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arXiv 2023年

作者： Fu, Dongqi Bao, Wenxuan Maciejewski, Ross Tong, Hanghang He, Jingrui University of Illinois Urbana-Champaign United States Arizona State University United States

In graph machine learning, data collection, sharing, and analysis often involve multiple parties, each of which may require varying levels of data security and privacy. To this end, preserving privacy is of great importance in protecting sensitive information. In the era of big data, the relationships among data entities have become unprecedentedly complex, and more applications utilize advanced data structures (i.e., graphs) that can support network structures and relevant attribute information. To date, many graph-based AI models have been proposed (e.g., graph neural networks) for various domain tasks, like computer vision and natural language processing. In this paper, we focus on reviewing privacy-preserving techniques of graph machine learning. We systematically review related works from the data to the computational aspects. We first review methods for generating privacy-preserving graph data. Then we describe methods for transmitting privacy-preserved information (e.g., graph model parameters) to realize the optimization-based computation when data sharing among multiple parties is risky or impossible. In addition to discussing relevant theoretical methodology and software tools, we also discuss current challenges and highlight several possible future research opportunities for privacy-preserving graph machine learning. Finally, we envision a unified and comprehensive secure graph machine learning system. Copyright © 2023, The Authors. All rights reserved.

关键词： Privacy-preserving techniques

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基于深度学习模型的文化景观基因命名实体识别

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地球信息科学学报 2025年第1期27卷 207-225页

作者：林洁如胡最衡阳师范学院地理与旅游学院衡阳421002 传统村镇文化数字化保护与创意利用技术国家地方联合工程实验室衡阳421002

【目的】针对当前传统聚落研究缺乏结合自然语言处理的原理与方法从海量文本数据中提取文化景观基因信息的相关探索,本文提出景观基因命名实体(Traditional Settlement Landscape Genes Named Entity,TSLGNE)的概念,并以邵阳市48个传统... 详细信息

【目的】针对当前传统聚落研究缺乏结合自然语言处理的原理与方法从海量文本数据中提取文化景观基因信息的相关探索,本文提出景观基因命名实体(Traditional Settlement Landscape Genes Named Entity,TSLGNE)的概念,并以邵阳市48个传统村落为案例,采用BERT-BiLSTM-CRF深度学习模型开展TSLGNE的识别研究。【方法】首先,结合地理实体特征与文化景观基因理论及其分类体系,提出TSLGNE的概念、分类体系与知识表达方法。其次,基于TSLGNE的分类体系与扩展BIOES标注方法,对研究案例源文本数据进行语料标注,构建了相应的语料库。随后,基于BERT-BiLSTM-CRF深度学习模型识别并提取语料数据中的TSLGNE信息。最后,通过Neo4j图数据库对获取到的TSLGNE知识进行组织与储存,并基于此对区域传统聚落及其TSLGNE进行空间特征分析。【结果】本文模型能够有效识别文本数据中的建筑、环境、文化等12类景观基因实体信息,总体精确率、召回率、F1值相较对比模型均有提升。与BiLSTM-CRF、BERT-CRF模型相比,本模型的F1值分别提高11%与1%。特别地,本模型对于语料数据质量差且语义复杂的实体的识别效果有极大提升,如文化基因C3类实体识别的F1较对比模型分别提高了31%与5%。【结论】此方法能够有效地对区域复杂的传统聚落文化基因特征及其关系进行空间分析处理,对今后结合GIS和数据挖掘方法分析传统聚落的重要文化特征及传统聚落知识服务等具有较好的借鉴意义。

关键词：传统聚落文化景观基因自然语言处理命名实体识别 BERT预训练语言模型 Neo4j图数据库知识图谱

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Unveiling Hidden Links Between Unseen Security Entities

arXiv

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arXiv 2024年

作者： Alfasi, Daniel Shapira, Tal Barr, Anat Bremler Reichman University Israel The Hebrew University of Jerusalem Israel Tel Aviv University Israel

