid development of the Internet of Things (IoT), ensuring secure communication between devices has become a crucial challenge. This paper proposes a novel secure communication solution by extracting wireless channel state information (CSI) features from IoT devices to generate a device identity. Due to the instability of the wireless channel, the CSI features are fuzzy and time-varying; thus, we a employ locally sensitive hashing (LSH) algorithm to ensure the stability of the generated identity in a dynamically changing wireless channel environment. Furthermore, zero-knowledge proofs are utilized to guarantee the authenticity and effectiveness of the generated identity. Finally, the identity generated using the aforementioned approach is integrated into an IBE communication scheme, which involves the fuzzy extraction of channel state information from IoT devices, stable identity extraction for fuzzy IoT devices using LSH, and the use of zero-knowledge proofs to ensure the authenticity of the generated identity. This identity is then employed as the identity information in identity-based encryption (IBE), constructing the device’s public key for achieving confidential communication between devices. Full article

(This article belongs to the Special Issue Knowledge Information Extraction Research)

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18 pages, 2769 KiB

Open AccessArticle

Device Identity Recognition Based on an Adaptive Environment for Intrinsic Security Fingerprints

by Zesheng Xi, Gongxuan Zhang, Bo Zhang and Tao Zhang

Electronics 2024, 13(3), 656; https://doi.org/10.3390/electronics13030656 - 5 Feb 2024

Viewed by 716

Abstract

A device’s intrinsic security fingerprint, representing its physical characteristics, serves as a unique identifier for user devices and is highly regarded in the realms of device security and identity recognition. However, fluctuations in the environmental noise can introduce variations in the physical features of the device. To address this issue, this paper proposes an innovative method to enable the device’s intrinsic security fingerprint to adapt to environmental changes, aiming to improve the accuracy of the device’s intrinsic security fingerprint recognition in real-world physical environments. This paper initiates continuous data collection of device features in authentic noisy environments, recording the temporal changes in the device’s physical characteristics. The problem of unstable physical features is framed as a restricted statistical learning problem with a localized information structure. This paper employs an aggregated hypergraph neural network architecture to process the temporally changing physical features. This allows the system to acquire aggregated local state information from the interactive influences of adjacent sequential signals, forming an adaptive environment-enhanced device intrinsic security fingerprint recognition model. The proposed method enhances the accuracy and reliability of device intrinsic security fingerprint recognition in outdoor environments, thereby strengthening the overall security of terminal devices. Experimental results indicate that the method achieves a recognition accuracy of 98% in continuously changing environmental conditions, representing a crucial step in reinforcing the security of Internet of Things (IoT) devices when confronted with real-world challenges. Full article

(This article belongs to the Special Issue Knowledge Information Extraction Research)

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14 pages, 1877 KiB

Open AccessArticle

Robust Soliton Distribution-Based Zero-Watermarking for Semi-Structured Power Data

by Lei Zhao, Yunfeng Zou, Chao Xu, Yulong Ma, Wen Shen, Qiuhong Shan, Shuai Jiang, Yue Yu, Yihan Cai, Yubo Song and Yu Jiang

Electronics 2024, 13(3), 655; https://doi.org/10.3390/electronics13030655 - 4 Feb 2024

Viewed by 640

Abstract

To ensure the security of online-shared power data, this paper adopts a robust soliton distribution-based zero-watermarking approach for tracing semi-structured power data. The method involves extracting partial key-value pairs to generate a feature sequence, processing the watermark into an equivalent number of blocks. Robust soliton distribution from erasure codes and redundant error correction codes is utilized to generate an intermediate sequence. Subsequently, the error-corrected watermark information is embedded into the feature sequence, creating a zero-watermark for semi-structured power data. In the tracking process, the extraction and analysis of the robust zero-watermark associated with the tracked data facilitate the effective identification and localization of data anomalies. Experimental and simulation validation demonstrates that this method, while ensuring data security, achieves a zero-watermark extraction success rate exceeding 98%. The proposed approach holds significant application value for data monitoring and anomaly tracking in power systems. Full article

