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From natural language Texts to RDF Triples: A Novel Approach to Generating e-Commerce knowledge Graphs 14th

From Natural Language Texts to RDF Triples: A Novel Approac...

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14th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2022

作者： Regino, André Gomes Caus, Rodrigo Oliveira Hochgreb, Victor dos Reis, Julio Cesar São Paulo Brazil GoBots Campinas São Paulo Brazil Nucleus of Informatics Applied to Education University of Campinas SP Campinas Brazil

ISBN: (纸本)9783031434709

The use of knowledge Graphs (KGs) has gained traction in various software systems to improve data handling. E-commerce platforms, for example, have leveraged KGs to perform a range of tasks, including advertisement and product recommendation. However, generating accurate, trustworthy, and scalable RDF triples for populating KGs remains a challenging and error-prone task, especially given the lack of accurate and complete data. This article presents the QART framework, a natural language processing-based approach for generating RDF triples from e-commerce product Q &A. Our QART framework leverages templates to extract entities and intents and generates summarized sentences useful to populate KGs. Our experimental results demonstrate results concerning how we fine-tuned and used few-shots prompts in models such as T5, PTT5, GPT Neo and Bloom in an e-commerce dataset for the summarization task. Our evaluations identified key challenges in building the framework. Our contribution paves the way for the development of automatic mechanisms for text-to-triple transformation in e-commerce systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electronic commerce

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Enhancing Misinformation Detection through Semantic Analysis and knowledge Graphs

Enhancing Misinformation Detection through Semantic Analysis...

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Data engineering and Communication Systems (ICDECS), international conference on

作者： Sanjaikanth E Vadakkethil Somanathan Pillai School of Electrical Engineering and Computer Science University Of North Dakota Grand Forks USA

Introducing improvements in technology has caused an exponential increase in the availability and consumption of virtual data. This has also given rise to the spread of misinformation, a problem that has significant consequences for both individuals and society. The ability to correctly locate and combat misinformation is vital in preventing its dangerous outcomes. Traditional methods of detecting misinformation, including truth-checking and source evaluation, have proven to be inadequate in the contemporary digital landscape. To address this problem, researchers have turned to advanced technology, including natural language processing and machine learning, to improve the accuracy and performance of misinformation detection. However, these techniques often depend on surface-level features, such as language patterns and metadata, which can be easily manipulated by those creating and disseminating misinformation. This quandary requires a deeper understanding of the underlying semantic meaning of the facts. Semantic analysis, a branch of natural language processing, focuses on understanding the meaning and context of language. By incorporating this method into misinformation detection, it is possible to go beyond surface-level features and analyze the underlying intent and meaning of the information. This could offer a much better and accurate approach for detecting misinformation. Moreover, the use of knowledge graphs can further enhance this approach by capturing and representing the relationships between entities, ideas, and information.

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AI-Enhanced Cross-Modal Anime Recommendation System with Explainable Deep Learning

AI-Enhanced Cross-Modal Anime Recommendation System with Exp...

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IT Innovation and knowledge Discovery (ITIKD), international conference on

作者： Jayabhaduri Radhakrishnan H. Naga R. Guna Vardhan Akey Sungheetha K. Dinesh Kumar Reddy K. Danush Kumar B. Charan Reddy Department of Computer Science and Engineering Alliance University Bengaluru India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

This paper presents an innovative approach to anime recommendation systems by integrating multi-modal deep learning with explainable AI techniques. We propose a novel framework that combines visual features, textual content, and user interaction data to create more accurate and interpretable recommendations. Our system addresses key challenges in ex-isting recommendation systems, including the cold-start problem and limited content understanding, through a hybrid architecture that leverages BERT-based natural language processing and convolutional neural networks for visual analysis. Experimental results demonstrate a 27% improvement in recommendation accuracy compared to traditional methods, while providing transparent explanations for recommendations through attention visualization.