The proliferation of software vulnerabilities poses a significant challenge for security databases and analysts tasked with their timely identification, classification, and remediation. With the National Vulnerability Database (NVD) reporting an ever-increasing number of vulnerabilities, the traditional manual analysis becomes untenably time-consuming and prone to errors. This paper introduces VulnScopper, an innovative approach that utilizes multi-modal representation learning, combining Knowledge graphs (KG) and natural language processing (NLP), to automate and enhance the analysis of software vulnerabilities. Leveraging ULTRA, a knowledge graph foundation model, combined with a Large language Model (LLM), VulnScopper effectively handles unseen entities, overcoming the limitations of previous KG approaches. We evaluate VulnScopper on two major security datasets, the NVD and the Red Hat CVE database. Our method significantly improves the link prediction accuracy between Common Vulnerabilities and Exposures (CVEs), Common Weakness Enumeration (CWEs), and Common Platform Enumerations (CPEs). Our results show that VulnScopper outperforms existing methods, achieving up to 78% Hits@10 accuracy in linking CVEs to CPEs and CWEs and presenting an 11.7% improvement over large language models in predicting CWE labels based on the Red Hat database. based on the NVD, only 6.37% of the linked CPEs are being published during the first 30 days;many of them are related to critical and high-risk vulnerabilities which, according to multiple compliance frameworks (such as CISA and PCI), should be remediated within 15-30 days. We provide an analysis of several CVEs published during 2023, showcasing the ability of our model to uncover new products previously unlinked to vulnerabilities. As such, our approach dramatically reduces the vulnerability remediation time and improves the vulnerability management process. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Exploring Text-to-Text Transformers for English to Hinglish Machine Translation with Synthetic Code-Mixing 5

Exploring Text-to-Text Transformers for English to Hinglish ...

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5th workshop on Computational Approaches to Linguistic Code-Switching, CALCS 2021

作者： Jawahar, Ganesh Billah Nagoudi, El Moatez Abdul-Mageed, Muhammad Lakshmanan, Laks V.S. Natural Language Processing Lab University of British Columbia Canada Department of Computer Science University of British Columbia Canada

ISBN: (纸本)9781954085459

We describe models focused at the understudied problem of translating between monolingual and code-mixed language pairs. More specifically, we offer a wide range of models that convert monolingual English text into Hinglish (code-mixed Hindi and English). Given the recent success of pretrained language models, we also test the utility of two recent Transformer-based encoder-decoder models (i.e., mT5 and mBART) on the task finding both to work well. Given the paucity of training data for code-mixing, we also propose a dependency-free method for generating code-mixed texts from bilingual distributed representations that we exploit for improving language model performance. In particular, armed with this additional data, we adopt a curriculum learning approach where we first finetune the language models on synthetic data then on gold code-mixed data. We find that, although simple, our synthetic code-mixing method is competitive with (and in some cases is even superior to) several standard methods (backtranslation, method based on equivalence constraint theory) under a diverse set of conditions. Our work shows that the mT5 model, finetuned following the curriculum learning procedure, achieves best translation performance (12.67 BLEU). Our models place first in the overall ranking of the English-Hinglish official shared task. ©2021 Association for Computational Linguistics

关键词： Curricula

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Spectral graph-based method of multimodal word embedding 11

Spectral graph-based method of multimodal word embedding

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11th workshop on graph-based methods for natural language processing, Textgraphs 2017, in conjunction with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017

作者： Fukui, Kazuki Oshikiri, Takamasa Shimodaira, Hidetoshi Department of Systems Science Graduate School of Informatics Kyoto University Japan Mathematical Statistics Team RIKEN Center for Advanced Intelligence Project Japan Division of Mathematical Science Graduate School of Engineering Science Osaka University Japan

ISBN: (纸本)9781945626609

In this paper, we propose a novel method for multimodal word embedding, which exploit a generalized framework of multi-view spectral graph embedding to take into account visual appearances or scenes denoted by words in a corpus. We evaluated our method through word similarity tasks and a concept-to-image search task, having found that it provides word representations that reflect visual information, while somewhat trading-off the performance on the word similarity tasks. Moreover, we demonstrate that our method captures multimodal linguistic regularities, which enable recovering relational similarities between words and images by vector arithmetic. © 2017 Association for Computational Linguistics

关键词： Embeddings

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Multi-channel graph Convolution for Aspect-level Sentiment Classification of Online Student Reviews 22

Multi-channel Graph Convolution for Aspect-level Sentiment C...