(This article belongs to the Special Issue Knowledge Information Extraction Research)

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24 pages, 5547 KiB

Open AccessArticle

Demonstration-Based and Attention-Enhanced Grid-Tagging Network for Mention Recognition

by Haitao Jia, Jing Huang, Kang Zhao, Yousi Mao, Huanlai Zhou, Li Ren, Yuming Jia and Wenbo Xu

Electronics 2024, 13(2), 261; https://doi.org/10.3390/electronics13020261 - 5 Jan 2024

Viewed by 712

Abstract

Concepts empower cognitive intelligence. Extracting flat, nested, and discontinuous name entities and concept mentions from natural language texts is significant for downstream tasks such as concept knowledge graphs. Among the algorithms that uniformly detect these types of name entities and concepts, Li et al. proposed a novel architecture by modeling the unified mention recognition as the classification of word–word relations, named W²NER, achieved state-of-the-art (SOTA) results in 2022. However, there is still room for improvement. This paper presents three improvements based on W²NER. We enhanced the grid-tagging network by demonstration learning and tag attention feature extraction, so our modified model is named DTaE. Firstly, addressing the issue of insufficient semantic information in short texts and the lack of annotated data, and inspired by the demonstration learning from GPT-3, a demonstration is searched during the training phase according to a certain strategy to enhance the input features and improve the model’s ability for few-shot learning. Secondly, to tackle the problem of W²NER’s subpar recognition accuracy problem for discontinuous entities and concepts, a multi-head attention mechanism is employed to capture attention scores for different positions based on grid tagging. Then, the tagging attention features are embedded into the model. Finally, to retain information about the sequence position, rotary position embedding is introduced to ensure robustness. We selected an authoritative Chinese dictionary and adopted a five-person annotation method to annotate multiple types of entities and concepts in the definitions. To validate the effectiveness of our enhanced model, experiments were conducted on the public dataset CADEC and our annotated Chinese dictionary dataset: on the CADEC dataset, with a slight decrease in recall rate, precision is improved by 2.78%, and the comprehensive metric F1 is increased by 0.89%; on the Chinese dictionary dataset, the precision is improved by 2.97%, the recall rate is increased by 2.35%, and the comprehensive metric F1 is improved by 2.66%. Full article

(This article belongs to the Special Issue Knowledge Information Extraction Research)

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16 pages, 6245 KiB

Open AccessArticle

A Study on Toponymic Entity Recognition Based on Pre-Trained Models Fused with Local Features for Genglubu in the South China Sea

by Yinwei Wei, Yihong Li and Xiaoyi Zhou

Electronics 2024, 13(1), 4; https://doi.org/10.3390/electronics13010004 - 19 Dec 2023

Viewed by 728

Abstract

Toponymic entity recognition is currently a critical research hotspot in knowledge graphs. Under the guidance of the national ancient book protection policy and the promotion of the wave of digital humanities research, this paper proposes a toponymic entity recognition model (ALBERT-Conv1D-BiLSTM-CRF) based on the fusion of a pre-trained language model and local features to address the problems of toponymic ambiguity and the differences in ancient and modern grammatical structures in the field of the Genglubu. This model extracts global features with the ALBERT module, fuses global and local features with the Conv1D module, performs sequence modeling with the BiLSTM module to capture deep semantics and long-distance dependency information, and finally, completes sequence annotation with the CRF module. The experiments show that while taking into account the computational resources and cost, this improved model is significantly improved compared with the benchmark model (ALBERT-BiLSTM-CRF), and the precision, recall, and F1 are increased by 0.74%, 1.28%, and 1.01% to 98.08%, 96.67%, and 97.37%, respectively. The model achieved good results in the field of Genglubu. Full article

(This article belongs to the Special Issue Knowledge Information Extraction Research)

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