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Exploring The Data Science 7

Exploring The Data Science

引用

2023 7th international conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Kumar, Amodh Kapil, Akshat Ahlawat, Deepak Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350304268

Data Science is a rapidly expanding field in technology that has attracted a lot of concentration recently due to the enormous influence it has on many different companies and domains. The field encompasses a wide range of techniques and tools aimed at extracting meaningful insights and knowledge from large and complex datasets. This study seeks to offer a thorough review of the field of Data Science by exploring its history, fundamental concepts, and current trends. It also highlights the current trends and developments in the field, such as big data, cloud computing, and the Internet of Things (IoT). The paper concludes by discussing the future of Data Science and the impact it will have on various industries, including healthcare, finance, and retail. © 2023 IEEE.

关键词： Artificial Intelligence Deep Learning Machine Learning natural language processing

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Social Robot Navigation and Comfortable Following: A Novel knowledge-Based Robot-Pedestrian Interaction Model with Self Learning Strategy

Social Robot Navigation and Comfortable Following: A Novel K...

引用

2023 IEEE international conference on Robotics and Biomimetics, ROBIO 2023

作者： Xu, Xujun Gong, Xiaofei Cao, Chenyang Xu, Qingchuan Sun, Chengfeng Chi, Wenzheng Sun, Lining Soochow University Robotics and Microsystems Center School of Mechanical and Electric Engineering Suzhou215021 China

ISBN: (纸本)9798350325706

Currently, mobile service robots have been playing an increasingly significant role in daily life. However, many mobile service robots still face challenges in navigation and interaction modes. Current navigation and interaction patterns are often fixed and lack personalization, making it difficult to meet the preferences of different users. Users often need to continually provide specific action instructions to the robot, resulting in a complex and cumbersome interaction process. This paper aims to propose a human-robot interaction framework for robot navigation and pedestrian following based on natural language processing (NLP), which combines a knowledge base and neural network predictions for comfortable following distance to achieve a more intelligent, comfortable, and personalized human-robot interaction experience. To validate the effectiveness of the proposed framework and algorithms, a series of experiments were conducted in different scenarios. The results demonstrate that the NLP-based navigation and following human-robot interaction framework presented in this paper exhibits favorable feasibility and applicability in various working environments. Additionally, the neural network-based algorithm for predicting comfortable following significantly enhances the pedestrian following experience. © 2023 IEEE.

关键词： Following Strategy natural language processing Neural Network Robot Navigation

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Design of Structured Query language Online Judge Systems 19

Design of Structured Query Language Online Judge Systems

引用

19th international conference on natural Computation, Fuzzy Systems and knowledge Discovery, ICNC-FSKD 2023

作者： Chen, Yijun Zheng, Yurui Ou, Jiajun Zhong, Qiuyi Zhang, Shaohong Guangzhou University School of Computer Science and Cyber Engineering Guangdong Guangzhou510000 China

ISBN: (纸本)9798350304398

The research described in this paper aims to implement a novel SQL query evaluation system, known as the SQL Online Judge (SQLOJ), and conduct mining and analysis on the relevant data obtained from SQLOJ. This system is intended to provide guidance and inspiration for student learning, teacher instruction, and course design. The paper focuses on introducing the technologies and system framework utilized by the system, and proposes a system availability tradeoff strategy to address the issue of excessive concurrent online users. The research results presented in this paper establish SQLOJ as a comprehensive platform and data resource for practical teaching in database courses, offering an innovative foundation that effectively complements traditional approaches to professional and course descriptions. © 2023 IEEE.

关键词： Query processing

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Towards Complex Ontology Alignment Using Large language Models 6th

Towards Complex Ontology Alignment Using Large Language Mode...

引用

6th international conference on knowledge Graphs and Semantic Web, KGSWC 2024

作者： Amini, Reihaneh Norouzi, Sanaz Saki Hitzler, Pascal Amini, Reza Kansas State University ManhattanKS United States Wright State University DaytonOH United States

ISBN: (纸本)9783031812200

Ontology alignment, a critical process in the Semantic Web for detecting relationships between different ontologies, has traditionally focused on identifying so-called "simple" 1-to-1 relationships through class labels and properties comparison. The more practically useful exploration of more complex alignments remains a hard problem to automate, and as such is largely underexplored, i.e. in application practice it is usually done manually by ontology and domain experts. Recently, the surge in natural language processing (NLP) capabilities, driven by advancements in Large language Models (LLMs), presents new opportunities for enhancing ontology engineering practices, including ontology alignment tasks. This paper investigates the application of LLM technologies to tackle the complex ontology alignment challenge. Leveraging a prompt-based approach and integrating rich ontology content – so-called modules – our work constitutes a significant advance towards automating the complex alignment task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Modeling languages

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OncoNLP: Cancer Comprehend Annotation - A Pipeline for Cancer Phenotype and Clinical Extraction

OncoNLP: Cancer Comprehend Annotation - A Pipeline for Cance...

