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Personalized Academic Communication Recommendation System Based on knowledge Graph 5

Personalized Academic Communication Recommendation System Ba...

引用

5th international conference on Electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Liu, Jiajia Luan, Meilin Jiang, Ning School of Information Engineering Wuhan University of Technology Wuhan China Wuhan University of Technology International Office Wuhan China

ISBN: (纸本)9798350386943

Scholar retrieval resources are faced with the problems of information overload, low efficiency, and low matching degree. To address this issue, we developed a personalized academic resource recommendation system based on the knowledge graph. The system consists of a data layer, a policy layer, and an application layer, which include four main functional modules: resource storage module, knowledge description module, wisdom recommendation module, and user management module. The back-end of the system utilizes the Spring Boot, Spring MVC, and MyBatis frameworks, while the front-end communicates with the back end via HTTP requests. The system database primarily utilizes MySQL and Neo4j. The system integrates knowledge graph and natural language processing technology to recommend high-quality academic resources for scholars. © 2024 IEEE.

关键词： knowledge graph

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Generating Multiple Choice Questions from Scientific Literature via Large language Models 15

Generating Multiple Choice Questions from Scientific Literat...

引用

15th IEEE international conference on knowledge Graph, ICKG 2024

作者： Luo, Shunyang Tang, Yuqi Jiang, Mingyuan Feng, Kehua Zhang, Qiang Ding, Keyan Zhejiang University - University of Illinois Urbana-Champaign Institute Zhejiang University China Zhejiang University College of Computer Science and Technology China Zhejiang University ZJU-Hangzhou Global Scientific and Technological Innovation Center China

ISBN: (纸本)9798331508821

The rapid development of Large language Models (LLMs) has significantly advanced the field of natural language processing, including the automated generation of Multiple-Choice Questions (MCQs) from scientific literature. This study introduces a systematic method for creating high-quality MCQs using advanced LLMs. We developed a specialized dataset by extracting data from extensive literature within the domain of materials science, focusing on five critical tasks: common science knowledge Q&A, digital data extraction, detailed understanding, reasoning and interpretation, and safety judgments. Leveraging carefully designed prompts of LLMs, we automated the generation process and conducted validation of MCQs to ensure their relevance and accuracy. The resulting dataset not only demonstrates the potential of LLMs in producing diverse MCQs but also serves as a benchmark for evaluating the problem-solving capabilities of different LLMs in materials science. Our experimental results reveal the strengths and weaknesses of these LLMs, providing valuable insights for future applications in science. © 2024 IEEE.

关键词： Data mining

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AI Assisted Domain Modeling Explainability and Traceability 24

AI Assisted Domain Modeling Explainability and Traceability

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ACM/IEEE 27th international conference on Model Driven engineering languages and Systems: Companion Proceedings (MODELS)

作者： Silva, Jonathan Univ Luxembourg Belval Esch Sur Alzett Luxembourg

ISBN: (纸本)9798400706226

Domain Models are abstract representations of selected elements in a domain that is created in a collaborative process between domain and modeler experts. The participants share domain knowledge to conceptualize and reason about the elements that will create the domain models. Through this exchange, a comprehensive and accurate representation of the domain is achieved, ensuring that the model captures the relevant aspects and relationships in the domain. Research in Artificial Intelligence (AI) has explored various methods to assist in the creation of domain models from text using natural language processing (NLP) and Machine Learning (ML). Recent advancements with Large language Models (LLMs) have shown that it is possible to create domain models using prompting techniques;however, the generated domain models contain errors and remain constrained by the performance of the LLM used. Despite the impressive capabilities of LLMs to create domain models, it is evident that it does not address the needs of domain and modelers experts that participate in the creation of domain models. Every AI technique has its advantages and limitations that must be integrated with human feedback in a collaboration process. Therefore, we propose an approach that incorporates human-AI collaboration supported by AI assistants that follows a dialogue approach to understand the users needs and purpose to suggest relevant models. Our proposal combines symbolic and subsymbolic AI techniques with explainability and traceability of the decisions that assist to create domain models that are relevant for the users.

关键词： Domain Modeling Large language Models Uncertainty Explainability Traceability

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Extracting Queryable knowledge Graphs from User Stories: An Empirical Evaluation 18

Extracting Queryable Knowledge Graphs from User Stories: An ...

