Remote driving,an emergent technology enabling remote operations of vehicles,presents a significant challenge in transmitting large volumes of image data to a central server.This requirement outpaces the capacity of t...Remote driving,an emergent technology enabling remote operations of vehicles,presents a significant challenge in transmitting large volumes of image data to a central server.This requirement outpaces the capacity of traditional communication methods.To tackle this,we propose a novel framework using semantic communications,through a region of interest semantic segmentation method,to reduce the communication costs by transmitting meaningful semantic information rather than bit-wise data.To solve the knowledge base inconsistencies inherent in semantic communications,we introduce a blockchain-based edge-assisted system for managing diverse and geographically varied semantic segmentation knowledge bases.This system not only ensures the security of data through the tamper-resistant nature of blockchain but also leverages edge computing for efficient management.Additionally,the implementation of blockchain sharding handles differentiated knowledge bases for various tasks,thus boosting overall blockchain efficiency.Experimental results show a great reduction in latency by sharding and an increase in model accuracy,confirming our framework's effectiveness.展开更多
This paper gives a semantic fuzzy retrieval method of multimedia object, discusses the principle of fuzzy semantic retrieval technique, presents a fuzzy reasoning mechanism based on the knowledge base, and designs the...This paper gives a semantic fuzzy retrieval method of multimedia object, discusses the principle of fuzzy semantic retrieval technique, presents a fuzzy reasoning mechanism based on the knowledge base, and designs the relevant reasoning algorithms. Researchful results have innovative significance.展开更多
There is a tremendous growth of digital data due to the stunning progress of digital devices which facilitates capturing them. Digital data include image, text, and video. Video represents a rich source of information...There is a tremendous growth of digital data due to the stunning progress of digital devices which facilitates capturing them. Digital data include image, text, and video. Video represents a rich source of information. Thus, there is an urgent need to retrieve, organize, and automate videos. Video retrieval is a vital process in multimedia applications such as video search engines, digital museums, and video-on-demand broadcasting. In this paper, the different approaches of video retrieval are outlined and briefly categorized. Moreover, the different methods that bridge the semantic gap in video retrieval are discussed in more details.展开更多
Alzheimer’s disease(AD)is a very complex disease that causes brain failure,then eventually,dementia ensues.It is a global health problem.99%of clinical trials have failed to limit the progression of this disease.The ...Alzheimer’s disease(AD)is a very complex disease that causes brain failure,then eventually,dementia ensues.It is a global health problem.99%of clinical trials have failed to limit the progression of this disease.The risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical symptoms.Therapies for AD are likely to be more helpful if the diagnosis is determined early before the final stage of neurological dysfunction.In this regard,the need becomes more urgent for biomarker-based detection.A key issue in understanding AD is the need to solve complex and high-dimensional datasets and heterogeneous biomarkers,such as genetics,magnetic resonance imaging(MRI),cerebrospinal fluid(CSF),and cognitive scores.Establishing an interpretable reasoning system and performing interoperability that achieves in terms of a semantic model is potentially very useful.Thus,our aim in this work is to propose an interpretable approach to detect AD based on Alzheimer’s disease diagnosis ontology(ADDO)and the expression of semantic web rule language(SWRL).This work implements an ontology-based application that exploits three different machine learning models.These models are random forest(RF),JRip,and J48,which have been used along with the voting ensemble.ADNI dataset was used for this study.The proposed classifier’s result with the voting ensemble achieves a higher accuracy of 94.1%and precision of 94.3%.Our approach provides effective inference rules.Besides,it contributes to a real,accurate,and interpretable classifier model based on various AD biomarkers for inferring whether the subject is a normal cognitive(NC),significant memory concern(SMC),early mild cognitive impairment(EMCI),late mild cognitive impairment(LMCI),or AD.展开更多
