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2. A review of knowledge graph application scenarios in cyber security

Author

Liu, Kai, Wang, Fei, Ding, Zhaoyun, Liang, Sheng, Yu, Zhengfei, and Zhou, Yun

Subjects
Computer Science - Cryptography and Security
Abstract

Facing the dynamic complex cyber environments, internal and external cyber threat intelligence, and the increasing risk of cyber-attack, knowledge graphs show great application potential in the cyber security area because of their capabilities in knowledge aggregation, representation, management, and reasoning. However, while most research has focused on how to develop a complete knowledge graph, it remains unclear how to apply the knowledge graph to solve industrial real challenges in cyber-attack and defense scenarios. In this review, we provide a brief overview of the basic concepts, schema, and construction approaches for the cyber security knowledge graph. To facilitate future research on cyber security knowledge graphs, we also present a curated collection of datasets and open-source libraries on the knowledge construction and information extraction task. In the major part of this article, we conduct a comparative review of the different works that elaborate on the recent progress in the application scenarios of the cyber security knowledge graph. Furthermore, a novel comprehensive classification framework is created to describe the connected works from nine primary categories and eighteen subcategories. Finally, we have a thorough outlook on several promising research directions based on the discussion of existing research flaws.

Published
2022

3. CNsum: Automatic Summarization for Chinese News Text

Author

Zhao, Yu, Huang, Songping, Zhou, Dongsheng, Ding, Zhaoyun, Wang, Fei, Nian, Aixin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Lei, editor, Segal, Michael, editor, Chen, Jenhui, editor, and Qiu, Tie, editor

Published
2022
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4. Research on NER Based on Register Migration and Multi-task Learning

Author

Ma, Haoran, Ding, Zhaoyun, Zhou, Dongsheng, Wang, Jinhua, Niu, ShuoShuo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Lei, editor, Segal, Michael, editor, Chen, Jenhui, editor, and Qiu, Tie, editor

Published
2022
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5. A General Personality Analysis Model Based on Social Posts and Links

Author

Ma, Xingkong, Qiu, Houjie, Yao, Shujia, Chen, Xinyi, Zhang, Jingsong, Ding, Zhaoyun, Li, Shaoyong, Liu, Bo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Khanna, Sankalp, editor, Cao, Jian, editor, Bai, Quan, editor, and Xu, Guandong, editor

Published
2022
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6. Implementing Large-Scale ABox Materialization Using Subgraph Reasoning

Author

Zhu, Xixi, Lin, Bin, Ding, Zhaoyun, Yao, Li, Zhu, Cheng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Memmi, Gerard, editor, Yang, Baijian, editor, Kong, Linghe, editor, Zhang, Tianwei, editor, and Qiu, Meikang, editor

Published
2022
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7. OTE: An Optimized Chinese Short Text Matching Algorithm Based on External Knowledge

Author

Ma, Haoyang, Ding, Zhaoyun, Li, Zeyu, Guo, Hongyu, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Memmi, Gerard, editor, Yang, Baijian, editor, Kong, Linghe, editor, Zhang, Tianwei, editor, and Qiu, Meikang, editor

Published
2022
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10. Drug-Target Interaction Prediction Based on Gaussian Interaction Profile and Information Entropy

Author

Liu, Lina, Yao, Shuang, Ding, Zhaoyun, Guo, Maozu, Yu, Donghua, Hu, Keli, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wei, Yanjie, editor, Li, Min, editor, Skums, Pavel, editor, and Cai, Zhipeng, editor

Published
2021
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11. A Semantic Textual Similarity Calculation Model Based on Pre-training Model

Author

Ding, Zhaoyun, Liu, Kai, Wang, Wenhao, Liu, Bin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Qiu, Han, editor, Zhang, Cheng, editor, Fei, Zongming, editor, Qiu, Meikang, editor, and Kung, Sun-Yuan, editor

Published
2021
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12. Predicting the Popularity of Topics based on User Sentiment in Microblogging Websites

Author

Wang, Xiang, Wang, Chen, Ding, Zhaoyun, Zhu, Min, and Huang, Jiumin

Subjects
Computer Science - Social and Information Networks
Abstract

Behavioral economics show us that emotions play an important role in individual behavior and decision-making. Does this also affect collective decision making in a community? Here we investigate whether the community sentiment energy of a topic is related to the spreading popularity of the topic. To compute the community sentiment energy of a topic, we first analyze the sentiment of a user on the key phrases of the topic based on the recent tweets of the user. Then we compute the total sentiment energy of all users in the community on the topic based on the Markov Random Field (MRF) model and graph entropy model. Experiments on two communities find the linear correlation between the community sentiment energy and the real spreading popularity of topics. Based on the finding, we proposed two models to predict the popularity of topics. Experimental results show the effectiveness of the two models and the helpful of sentiment in predicting the popularity of topics. Experiments also show that community sentiment affects collective decision making of spreading a topic or not in the community., Comment: Journal of Intelligent Information Systems