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proceedings of the 2022 5th International Conference on Machine Learning and natural language processing

作者： Churan Cao Peng Han Jian Qiu Dongmei Liu Li Peng Kaiqing Luo School of Physics and Telecommunications Engineering South China Normal University China School of Electronics and Information Engineering South China Normal University China

ISBN: (纸本)9781450399067

Abstract—It is important for schools and online learning platforms to mine student attitudes and opinions from student reviews so teachers know what needs to be improved. Previous studies have only focused on the semantic features of sentences, ignoring syntactic dependency relations in sentences. To address this problem, we build a graph convolutional network (GCN) on the syntactic dependency tree of sentences to exploit syntactic information for the first time in the field of online student reviews. Furthermore, in order to exploit positional relations and alleviate over-smoothing, we add relative aspect distance relations and stochastic edge elimination. On this basis, a novel multi-channel graph convolution model (MCGCN) is proposed, which constructs three GCN channels for syntactic dependency relations, relative aspect distance relations and stochastic edge elimination to extract relational features. Finally we use average pooling to fuse three relational features. Experiments on the CR23k dataset demonstrate that the overall performance of the model outperforms the state-of-the-art methods.

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SCMGR: Using Social Context and Multi-Granularity Relations for Unsupervised Social Summarization 21

SCMGR: Using Social Context and Multi-Granularity Relations ...

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30th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Liu, Huanyu He, Ruifang Zhao, Liangliang Wang, Haocheng Wang, Ruifang Peoples Daily Online China State Key Lab Commun Content Cognit Beijing Peoples R China Tianjin Univ China Coll Intelligence & Comp Tianjin Peoples R China

ISBN: (纸本)9781450384469

Social summarization aims to produce a concise summary that describes the core content of a collection of posts on a specific topic. Existing methods tend to produce sparse or ambiguous representations of posts due to only using short and informal text content. Latest researches use social relations to improve diversity of summaries, yet they model social relations as a regularization item, which has poor flexibility and generalization. Those methods could not embody the deep semantic and social interactions among posts, making summaries still suffer from redundancy. We propose to use Social Context and Multi-Granularity Relations (SCMGR) to improve unsupervised social summarization. It learns more informative representations of posts considering both text semantics and social structure information without any annotated data. first, we design two sociologically motivated meta-paths to construct a social context graph among posts, and adopt a graph convolutional network to aggregate social context information from neighbors. Second, we design a multi-granularity relation decoder to capture the deeper semantic and social interactions from post-word and post-post aspects respectively, which can provide guidance for summary selection from semantic and social structure perspectives. Finally, a sparse reconstruction-based extractor is used to select posts that can best reconstruct original content and social network structure as summaries. Our approach improves the coverage and diversity of summaries. Experimental results on both English and Chinese corpora prove the effectiveness of our model.

关键词： Social Summarization Social Network Analysis graph Convolutional Networks natural language processing

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图神经网络综述

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计算机研究与发展 2022年第1期59卷 47-80页

作者：马帅刘建伟左信中国石油大学(北京)信息科学与工程学院北京102249

近几年来,将深度学习应用到处理和图结构数据相关的任务中越来越受到人们的关注.图神经网络的出现使其在上述任务中取得了重大突破,比如在社交网络、自然语言处理、计算机视觉甚至生命科学等领域得到了非常广泛的应用.图神经网络可以把... 详细信息