This paper proposes a method to construct conceptual semantic knowledge base of software engineering domain based on Wikipedia. First, it takes the concept of SWEBOK V3 as the standard to extract the interpretation of...This paper proposes a method to construct conceptual semantic knowledge base of software engineering domain based on Wikipedia. First, it takes the concept of SWEBOK V3 as the standard to extract the interpretation of the concept from the Wikipedia, and extracts the keywords as the concept of semantic;Second, through the conceptual semantic knowledge base, it is formed by the relationship between the hierarchical relationship concept and the other text interpretation concept in the Wikipedia. Finally, the semantic similarity between concepts is calculated by the random walk algorithm for the construction of the conceptual semantic knowledge base. The semantic similarity of knowledge base constructed by this method can reach more than 84%, and the effectiveness of the proposed method is verified.展开更多
In recent years, there are many types of semantic similarity measures, which are used to measure the similarity between two concepts. It is necessary to define the differences between the measures, performance, and ev...In recent years, there are many types of semantic similarity measures, which are used to measure the similarity between two concepts. It is necessary to define the differences between the measures, performance, and evaluations. The major contribution of this paper is to choose the best measure among different similarity measures that give us good result with less error rate. The experiment was done on a taxonomy built to measure the semantic distance between two concepts in the health domain, which are represented as nodes in the taxonomy. Similarity measures methods were evaluated relative to human experts’ ratings. Our experiment was applied on the ICD10 taxonomy to determine the similarity value between two concepts. The similarity between 30 pairs of the health domains has been evaluated using different types of semantic similarity measures equations. The experimental results discussed in this paper have shown that the Hoa A. Nguyen and Hisham Al-Mubaid measure has achieved high matching score by the expert’s judgment.展开更多
This paper presents the semantic analysis of queries written in natural language (French) and dedicated to the object oriented data bases. The studied queries include one or two nominal groups (NG) articulating around...This paper presents the semantic analysis of queries written in natural language (French) and dedicated to the object oriented data bases. The studied queries include one or two nominal groups (NG) articulating around a verb. A NG consists of one or several keywords (application dependent noun or value). Simple semantic filters are defined for identifying these keywords which can be of semantic value: class, simple attribute, composed attribute, key value or not key value. Coherence rules and coherence constraints are introduced, to check the validity of the co-occurrence of two consecutive nouns in complex NG. If a query is constituted of a single NG, no further analysis is required. Otherwise, if a query covers two valid NG, it is a subject of studying the semantic coherence of the verb and both NG which are attached to it.展开更多
In this paper, we propose Term-based Semantic Peerto-Peer Networks （TSPN） to achieve semantic search. For each peer, TSPN builds a full text index of its documents. Through the analysis of resources, TSPN obtains se...In this paper, we propose Term-based Semantic Peerto-Peer Networks （TSPN） to achieve semantic search. For each peer, TSPN builds a full text index of its documents. Through the analysis of resources, TSPN obtains series of terms, and distributes these terms into the network. Thus, TSPN can use query terms to locate appropriate peers to perform semantic search. Moreover, unlike the traditional structured P2P networks, TSPN uses the terms, not the peers, as the logical nodes of DHT. This can withstand the impact of network chum. The experimental results show that TSPN has better performance compared with the existing P2P semantic searching algorithms.展开更多
Semantic representation of evidence-based medical guidelines provides the support for the data inter-operability and has been found many applications in the medical domain. In this paper, we describe a semantic repres...Semantic representation of evidence-based medical guidelines provides the support for the data inter-operability and has been found many applications in the medical domain. In this paper, we describe a semantic representation approach of evidence-based medical guidelines, which is based on the Semantic Web Technology standards. We discuss several use cases of that semantic representation of evidence-based medical guideline, and show that they are potentially useful for medical applications.展开更多