Published
2017

13. BVTED: A Specialized Bilingual (Chinese–English) Dataset for Vulnerability Triple Extraction Tasks.

Author

Liu, Kai, Wang, Yi, Ding, Zhaoyun, Li, Aiping, and Zhang, Weiming

Subjects
CYBERTERRORISM, DATABASES, DATA mining, INTERNET security, ONTOLOGY
Abstract

Extracting knowledge from cyber threat intelligence is essential for understanding cyber threats and implementing proactive defense measures. However, there is a lack of open datasets in the Chinese cybersecurity field that support both entity and relation extraction tasks. This paper addresses this gap by analyzing vulnerability description texts, which are standardized and knowledge-dense, to create a vulnerability knowledge ontology comprising 13 entities and 15 relations. We annotated 27,311 unique vulnerability description sentences from the China National Vulnerability Database, resulting in a dataset named BVTED for cybersecurity knowledge triple extraction tasks. BVTED contains 97,391 entities and 69,614 relations, with entities expressed in a mix of Chinese and English. To evaluate the dataset's value, we trained five deep learning-based named entity recognition models, two relation extraction models, and two joint entity–relation extraction models on BVTED. Experimental results demonstrate that models trained on this dataset achieve excellent performance in vulnerability knowledge extraction tasks. This work enhances the extraction of cybersecurity knowledge triples from mixed Chinese and English threat intelligence corpora by providing a comprehensive ontology and a new dataset, significantly aiding in the mining, analysis and utilization of the knowledge embedded in cyber threat intelligence. [ABSTRACT FROM AUTHOR]

Published
2024
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14. GLP‐1 receptor agonists alleviate colonic inflammation by modulating intestinal microbiota and the function of group 3 innate lymphoid cells.

Author

Sun, Hanxiao, Shu, Jie, Tang, Jupei, Li, Yue, Qiu, Jinxin, Ding, Zhaoyun, Xuan, Binbin, Chen, Minghui, Gan, Chenxin, Lin, Jinpiao, Qiu, Ju, Sheng, Huiming, and Wang, Chuanxin

Subjects
INNATE lymphoid cells, GUT microbiome, GLUCAGON-like peptide-1 receptor, INFLAMMATORY bowel diseases, GLUCAGON-like peptide-1 agonists
Abstract

Glucagon‐like peptide‐1 receptor agonists (GLP‐1RAs), which are drugs used for treating type 2 diabetes, have been reported to exert anti‐inflammatory effects on inflammatory bowel disease (IBD), the mechanism of which remains elusive. Here, we report that GLP‐1RAs ameliorate dextran sulfate sodium (DSS)‐induced colitis in both wild‐type and T/B‐cell‐deficient mice through modulating group 3 innate lymphoid cells (ILC3s), a subset of innate lymphoid cells that regulate intestinal immunity. GLP‐1RAs promote IL‐22 production by ILC3, and the protective effect of GLP‐1RAs on DSS‐induced colitis was abrogated in ILC3‐deficient RORgtgfp/gfp mice. Furthermore, the treatment effect of GLP‐RAs on colitis, as well as the generation of IL‐22‐producing ILC3s by GLP‐RAs, is dependent on the gut microbiota. GLP‐1RAs increase the abundance of Firmicutes and Proteobacteria in the gut, particularly beneficial bacteria such as Lactobacillus reuteri, and decrease the abundance of enteropathogenic Staphylococcus bacteria. The untargeted gas chromatography (GC)/liquid chromatography (LC)‐mass spectrometry (MS) of faecal metabolites further revealed enrichment of N,N‐dimethylsphingosine (DMS), an endogenous metabolite derived from sphingosine, in the GLP‐1RA‐treated group. Strikingly, DMS ameliorates colitis while promoting intestinal IL‐22‐producing ILC3s. Taken together, our findings show that GLP‐1RAs exert a therapeutic effect on colitis possibly by regulating the microbiota‐DMS‐IL‐22+ILC3 axis, highlighting the potential beneficial role of GLP‐RAs in inflammatory intestinal disorders with diabetes complications. [ABSTRACT FROM AUTHOR]

Published
2024
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20. Multi-Relation Extraction for Cybersecurity Based on Ontology Rule-Enhanced Prompt Learning.

Author

Wang, Fei, Ding, Zhaoyun, Liu, Kai, Xin, Lehai, Zhao, Yu, and Zhou, Yun

Subjects
REDUNDANCY in engineering, INTERNET security, ONTOLOGY, JUDGMENT (Psychology), DESIGN templates, CYBERSPACE
Abstract