关键词： Deep learning Visualization Technological innovation Accuracy Filtering Explainable AI knowledge discovery natural language processing Convolutional neural networks Recommender systems

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Discriminating AI-generated Fake News 27

Discriminating AI-generated Fake News

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27th international conference on knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Trandabat, Diana Gifu, Daniela Faculty of Computer Science Alexandru Ioan Cuza University General Berthelot 16 Iasi700483 Romania Institute of Computer Science Romanian Academy - Iasi branch Bulevardul Carol I 700505 Romania

Generating fake news has become a prevalent issue in the digital age, with profound consequences on society, politics, and media. Due to an exponential increase in the use of unverified information, spread on social media by users with different backgrounds, a significant task facing natural language processing is real-time recognition of generated fake news. The objective of this paper is to investigate the feasibility of utilizing artificial intelligence (AI) for the automatic generation of fake news that possesses a sufficiently high level of quality to serve as a synthetic corpus. These automatically generated texts are subsequently used to improve fake news detectors, evaluated on different publicly available datasets fake news datasets, but also against a novel fake news corpus, especially developed for this research. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： AI content generation fake news language model

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A Semantic Approach for Generating Graphical Representation from Aircraft Maintenance Text 14

A Semantic Approach for Generating Graphical Representation ...

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14th international conference on knowledge Management and Information Systems, KMIS 2022 as part of 14th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2022

作者： Hoang, Thi-Bich-Ngoc Tran, Ba-Huy Mozafari, Marzieh Capgemini Engineering France

ISBN: (纸本)9789897586149

Industrial maintenance is a strategic business function. Over the past twenty years, the role of maintenance in companies has become increasingly important both technologically and economically. However, maintenance service has not taken into account the frequent change in maintenance knowledge, the users’ perspective (the training, the origins, or the cultures), and the users’ support documents preferences. In this article, we propose preliminary results of an approach to make industrial maintenance universal. We use natural language processing techniques to extract core information from maintenance text and then construct a knowledge base to store all relevant information about maintenance processes, domain information, and corresponding graphics. Finally, we generate a graphical representation of input text to help better understand the procedure, thus increasing the user experience and the performance of maintenance operations in terms of reducing time and cost. This approach is first applied to aircraft maintenance and can be applied to maintenance in other industry domains as well. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Semantics

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Contextual transcription and Summarization of audio using AI 5

Contextual transcription and Summarization of audio using AI

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5th international conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Pandita, Karan Thakur, Purab Kulranjan Singh Annamalai, Suresh Department of Networking and Communications SRM Institute of Science and Technology SRM Nagar Chengalpattu Tamil Nadu Kattankulathur603203 India Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Chengalpattu Tamil NaduKattankulathur Chennai603203 India

ISBN: (纸本)9798400709418

The field of natural language processing (NLP) has revolutionized the way human language interacts with computer systems. NLP applications span machine translation, information extraction, summarization, and question answering, driven by vast computational resources and big data methodologies. Despite these advancements, NLP tools haven't fully integrated with Internet of Things (IoT) devices, like audio recorders, hindering their accessibility and usability. This paper introduces an innovative solution: a method for audio transcription and contextual summarization using NLP, addressing this gap and enhancing comprehension. Our approach employs cutting-edge NLP techniques, including word embedding methods and knowledge-based graphs, to create a system that efficiently converts audio content into written text and generates coherent summaries. Unlike existing AI tools, our system's summaries are not only accurate but also rich and deep, providing insightful representations of the original content. This depth is achieved through advanced linguistic analysis, surpassing tools like ChatGPT. Furthermore, our system breaks language barriers, enabling multilingual data traversal, enhancing accessibility on a global scale. Our research methodology ensures the system's adherence to industry standards like Request for Comments (RFC) and Constrained Application Protocol (CoAP), guaranteeing interoperability and reliability. By incorporating knowledge-based graphs, our system comprehensively understands audio content, enhancing the accuracy of summarization. This approach addresses the unmet need for seamlessly integrating NLP with IoT devices, making the technology accessible to a broader audience. © 2023 ACM.

关键词： Internet of things

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13th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management (IC3K) / 13th international conference on knowledge Discovery and Information Retrieval (KDIR)

作者： Vrbanec, Tedo Mestrovic, Ana Univ Zagreb Fac Teacher Educ Sayska Cesta 77 Zagreb 10000 Croatia Univ Rijeka Dept Informat Radmile Matejcic 2 Rijeka 51000 Croatia

ISBN: (纸本)9789897585333

The article describes the experiments and their results using two Deep Learning (DL) models and four measures of similarity/distance, determining the similarity of documents from the three publicly available corpora of paraphrased documents. As DL models, Word2Vec was used in two variants and FastText in one. The article explains the existence of a multitude of hyperparameters and defines their values, selection of effective ways of text processing, the use of some non-standard parameters in natural language processing (NLP), the characteristics of the corpora used, the results of the pairs (DL model, similarity measure) processing corpora, and tries to determine combinations of conditions under which use of exactly certain pairs yields the best results (presented in the article), measured by standard evaluation measures Accuracy, Precision, Recall and primarily F-measure.