Purpose: To design an efficient high-performance algorithm for semantic annotation of biodiversity documents in Chinese.Design/methodology/approach: Data set consists of 1,000 randomly selected documents from Flora of...Purpose: To design an efficient high-performance algorithm for semantic annotation of biodiversity documents in Chinese.Design/methodology/approach: Data set consists of 1,000 randomly selected documents from Flora of China. Comparative evaluation of the proposed approach with the Na ve Bayes algorithm have been developed before for the same purpose.Findings: Experimental results show that the heuristics based algorithm outperformed the Na ve Bayes algorithm. The use of leading words helped improving the annotation performance while prioritizing rule application based on their weights had no significant impact on algorithm performance.Research limitations: The ICTCLAS was used to identify word boundaries off-shelf without optimatization for biodiversity domain. This may have not made the best use of the tool.Practical implications & Originality/value: The performance of heuristics based approach,enhanced by leading words analysis, reached an F value of 0.9216, which is sufficiently accurate for practical use.展开更多
As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework...As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain.The proposed framework comprises three core modules:legal feature extraction,semantic similarity assessment,and verdict recommendation.For legal feature extraction,a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts.Semantic similarity between cases is evaluated using a hybrid method that combines rule-based logic with an LSTM model,analyzing the feature vectors of query cases against a legal knowledge base.Verdicts are then recommended through a rule-based retrieval system,enhanced by predefined legal statutes and regulations.By merging rule-based methodologies with deep learning,this framework addresses the interpretability challenges often associated with contemporary AImodels,thereby enhancing both transparency and generalizability across diverse legal contexts.The system was rigorously tested using a legal corpus of 43,000 case laws across six categories:Criminal,Revenue,Service,Corporate,Constitutional,and Civil law,ensuring its adaptability across a wide range of judicial scenarios.Performance evaluation showed that the feature extraction module achieved an average accuracy of 91.6%with an F-Score of 95%.The semantic similarity module,tested using Manhattan,Euclidean,and Cosine distance metrics,achieved 88%accuracy and a 93%F-Score for short queries(Manhattan),89%accuracy and a 93.7%F-Score for medium-length queries(Euclidean),and 87%accuracy with a 92.5%F-Score for longer queries(Cosine).The verdict recommendation module outperformed existing methods,achieving 90%accuracy and a 93.75%F-Score.This study highlights the potential of hybrid AI frameworks to improve judicial decision-making and streamline legal processes,offering a robust,interpretable,and adaptable solution for the evolving demands of modern legal systems.展开更多
针对大规模开放在线课程(MOOC)质量评估维度单一、缺乏细粒度分析的问题,构建了一个以方面级情感分析为核心的语义切分至多维评估框架。基于中国大学MOOC平台中大数据与人工智能类课程的评论数据,利用通用信息抽取(universal informatio...针对大规模开放在线课程(MOOC)质量评估维度单一、缺乏细粒度分析的问题,构建了一个以方面级情感分析为核心的语义切分至多维评估框架。基于中国大学MOOC平台中大数据与人工智能类课程的评论数据,利用通用信息抽取(universal information extraction,UIE)工具抽取影响要素词,通过K-means聚类与变异系数法,构建了涵盖5个一级指标和10个二级指标的加权评价体系。设计了基于影响要素词定位的语义切分(impact element word-based targeted comment segmentation and classification,ITCSC)算法,将长评论切分为方面级短句,结合SKEP情感模型实现多维度量化分析,揭示了内容配置、服务评价等维度的表现特征。实验表明,该框架兼顾整体趋势与细节特征,为课程优化及选课提供数据支持,丰富了细粒度教育质量评估路径。展开更多
To semantically integrate heterogeneous resources and provide a unified intelligent access interface, semantic web technology is exploited to publish and interlink machineunderstandable resources so that intelligent s...To semantically integrate heterogeneous resources and provide a unified intelligent access interface, semantic web technology is exploited to publish and interlink machineunderstandable resources so that intelligent search can be supported. TCMSearch, a deployed intelligent search engine for traditional Chinese medicine （TCM）, is presented. The core of the system is an integrated knowledge base that uses a TCM domain ontology to represent the instances and relationships in TCM. Machine-learning techniques are used to generate semantic annotations for texts and semantic mappings for relational databases, and then a semantic index is constructed for these resources. The major benefit of representing the semantic index in RDF/OWL is to support some powerful reasoning functions, such as class hierarchies and relation inferences. By combining resource integration with reasoning, the knowledge base can support some intelligent search paradigms besides keyword search, such as correlated search, semantic graph navigation and concept recommendation.