In the domain of cybersecurity, available annotated data are often scarce, especially for Chinese cybersecurity datasets, often necessitating the manual construction of datasets. The scarcity of samples is one of the challenges in researching cybersecurity, especially for the "no-relation" class. Since the annotation process typically focuses only on known relation classes, there are usually no training samples for the "no-relation" class. This poses a zero-shot classification problem, where during the classification process, there is a tendency to classify into a class with a relationship. Zero-shot classification tasks are particularly challenging in this context. Moreover, most relation classification models currently need to traverse all relations to calculate the class with the highest probability. Therefore, the problem of "computational redundancy" is another challenge faced. Thus, how to accurately and efficiently acquire cyberspace knowledge from heterogeneous data sources and address the challenges such as sample scarcity, zero-shot recognition, and computational redundancy is the main focus of this chapter. To address these problems, this chapter designs a multi-relation extraction model based on ontology rule-enhanced prompt learning, which is a parameter-sharing-based multi-task model. By introducing prompt learning, which has shown significant effectiveness in the few-shot domain, this chapter designs prompt templates combining discrete and continuous tokens and uses rule injection in prompt learning to solve the difficulties in zero-shot recognition of "no-relation" and computational redundancy issues, achieving efficient and accurate multi-relation extraction. Specifically, by constructing sub-prompts to achieve an efficient combination of templates, a parameter-sharing structure is used to implement knowledge extraction step by step: The first step constructs entity prompt templates combining discrete and continuous tokens, identifying the classes of two entities based on prompt learning. The second step involves rule injection, identifying whether it belongs to the "no-relation" class based on the combination of sub-prompts; if there is no connection between the classes of two entities, it is classified as "no relation"; if a connection exists, the candidate relation set is filtered out. The third step uses the pre-trained model and vectors from the first step, utilizing prompt learning and rule judgment to determine the relation class from the candidate relation set. Finally, the effectiveness of our model is validated on the general datasets TACRED, ReTACRED, and the cybersecurity dataset constructed in this paper. [ABSTRACT FROM AUTHOR]

Published
2024
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24. CNN-Based Sequence Labeling for Fine-Grained Opinion Mining of Microblogs

Author

Cheng, Jiajun, Li, Pei, Zhang, Xin, Ding, Zhaoyun, Wang, Hui, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Kang, U, editor, Lim, Ee-Peng, editor, Yu, Jeffrey Xu, editor, and Moon, Yang-Sae, editor

Published
2017
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25. Confirmatory Analysis on Influencing Factors When Mention Users in Twitter

Author

Li, Yueyang, Ding, Zhaoyun, Zhang, Xin, Liu, Bo, Zhang, Weice, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Morishima, Atsuyuki, editor, Zhang, Rong, editor, Zhang, Wenjie, editor, Chang, Lijun, editor, Fu, Tom Z. J, editor, Liu, Kuien, editor, Yang, Xiaoyan, editor, Zhu, Jia, editor, and Zhang, Zhiwei, editor

Published
2016
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26. Mentioning the Optimal Users in the Appropriate Time on Twitter

Author

Ding, Zhaoyun, Zou, Xueqing, Li, Yueyang, He, Su, Cheng, Jiajun, Qiao, Fengcai, Wang, Hui, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Li, Feifei, editor, Shim, Kyuseok, editor, Zheng, Kai, editor, and Liu, Guanfeng, editor

Published
2016
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27. Behavior-Based Twitter Overlapping Community Detection

Author

Guo, Lixiang, Ding, Zhaoyun, Wang, Hui, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Gao, Hong, editor, Kim, Jinho, editor, and Sakurai, Yasushi, editor

Published
2016
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28. Finding the Optimal Users to Mention in the Appropriate Time on Twitter

Author

Shen, Dayong, Ding, Zhaoyun, Qiao, Fengcai, Cheng, Jiajun, Wang, Hui, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Lehner, Franz, editor, and Fteimi, Nora, editor

Published
2016
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29. Collaborative Model for Predicting Retweeting Behaviors on Twitter

Author

Guo, Liang, Ding, Zhaoyun, Zhang, Sheng, Li, Taowei, Jiang, Weiwei, Wang, Hui, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Cai, Ruichu, editor, Chen, Kang, editor, Hong, Liang, editor, Yang, Xiaoyan, editor, Zhang, Rong, editor, and Zou, Lei, editor

Published
2015
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30. Finding Influential Users and Popular Contents on Twitter

Author

Ding, Zhaoyun, Wang, Hui, Guo, Liang, Qiao, Fengcai, Cao, Jianping, Shen, Dayong, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Wang, Jianyong, editor, Cellary, Wojciech, editor, Wang, Dingding, editor, Wang, Hua, editor, Chen, Shu-Ching, editor, Li, Tao, editor, and Zhang, Yanchun, editor

Published
2015
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36. An Influence Strength Measurement via Time-Aware Probabilistic Generative Model for Microblogs

Author

Ding, Zhaoyun, Jia, Yan, Zhou, Bin, Zhang, Jianfeng, Han, Yi, Yu, Chunfeng, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ishikawa, Yoshiharu, editor, Li, Jianzhong, editor, Wang, Wei, editor, Zhang, Rui, editor, and Zhang, Wenjie, editor

Published
2013
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37. Mining Tribe-Leaders Based on the Frequent Pattern of Propagation

Author

Ding, Zhaoyun, Jia, Yan, Zhou, Bin, Han, Yi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wang, Hua, editor, Zou, Lei, editor, Huang, Guangyan, editor, He, Jing, editor, Pang, Chaoyi, editor, Zhang, Hao Lan, editor, Zhao, Dongyan, editor, and Yi, Zhuang, editor

Published
2012
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