引用

2024 IEEE-EMBS international conference on Biomedical and Health Informatics, BHI 2024

作者： Duong, Thanh Thieu, Thanh University of South Florida Department of Computer Science and Engineering TampaFL United States Moffitt Cancer Center Department of Machine Learning TampaFL United States

ISBN: (纸本)9798350351552

Information extraction from clinical text is needed to comprehend patient conditions and determine anticancer treatment. Existing NLP systems, such as Amazon Comprehend Medical, Clamp, and DeepPhe, are proprietary and suboptimal due to the shift in the textual distribution and expression of cancer phenotypes. We introduce OncoNLP, a natural language processing toolkit that comprises deep neural network BERT models designed to extract cancer phenotype and related biomedical information. Currently, the toolkit contains two primary components: a Biomedical BERT(BiomedBERT) model to extract general medical information and a Cancer Bert(CaBERT) model to identify the primary tumor site and histology. We evaluate the performance of BiomedBERT on Informatics for Integrating Biology & the Bedside (i2b2) dataset against Amazon Comprehend Medical and Clamp. BiomedBERT outperforms all other methods with an exact matching F1-score of 88.5%. Next, we evaluate CaBERT on a Moffitt dataset with over 2000 clinical notes against DeepPhe, which shows a 50% improvement in both tumor site and histology tasks. Lastly, we introduce a knowledge prompt with OncoNLP and engineer it for large language models. We name it OncoNLP-Assist, a chatbot system powered by OncoNLP and Llama2 that could extract information from electrical health records and interact with physicians. © 2024 IEEE.

关键词： Diseases

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Towards knowledge Graph Creation from Greek Governmental Documents 36th

Towards Knowledge Graph Creation from Greek Governmental Doc...

引用

36th international conference on Industrial, engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Georgoudi, Amalia Stylianou, Nikolaos Konstantinidis, Ioannis Meditskos, Georgios Mavropoulos, Thanassis Vrochidis, Stefanos Bassiliades, Nick Aristotle Univ Thessaloniki Sch Informat Thessaloniki 54124 Greece Int Hellen Univ Sch Sci & Technol Thessaloniki 57001 Greece Ctr Res & Technol Hellas Informat Technol Inst Thessaloniki 57001 Greece

ISBN: (纸本)9783031368189;9783031368196

Documents contain textual information, which is of the utmost importance for all the organizations. Document management systems have been used to store vast amounts of unstructured textual data described with minimal metadata, a method that has several limitations. In order to convert hidden knowledge into machine-readable data with rich connections, this paper presents work in progress on the development of the first end-to-end guided approach to construct a knowledge Graph from Greek governmental documents from the Greek open government portal. The resulted knowledge graph constitutes a proof-of-concept graph, that illustrates the beneficial semantic relationships between the textual data.

关键词： knowledge Graph Construction RDF Triples Ontologies natural language processing knowledge Representation Government Documents

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Development of New Information processing Technologies Based on Lexical and Grammatical Models of Agglutinative languages 8

Development of New Information Processing Technologies Based...

引用

8th international conference on Computer Science and engineering, UBMK 2023

作者： Suleimanov, Dzavdet Sh. Fridman, Alexander Ya. Gilmullin, Rinat A. Kubedinova, Lenara Institute of Applied Semiotics Academy of Sciences of the Republic of Tatarstan Kazan Russia Institute for Informatics and Mathematical Modelling Kola Science Center Ras Apatity Russia

ISBN: (纸本)9798350340815

In this paper, we propose a new structure to model agglutinative natural languages (ANLs) on the basis of their lex-ical and grammatical features, namely recursion, morphological ellipsis, functional diversity and semantic multivalence of affixes (in particular, the affixes encoding underdefined information and fuzzy commands). This way we try to overcome the root cause of low efficiency of modern tools to accumulate and process knowledge in such languages. In our opinion, it lies in complexity of intellectualization of such tools created on the basis of primitive artificial programming languages that practically represent a subset of inflectional-analytical languages or artificial structures based on them. To reduce severity of the identified problem, it is proposed to build systems for modeling ANLs based on a decentralized system of technological means for ver-balization and meaning recognition, which consider the above-listed peculiarities of such languages. To evaluate and exemplify the proposed ideas, Tatar language will be used since authors have a fair experience in investigating this language. © 2023 IEEE.