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant No.62062031in part by the MIC/SCOPE#JP235006102+2 种基金in part by JST ASPIRE Grant Number JPMJAP2325in part by ROIS NII Open Collaborative Research under Grant 24S0601in part by collaborative research with Toyota Motor Corporation,Japan。
文摘Remote driving,an emergent technology enabling remote operations of vehicles,presents a significant challenge in transmitting large volumes of image data to a central server.This requirement outpaces the capacity of traditional communication methods.To tackle this,we propose a novel framework using semantic communications,through a region of interest semantic segmentation method,to reduce the communication costs by transmitting meaningful semantic information rather than bit-wise data.To solve the knowledge base inconsistencies inherent in semantic communications,we introduce a blockchain-based edge-assisted system for managing diverse and geographically varied semantic segmentation knowledge bases.This system not only ensures the security of data through the tamper-resistant nature of blockchain but also leverages edge computing for efficient management.Additionally,the implementation of blockchain sharding handles differentiated knowledge bases for various tasks,thus boosting overall blockchain efficiency.Experimental results show a great reduction in latency by sharding and an increase in model accuracy,confirming our framework's effectiveness.
文摘This paper gives a semantic fuzzy retrieval method of multimedia object, discusses the principle of fuzzy semantic retrieval technique, presents a fuzzy reasoning mechanism based on the knowledge base, and designs the relevant reasoning algorithms. Researchful results have innovative significance.
文摘There is a tremendous growth of digital data due to the stunning progress of digital devices which facilitates capturing them. Digital data include image, text, and video. Video represents a rich source of information. Thus, there is an urgent need to retrieve, organize, and automate videos. Video retrieval is a vital process in multimedia applications such as video search engines, digital museums, and video-on-demand broadcasting. In this paper, the different approaches of video retrieval are outlined and briefly categorized. Moreover, the different methods that bridge the semantic gap in video retrieval are discussed in more details.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1A2C1011198).
文摘Alzheimer’s disease(AD)is a very complex disease that causes brain failure,then eventually,dementia ensues.It is a global health problem.99%of clinical trials have failed to limit the progression of this disease.The risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical symptoms.Therapies for AD are likely to be more helpful if the diagnosis is determined early before the final stage of neurological dysfunction.In this regard,the need becomes more urgent for biomarker-based detection.A key issue in understanding AD is the need to solve complex and high-dimensional datasets and heterogeneous biomarkers,such as genetics,magnetic resonance imaging(MRI),cerebrospinal fluid(CSF),and cognitive scores.Establishing an interpretable reasoning system and performing interoperability that achieves in terms of a semantic model is potentially very useful.Thus,our aim in this work is to propose an interpretable approach to detect AD based on Alzheimer’s disease diagnosis ontology(ADDO)and the expression of semantic web rule language(SWRL).This work implements an ontology-based application that exploits three different machine learning models.These models are random forest(RF),JRip,and J48,which have been used along with the voting ensemble.ADNI dataset was used for this study.The proposed classifier’s result with the voting ensemble achieves a higher accuracy of 94.1%and precision of 94.3%.Our approach provides effective inference rules.Besides,it contributes to a real,accurate,and interpretable classifier model based on various AD biomarkers for inferring whether the subject is a normal cognitive(NC),significant memory concern(SMC),early mild cognitive impairment(EMCI),late mild cognitive impairment(LMCI),or AD.