关键词： Plagiarism Deep Learning natural language processing Text Similarity Distance Measures

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Building the knowledge Graph from Medical Conversational Text Data and its Applications 4

Building the Knowledge Graph from Medical Conversational Tex...

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Kulkarni, Rugwedi Haribhakta, Yashodhara College of Engineering Pune Department of Computer Engineering Pune India

ISBN: (纸本)9781665474368

Conversations between doctor and patient contain essential information like past diseases, medical history, and treatments given in the past. This information can be helpful in future diagnosis and treatment. However, maintaining the raw text conversations is a tedious task. In this paper, we propose the step-wise approach for building the medical knowledge graph(KG) from the raw clinical text conversations, which stores the information extracted from the conversations in the form of tuples. knowledge graph building is a natural language processing task and includes steps such as entity extraction, defining relations, creating tuples, and knowledge graph embedding. This paper gives a mathematical approach for calculating the valid tuples in the graph based on occurrences of the entities. This paper also proposes using the created knowledge graph to predict disease based on the symptoms and past treatment from the sentences. © 2022 IEEE.

关键词： knowledge graph

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KnowReQA: A knowledge-aware Retrieval Question Answering System 1

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15th international conference on knowledge Science, engineering, and Management (KSEM)

作者： Wang, Chuanrui Bai, Jun Zhang, Xiaofeng Yan, Cen Ouyang, Yuanxin Rong, Wenge Xiong, Zhang Beihang Univ Sino French Engineer Sch Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China

ISBN: (数字)9783031109836

ISBN: (纸本)9783031109836;9783031109829

Retrieval question answering (ReQA) is an essential mechanism to automatically satisfy the users' information needs and overcome the problem of information overload. As a promising solution to achieve fast retrieval from large-scale candidate answers, dual-encoder framework has been widely studied to improve its representation quality for text in the recent years. Inspired by that humans usually answer the question using their background knowledge, in this work, we explore the way to incorporate knowledge entities into the retrieval model to build high-quality text representations and propose novel knowledge-aware text encoding and knowledge-aware text matching modules to facilitate the fusion between text and knowledge. The promising experimental results on various benchmarks prove the potential of the proposed approach.

关键词： Retrieval question answering Dual-encoder knowledge aware retrieval natural language processing

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Music Emotion Recognition using Convolutional Neural Networks for Regional languages 1

Music Emotion Recognition using Convolutional Neural Network...

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1st IEEE international conference on Ambient Intelligence, knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Shashidhar, R. Balivada, Dhanush Shalini, D.N. Hirebasur Krishnappa, Krutthika Roopa, M. Jss Science and Technology University Department of Electronics and Communication Engineering Mysuru India Sothern University and A&m College Baton Department of Computer Science Baton RougeLA United States Dayananda Sagar College of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350316469

Music emotion recognition is a challenging task that has been getting increasing kindness in the field of artificial intelligence. We propose a unique lD Convolutional Neural Network (lD CNN) method for music emotion recognition. Our proposed approach combines the advantages of convolutional neural networks with the lD temporal structure of music signals. Evaluate our model on two publicly available datasets and demonstrate its effectiveness in accurately recognizing different emotions from music signals. Our method presents a novel approach to abstracting raw audio signals into meaningful representations. It utilizes multiple lD convolutional and pooling layers to extract features from the raw audio signals, which can then be used for further analysis. We demonstrate the effectiveness of our approach by providing examples of applications that can benefit from this abstraction process, such as sound recognition and speech recognition. Additionally, we discuss the potential of this method to be applied in other areas such as natural language processing and music generation. We compare the performance of our model with traditional machine learning (ML) methods and demonstrate that our lD CNN outperforms these traditional ML methods in terms of accuracy. We also discuss potential use cases for this technology and its implications for future research in audio signal processing. We show that our model achieves superior results in terms of accuracy and computational efficiency, making it a promising solution for music emotion recognition tasks. Our results suggest that the lD CNN approach is an effective tool for recognizing musical emotions with high accuracy. © 2023 IEEE.

关键词： lD Convolutional Neural Networks Mel Frequency Cepstral Coefficients Music Information Retrieval Relu Weighted L2 Regularization

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