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18th international conference on Evaluation of Novel Approaches to Software engineering (ENASE)

作者： Ladeinde, Ayodeji Arora, Chetan Khalajzadeh, Hourieh Kanij, Tanjila Grundy, John Monash Univ Dept Software Syst & Cybersecur Melbourne Vic Australia Deakin Univ Sch Info Technol Melbourne Burwood Campus Melbourne Vic Australia

ISBN: (纸本)9789897586477

User stories are brief descriptions of a system feature told from a user's point of view. During requirements elicitation, users and analysts co-specify these stories using natural language. A number of approaches have tried to use natural language processing (NLP) techniques to extract different artefacts, such as domain models and conceptual models, and reason about software requirements, including user stories. However, large collections of user story models can be hard to navigate once specified. We extracted different components of user story data, including actors, entities and processes, using NLP techniques and modelled them with graphs. This allows us to organise and link the structures and information in user stories for better analysis by different stakeholders. Our NLP-based automated approach further allows the stakeholders to query the model to view the parts of multiple user stories of interest. This facilitates project development discussions between technical team members, domain experts and users. We evaluated our tool on user story datasets and through a user study. The evaluation of our approach shows an overall precision above 96% and a recall of 100%. The user study with eight participants showed that our querying approach is beneficial in practical contexts.

关键词： Requirements engineering Modeling User Stories natural language processing Querying

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Research on I-PageRank algorithm model of Process knowledge graph based on K-Shell decomposition algorithm 5

Research on I-PageRank algorithm model of Process knowledge ...

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5th international conference on natural language processing, ICNLP 2023

作者： Huo, Yanwei Cheng, Hongyu Xi'an University of Posts and Telecommunications School of Computing Xi'an710121 China Xidian University School of Electro-Mechanical Engineering Xi'an710121 China

ISBN: (纸本)9798350302219

PageRank algorithm in the calculation of nodes is equally distributed to the node chain of all nodes, but in the actual production of manufacturing enterprises, the importance of process knowledge in process documents is different, if according to the PageRank algorithm PR value equal transfer to calculate the importance of the artifact, efficiency and accuracy is generally low, so the importance of PR value transfer difference should be considered. Therefore, this paper introduces K-Shell decomposition algorithm in PageRank algorithm, constructs a new I-PageRank algorithm model, adding the importance of each node in the linked network to the PageRank algorithm, which improves the efficiency and accuracy of PageRank algorithm in identifying key nodes. © 2023 IEEE.

关键词： Efficiency

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knowledge Graphs for Textbooks: Extraction and Completion Techniques 6

Knowledge Graphs for Textbooks: Extraction and Completion Te...

引用

6th IEEE international conference on Artificial Intelligence and knowledge engineering, AIKE 2023

作者： Yao, Yutong Potikas, Petros Potika, Katerina Department of Computer Science San José State University San JoseCA United States School of Electrical and Computer Engineering National Technical University of Athens Greece

ISBN: (纸本)9798350331288

This paper aims to apply knowledge graph construction techniques to textbooks, explicitly focusing on the challenge of the absence of domain-specific schema for each textbook. Various entity and relation extraction models are utilized to capture logical and semantic information related to the textbook s topic. These models include a Text-Encoding-Initiative (TEI) model to extract hierarchical concepts, spaCy natural language processing (NLP), and Google Cloud natural language to extract semantic information from the main textual content. The study includes a case study on a cloud computing textbook, where each approach is evaluated and analyzed. Ultimately, the goal is to create knowledge graphs of textbooks, enabling the completion task of predicting missing entities or relations in a low-dimensional space. © 2023 IEEE.

关键词： knowledge graph

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Enhancing Communication and Data Transmission Security in RAG Using Large language Models 4

Enhancing Communication and Data Transmission Security in RA...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Gummadi, Venkata Udayaraju, Pamula Sarabu, Venkata Rahul Ravulu, Chaitanya Seelam, Dhanunjay Reddy Venkataramana, S. Expedia Group Austin United States School of Engineering and Sciences Srm University-AP Department of Cse India Wallmart Global Tech TX United States Rocket Companies MI United States Wallmart Global Tech BentonvilleAR United States Srkr Engineering College Department of It Bhimavaram India

ISBN: (纸本)9798331540364

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources, enabling more useful information and generating accurate responses. This paper explores RAG's architecture and applications, combining generator and retriever models to access and utilize vast external data repositories. While RAG holds significant promise for various natural language processing (NLP) processes like dialogue generation, summarization, and question answering, it also presents unique security challenges that must be addressed to ensure system integrity and reliability. RAG systems face several security threats, including data poisoning, model manipulation, privacy leakage, biased information retrieval, and harmful outputs generation. Generally, in the traditional RAG application, security threat is one of the major concerns. To tighten the security system and enhance the efficiency of the model on processing more complex data this paper outlines key strategies for securing RAG-based applications to mitigate these risks paper outlines key strategies for securing RAG-based applications to mitigate these risks. Ensuring data security through filtering, sanitization, and provenance tracking can prevent data poisoning and enhance the quality of external knowledge sources. Strengthening model security via adversarial training, input validation, and anomaly detection improves resilience against manipulative attacks. Implementing output monitoring and filtering techniques, such as factual verification, language moderation, and bias detection, ensures the accuracy and safety of generated responses. Additionally, robust infrastructure and access control measures, including secure data storage, secure APIs, and regulated model access, protect against unauthorized access and manipulation. Moreover, this study analyzes various use cases for LLMs enhanced by RAG, including personalized recommendations, customer support automation, content creation,

关键词： natural language processing systems

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LlmRe: A zero-shot entity relation extraction method based on the large language model 7

LlmRe: A zero-shot entity relation extraction method based o...