文摘This paper proposes a method to construct conceptual semantic knowledge base of software engineering domain based on Wikipedia. First, it takes the concept of SWEBOK V3 as the standard to extract the interpretation of the concept from the Wikipedia, and extracts the keywords as the concept of semantic;Second, through the conceptual semantic knowledge base, it is formed by the relationship between the hierarchical relationship concept and the other text interpretation concept in the Wikipedia. Finally, the semantic similarity between concepts is calculated by the random walk algorithm for the construction of the conceptual semantic knowledge base. The semantic similarity of knowledge base constructed by this method can reach more than 84%, and the effectiveness of the proposed method is verified.
文摘In recent years, there are many types of semantic similarity measures, which are used to measure the similarity between two concepts. It is necessary to define the differences between the measures, performance, and evaluations. The major contribution of this paper is to choose the best measure among different similarity measures that give us good result with less error rate. The experiment was done on a taxonomy built to measure the semantic distance between two concepts in the health domain, which are represented as nodes in the taxonomy. Similarity measures methods were evaluated relative to human experts’ ratings. Our experiment was applied on the ICD10 taxonomy to determine the similarity value between two concepts. The similarity between 30 pairs of the health domains has been evaluated using different types of semantic similarity measures equations. The experimental results discussed in this paper have shown that the Hoa A. Nguyen and Hisham Al-Mubaid measure has achieved high matching score by the expert’s judgment.
文摘This paper presents the semantic analysis of queries written in natural language (French) and dedicated to the object oriented data bases. The studied queries include one or two nominal groups (NG) articulating around a verb. A NG consists of one or several keywords (application dependent noun or value). Simple semantic filters are defined for identifying these keywords which can be of semantic value: class, simple attribute, composed attribute, key value or not key value. Coherence rules and coherence constraints are introduced, to check the validity of the co-occurrence of two consecutive nouns in complex NG. If a query is constituted of a single NG, no further analysis is required. Otherwise, if a query covers two valid NG, it is a subject of studying the semantic coherence of the verb and both NG which are attached to it.
基金Supported by the National Natural Science Foundation of China( 60873225, 60773191, 70771043)National High Technology Research and Development Program of China ( 2007AA01Z403)Wuhan Youth Science and Technology Chenguang Program (200950431171)
文摘In this paper, we propose Term-based Semantic Peerto-Peer Networks （TSPN） to achieve semantic search. For each peer, TSPN builds a full text index of its documents. Through the analysis of resources, TSPN obtains series of terms, and distributes these terms into the network. Thus, TSPN can use query terms to locate appropriate peers to perform semantic search. Moreover, unlike the traditional structured P2P networks, TSPN uses the terms, not the peers, as the logical nodes of DHT. This can withstand the impact of network chum. The experimental results show that TSPN has better performance compared with the existing P2P semantic searching algorithms.
基金Supported by the European Commission under the 7th Framework EURECA Project(FP7-ICT-2011-7,288048)the Key Projects of National Social Science Foundation of China(11ZD&189)the Natural Science Foundation of Hubei Province(2014CFB247)
文摘Semantic representation of evidence-based medical guidelines provides the support for the data inter-operability and has been found many applications in the medical domain. In this paper, we describe a semantic representation approach of evidence-based medical guidelines, which is based on the Semantic Web Technology standards. We discuss several use cases of that semantic representation of evidence-based medical guideline, and show that they are potentially useful for medical applications.
基金supported by the National Social Science Foundation of China (Grant No.:11BTQ024)the Foundation for Humanities and Social Sciences of the Chinese Ministry of Education (Grant No.:10YJC87004)
文摘Purpose: To design an efficient high-performance algorithm for semantic annotation of biodiversity documents in Chinese.Design/methodology/approach: Data set consists of 1,000 randomly selected documents from Flora of China. Comparative evaluation of the proposed approach with the Na ve Bayes algorithm have been developed before for the same purpose.Findings: Experimental results show that the heuristics based algorithm outperformed the Na ve Bayes algorithm. The use of leading words helped improving the annotation performance while prioritizing rule application based on their weights had no significant impact on algorithm performance.Research limitations: The ICTCLAS was used to identify word boundaries off-shelf without optimatization for biodiversity domain. This may have not made the best use of the tool.Practical implications & Originality/value: The performance of heuristics based approach,enhanced by leading words analysis, reached an F value of 0.9216, which is sufficiently accurate for practical use.