近几年来,将深度学习应用到处理和图结构数据相关的任务中越来越受到人们的关注.图神经网络的出现使其在上述任务中取得了重大突破,比如在社交网络、自然语言处理、计算机视觉甚至生命科学等领域得到了非常广泛的应用.图神经网络可以把实际问题看作图中节点之间的连接和消息传播问题,对节点之间的依赖关系进行建模,从而能够很好地处理图结构数据.鉴于此,系统综述了图神经网络模型以及应用.首先从谱域、空间域和池化3方面对图卷积神经网络进行了阐述.然后,描述了基于注意力机制和自编码器的图神经网络模型,并补充了一些其他方法实现的图神经网络.其次,总结了针对图神经网络能不能做大做深等问题的讨论分析.进而,概括了图神经网络的4个框架.还详细说明了在图神经网络在自然语言处理、计算机视觉等方面的应用.最后,对图神经网络未来的研究进行了展望和总结.相较于已有的图神经网络综述文章,详细阐述了谱理论知识,并对基于谱域的图卷积神经网络体系进行全面总结.同时,给出了针对空间域图卷积神经网络效率低的改进模型这一新的分类标准.并总结了针对图神经网络表达能力、理论保障等的讨论分析,增加了新的框架模型.在应用部分,阐述了图神经网络的最新应用.

关键词：图结构数据图神经网络图卷积神经网络谱域空间域和池化

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A Conversational Framework for Semantic Question Answering in Customer Services with Machine Learning on Knowledge graph 1

A Conversational Framework for Semantic Question Answering i...

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1st Italian workshop on Artificial Intelligence and Applications for Business and Industries, AIABI 2021

作者： Giorgio, Lazzarinetti Nicola, Massarenti Noovle S.p.A Milan Italy

Despite the recent advances in natural language processing (NLP) techniques many issues and inefficiencies arise when it comes to creating a system capable of interacting with the users by means of text conversations. The current techniques rely on the development of chatbots that, however, require designing the conversation flow, defining training questions and associating the expected responses. Even though this process allows the creation of effective question-answering systems, this methodology is not scalable, especially when the answers are to be found in documents. Other approaches, instead, rely on graph embedding techniques and graph neural networks to define the best answer given a question. These methods, however, require set-up training routines and to dispose of the ground truth that, in general, is difficult to retrieve or create for real industrial applications. In this paper we introduce a conversational framework for semantic question answering. Our work relies on knowledge graphs and the use of machine learning for determining the best answer given a question associated with the content of the knowledge graph. In addition, by leveraging text mining techniques we are able to identify the best set of answers that suit the question that are further filtered by means of deep learning algorithms. Copyright © 2021 for this paper by its authors.

关键词： Semantics

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Weak-PMLC: A large-scale framework for multi-label policy classification based on extremely weak supervision

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INFORMATION processing & MANAGEMENT 2023年第5期60卷

作者： Zhao, Jiufeng Song, Rui Yue, Chitao Wang, Zhenxin Xu, Hao Jilin Univ Sch Artificial Intelligence Changchun 130012 Peoples R China Jilin Propitious Cloud Informat Technol Co Ltd Changchun 130041 Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Chongqing Res Inst Chongqing 401123 Peoples R China

With the development of e-government, multiple local governments in China are developing Internet-based open policy platforms, and these online platforms need to automatically classify policies. Current policy classification methods are usually supervised models that require massive amounts of annotated policies, which can be expensive and difficult to obtain in practice. To alleviate the burden of human experts annotating a large number of policies, we propose a large-scale framework (Weak-PMLC) for multi-label policy classification based on extremely weak supervision, which does not rely on any labeled documents and uses only the label names of each category. Specifically, we first pre-train a language model (LM) on a given dataset to extend the LM from general to domain-specific. We then utilize the domain -specific LM to generate seed words semantically related to label names. Finally, following the category-related seed words, we generate massive pseudo-labeled policies as training data for high-performance supervised models. To verify the effectiveness of our proposed method, we created two new human-labeled datasets containing about 56k and 37k policies, respectively. We also define 59 label names, which are several key feature words used to summarize all collected policies. We show that Weak-PMLC achieves around 90% F1-scores on these two datasets and improves performance by 4% over state-of-the-art weakly supervised methods. Further experiments show that the proposed Weak-PMLC is even comparable to some supervised models.

关键词： natural language processing Weak supervision Policy text classification Domain-specific pre-training

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