引用

7th international conference on Electronic Information Technology and Computer engineering (EITCE)

作者： Zhao, Wei Chen, Qinghui You, Junling Zhengzhou Univ Sch Cyber Sci & Engn Zhengzhou Peoples R China

ISBN: (纸本)9798400708305

Entity relation extraction aims to extract knowledge triples from unstructured or semi-structured text data and can be applied to various fields, including medicine, finance knowledge graph construction and intelligent question-answering. Traditional entity relation extraction requires a large amount of labeled data, consumes a lot of labor and time, and the trained model lacks generalization ability, which is difficult to migrate to other fields. Zero-shot entity relation extraction relieves the dependence on labeled data in traditional method. Based on unlabeled text data, zero-shot entity relation extraction has strong domain adaptability, which is a very challenging and practical task. Recent work on large language models shows that large models can effectively complete downstream tasks through natural language instructions and have good generalization ability. Inspired by this, we explore the use of large models for information extraction. Due to the randomness of large language model generation, we introduce in-context learning in entity relation extraction task to guide large language model to output data in a specified format to help obtain structured data. At the same time, we propose a three-stage extraction framework for decomposing entity relation extraction tasks, and each stage is conducted in the form of question and answer to reduce the complexity of extraction. We evaluated the knowledge triples extraction performance of the model on three self-built test datasets in different fields, and the experimental result showed that our proposed method achieved impressive performance in the zero-shot entity relation extraction task, surpassing the comparison model on multiple metrics, proving the effectiveness and domain adaptability of the proposed method.

关键词： natural language processing Entity relation extraction Large language model Information extraction In-context learning mutistage framework

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3D-EX: A Unified Dataset of Definitions and Dictionary Examples

3D-EX: A Unified Dataset of Definitions and Dictionary Examp...

引用

2023 international conference Recent Advances in natural language processing: Large language Models for natural language processing, RANLP 2023

作者： Almeman, Fatemah Sheikhi, Hadi Espinosa-Anke, Luis CardiffNLP School of Computer Science and Informatics Cardiff University United Kingdom College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia School of Computer Engineering Iran University of Science and Technology Iran AMPLYFI United Kingdom

ISBN: (纸本)9789544520922

Definitions are a fundamental building block in lexicography, linguistics and computational semantics. In NLP, they have been used for retrofitting word embeddings or augmenting contextual representations in language models. However, lexical resources containing definitions exhibit a wide range of properties, which has implications in the behaviour of models trained and evaluated on them. In this paper, we introduce 3D-EX, a dataset that aims to fill this gap by combining well-known English resources into one centralized knowledge repository in the form of triples. 3D-EX is a unified evaluation framework with carefully pre-computed train/validation/test splits to prevent memorization. We report experimental results that suggest that this dataset could be effectively leveraged in downstream NLP tasks. Code and data are available at https://***/F-Almeman/3D-EX. © 2023 Incoma Ltd. All rights reserved.

关键词： natural language processing systems

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A Lightweight Chinese Summarization Generation Method Based on knowledge Distillation 4

A Lightweight Chinese Summarization Generation Method Based ...

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4th international conference on Information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Liu, Shiyue Xiao, Yannan Wang, Xueyong Huang, Wenming Deng, Zhenrong School of Computer and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China School of Computer and Information Security Guilin University of Electronic Technology Guilin China Guilin Xintong Technology Co. Guilin China

ISBN: (纸本)9798350337181

In recent years, large-scale pretrained natural language processing models such as BERT, GPT3 have achieved good results in processing tasks. However, in daily applications, these large-scale language models usually exist large model size or long running time, and the model portability and application are not very convenient. To settle this problem, we come up with a lightweight summarization generation method based on knowledge distillation (LW-BERT-KD, abbreviated as LBK model) to prove that complex neural network knowledge (taking BERT model as an example) can be extracted to lightweight language processing models (taking BiLSTM model as an example), thus achieving the purpose of complex knowledge extraction and model compression. In this paper, the BERT model is regarded as the teacher model, and the BiLSTM is regarded as the student model. The knowledge distillation technology is the transfer of knowledge from teacher models to student models, so that the generated model is effective, lightweight, and easy to port. On the Chinese abstract generation dataset, the training effect of this model is significantly better than the basic Transformer baseline. The experimental results show that if the training parameters are increased by 100 times, the model achieves comparable results with complex preprocessing language models. © 2023 IEEE.

关键词： Distillation

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