基金funded by the Deanship of Scientific Research at Jouf University under Grant number DSR-2022-RG-0101。
文摘As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain.The proposed framework comprises three core modules:legal feature extraction,semantic similarity assessment,and verdict recommendation.For legal feature extraction,a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts.Semantic similarity between cases is evaluated using a hybrid method that combines rule-based logic with an LSTM model,analyzing the feature vectors of query cases against a legal knowledge base.Verdicts are then recommended through a rule-based retrieval system,enhanced by predefined legal statutes and regulations.By merging rule-based methodologies with deep learning,this framework addresses the interpretability challenges often associated with contemporary AImodels,thereby enhancing both transparency and generalizability across diverse legal contexts.The system was rigorously tested using a legal corpus of 43,000 case laws across six categories:Criminal,Revenue,Service,Corporate,Constitutional,and Civil law,ensuring its adaptability across a wide range of judicial scenarios.Performance evaluation showed that the feature extraction module achieved an average accuracy of 91.6%with an F-Score of 95%.The semantic similarity module,tested using Manhattan,Euclidean,and Cosine distance metrics,achieved 88%accuracy and a 93%F-Score for short queries(Manhattan),89%accuracy and a 93.7%F-Score for medium-length queries(Euclidean),and 87%accuracy with a 92.5%F-Score for longer queries(Cosine).The verdict recommendation module outperformed existing methods,achieving 90%accuracy and a 93.75%F-Score.This study highlights the potential of hybrid AI frameworks to improve judicial decision-making and streamline legal processes,offering a robust,interpretable,and adaptable solution for the evolving demands of modern legal systems.
文摘针对大规模开放在线课程(MOOC)质量评估维度单一、缺乏细粒度分析的问题,构建了一个以方面级情感分析为核心的语义切分至多维评估框架。基于中国大学MOOC平台中大数据与人工智能类课程的评论数据,利用通用信息抽取(universal information extraction,UIE)工具抽取影响要素词,通过K-means聚类与变异系数法,构建了涵盖5个一级指标和10个二级指标的加权评价体系。设计了基于影响要素词定位的语义切分(impact element word-based targeted comment segmentation and classification,ITCSC)算法,将长评论切分为方面级短句,结合SKEP情感模型实现多维度量化分析,揭示了内容配置、服务评价等维度的表现特征。实验表明,该框架兼顾整体趋势与细节特征,为课程优化及选课提供数据支持,丰富了细粒度教育质量评估路径。
基金Program for Changjiang Scholars and Innovative Research Team in University (NoIRT0652)the National High Technology Research and Development Program of China (863 Program) ( No2006AA01A123)
文摘To semantically integrate heterogeneous resources and provide a unified intelligent access interface, semantic web technology is exploited to publish and interlink machineunderstandable resources so that intelligent search can be supported. TCMSearch, a deployed intelligent search engine for traditional Chinese medicine （TCM）, is presented. The core of the system is an integrated knowledge base that uses a TCM domain ontology to represent the instances and relationships in TCM. Machine-learning techniques are used to generate semantic annotations for texts and semantic mappings for relational databases, and then a semantic index is constructed for these resources. The major benefit of representing the semantic index in RDF/OWL is to support some powerful reasoning functions, such as class hierarchies and relation inferences. By combining resource integration with reasoning, the knowledge base can support some intelligent search paradigms besides keyword search, such as correlated search, semantic graph navigation and concept recommendation.