关键词： Semantics

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Construction and Application of Robo-Advisory System Based on knowledge Graph

Construction and Application of Robo-Advisory System Based o...

引用

Electronic Information engineering and Computer Technology (EIECT), international conference on

作者： Ling Jing Liying Wang School of Accountancy Guangzhou Xinhua University Guangzhou Guangdong China School of Economics and Trade Guangzhou Xinhua University Guangzhou Guangdong China

ISBN: (数字)9798331528850

ISBN: (纸本)9798331528867

This study constructs an intelligent investment advisor system based on a knowledge graph to address the complex decision-making needs in financial markets. By integrating natural language processing (NLP) technology and a graph database, the system effectively extracts and analyzes entities and relationships in the investment domain. Experimental results demonstrate that personalized optimization strategies significantly improve the accuracy and response speed of investment recommendations.

关键词： Measurement knowledge engineering Deep learning Accuracy Decision making knowledge graphs natural language processing Real-time systems Optimization Investment

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GlossGPT: GPT for Word Sense Disambiguation using Few-shot Chain-of-Thought Prompting 16

GlossGPT: GPT for Word Sense Disambiguation using Few-shot C...

引用

16th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th international conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Sumanathilaka, Deshan Micallef, Nicholas Hough, Julian Swansea University United Kingdom

Lexical ambiguity is a major challenge in computational linguistic tasks, as limitations in proper sense identification lead to inefficient translation and question answering. General-purpose Large language Models (LLMs) are commonly utilized for natural language processing (NLP) tasks. However, utilizing general-purpose LLMs for specific tasks has been challenging, and fine-tuning has become a critical requirement for task specification. In this work, we craft advanced prompts with different contextual parameters to guide the model's inference towards accurate sense prediction to handle Word Sense Disambiguation (WSD). We present a few-shot Chain of Thought (COT) prompt-based technique using GPT-4-Turbo with knowledgebase as a retriever that does not require fine-tuning the model for WSD tasks and sense definitions are supported by synonyms to broaden the lexical meaning. Our approach achieves comparable performance on the SemEval and Senseval datasets. More importantly, we set a new state-of-the-art performance with the few-shot FEWS dataset, breaking through the 90% F1 score barrier. © 2025 Elsevier B.V.. All rights reserved.

关键词： knowledge engineering

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LAxplore: An NLP-Based Tool for Distilling Learning Analytics and Learning Design Instruments out of Scientific Publications 15

LAxplore: An NLP-Based Tool for Distilling Learning Analytic...

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15th international conference on knowledge Discovery and Information Retrieval, KDIR 2023 as part of the 15th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2023

作者： Ahmad, Atezaz Schneider, Jan Schiffner, Daniel Islamovic, Esad Drachsler, Hendrik DIPF - Leibniz Institute for Research and Information in Education Frankfurt Germany Goethe University Frankfurt Germany

ISBN: (纸本)9789897586712

Each year, the amount of research publications is increasing. Staying on top of the state of the art is a pressing issue. The field of Learning Analytics (LA) is no exception, with the rise of digital education systems that are used broadly these days from K12 up to Higher Education. Keeping track of the advances in LA is challenging. This is especially the case for newcomers to the field, as well as for the increasing number of LA units that consult their teachers and scholars on applying evidence-based research outcomes in their lectures. To keep an overview of the rapidly growing research findings on LA, we developed LAxplore, a tool that uses NLP to extract relevant information from the LA literature. In this article, we present the evaluation of LAxplore. Results from the evaluation show that LAxplore can significantly support researchers in extracting information from relevant LA publications as it reduces the time of searching and retrieving the knowledge by a factor of six. However, the accurate extraction of relevant information from LA literature is not yet ready to be fully automatized and some manual work is still required. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： natural language processing systems

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