A Semantic Retrieval Method Based on the Fuzzy Reasoning

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.

Semantic-Based Video Retrieval Survey

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.

Automatic Construction Method for Domain Concepts Based on Wikipedia Semantic Knowledge Base

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.

Comparison of Ontology-Based Semantic-Similarity Measures in the Biomedical Text

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.

Alzheimer’s Disease Diagnosis Based on a Semantic Rule-Based Modeling and Reasoning Approach

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.

Semantic Analysis of Natural Language Queries for an Object Oriented Database

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.

Heuristics based semantic annotation of biodiversity documents in Chinese

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.

A Semantic-based Clustering Method to Build Domain Ontology from Multiple Heterogeneous Knowledge Sources

In order to improve the efficiency of ontology construction from heterogeneous knowledge sources, a semantic-based approach is presented. The ontology will be constructed with the application of cluster technique in an incremental way. Firstly, terms will be extracted from knowledge sources and congregate a term set after pretreat-ment. Then the concept set will be built via semantic-based clustering according to semanteme of terms provided by WordNet. Next, a concept tree is constructed in terms of mapping rules between semanteme relationships and concept relationships. The semi-automatic approach can avoid non-consistence due to knowledge engineers having different understanding of the same concept and the obtained ontology is easily to be expanded.

SEBS熔融接枝马来酸酐的研究

采用熔融接枝法在Haake转距流变仪上进行SEBS接枝马来酸酐(MAH)的反应,通过FT-IR验证了SEBS接枝产物的产生。用酸碱滴定法和FT-IR测定SEBS-g-MAH的接枝率,同时考察不同反应条件:反应时间、反应温度、MAH量、过氧化二异丙苯(DCP)量对SEBS-g-MAH的接枝率和接枝效率的影响。实验结果证明,SEBS接枝MAH的优化反应条件是:反应时间为10min,反应温度为160℃,MAH量为6%,DCP量为0.55%。

面向语义通信的信道知识库构建与信道处理研究综述

语义通信基于共享语义知识库辅助,提取信源语义特征进行编码传输,实现信息的“达意”传递,被认为是应对6G愿景的使能技术之一。为了构建高效、鲁棒的语义通信系统,深入研究语义通信中涉及无线信道建模与处理的相关内容必不可少,如挖掘信道特征以指导语义编/译码、对抗信道衰落与噪声以抑制语义信息误解等。旨在综述面向语义通信涉及无线信道建模与处理的研究进展,在概述语义通信原理基础上,总结了语义通信中涉及无线信道建模与处理的三个方面,包括信道知识库构建、物理信道处理和语义信道处理,综述了相关方面的研究方法与进展。结合已有研究成果,探讨了信道知识库构建和信道处理存在的挑战并展望了其未来发展方向。

车联网中基于位置语义的差分隐私保护方法

车联网中车辆获取基于位置服务时需要提供真实的位置信息,导致用户的轨迹隐私存在泄漏风险.针对车联网轨迹隐私保护中未考虑位置语义信息及生成的虚假轨迹可用性低问题,提出一种基于位置语义的差分隐私轨迹保护方法.首先,根据语义流行度和信息熵计算出真实位置对隐私预算的敏感程度,并设计实时隐私等级计算算法,同时搭建差分隐私模型实时分配隐私预算.然后划分语义Voronoi地图,对具有相同语义的虚假位置使用K-means算法进行聚类,并使用Hausdorff距离衡量轨迹相似度,以提升轨迹的可用性.在真实数据集上的仿真实验表明,所提方法与其他差分隐私保护方法相比轨迹可用性有明显的提升.

基于音素语义基的语音语义知识库构建方法

语义通信以保证信息含义的成功传递为目的,被视为下一代通信的潜在技术之一。然而,当前面向语音语义通信系统的语义知识库研究尚不成熟。为此,提出了一种基于音素的语音语义基模型,并基于双向长短期记忆网络提取音素语义基。进一步,通过将音素语义基与知识图谱的文本知识结构结合,构建了一种集语音含义及语音特性为一体的语音语义知识库框架,该框架生成具有知识结构的音素语义基,以此作为发送方和接收方之间共享的背景知识,支撑语音语义信息的高效、简约表征。仿真结果表明,相较于经典的语音语义通信系统,该框架在不同信噪比(SNR)下的语音质量(PESQ)和可懂度(STOI)都有显著提高,其中,在信噪比相同的条件下,基于所提语义知识库的语音语义通信系统在PESQ方面性能提升了8%,有效验证了所构建语义知识库的有效性。

基于改进Res-UNet的昼夜地基云图分割网络

针对昼夜地基云图在分割中细节信息丢失、分割精度低等问题,提出一种基于改进Res-UNet(Residual network-UNetwork)的昼夜地基云图分割网络CloudRes-UNet(Cloud ResNet-UNetwork),整体采用编码器-解码器的网络结构。首先,编码器使用ResNet50提取特征,增强特征提取能力;其次,设计多级特征提取(Multi-Stage)模块,该模块结合分组卷积、膨胀卷积和通道打乱这3种技巧,获取高强度语义信息;再次,加入高效通道注意力(ECA‑Net)模块,在通道维度上聚焦重要信息,加强对地基云图中云区域的关注,提高分割精度;最后,解码器使用双线性插值对特征进行上采样,提高分割图像的清晰度并减少目标和位置信息丢失。实验结果表明,与当前基于深度学习表现较好的地基云图分割网络(Cloud-UNet)相比,CloudRes-UNet在昼夜地基云图分割数据集上的分割准确率提升了1.5个百分点,平均交并比(MIoU)上升了1.4个百分点,更准确地获取了云量信息,对天气预报、气候研究和光伏发电等方面具有积极意义。

基于汉字形音义多元知识和标签嵌入的文本语义匹配模型

文本语义匹配指基于给定的文本判别文本之间的语义关系。针对该任务,现有模型的信息编码未考虑利用除汉字字符外的潜在语义信息,且在分类时未考虑标签信息对模型性能的影响。因此,该文提出了一种使用汉字形音义多元知识和标签嵌入的文本语义匹配方法。首先,通过信息编码层对汉字的形音义的多元知识进行编码;其次,通过信息整合层获取融合汉字形音义多元知识的联合表示;然后,经过标签嵌入层利用编码后的分类标签与汉字形音义的联合表示生成信号监督标签;最后,经过标签预测层获取文本层面与标签层面的联合信息表示,进而对文本语义关系进行最终的判别。在多个数据集上的实验结果显示,该文提出的模型优于多个基线模型,验证了模型的有效性。

融合歧义感知的检索式问答方法

针对多义词在不同上下文中语义表达不一致的问题,提出了一个融合歧义感知的问答模型,即模型在问题-候选答案的语义匹配过程中,与外部知识源相结合,动态识别并检测出每个多义词在不同场景下的语义,并将检测到的语义信息进行特征编码后融合到语义匹配任务中,使模型能够更为准确地理解每个词的精准含义,从而做出更为精准的匹配判断.在歧义感知模型的设计上,采用基于Transformer的深度语义编码器,使其能够更加全方位地抓取到待分析歧义词以及知识源的深度语义特征,从而做出更加准确的语义消歧.在标准检索式问答数据集上(Wiki QA和TrecQA)的实验结果表明,所提出的歧义感知的问答方法能够有效融合到多个基线模型中,并捕捉到多义词在不同语境中的精准语义,使其在包含公开数据集上的问答性能MAP评估高于对应基线模型约1%,且该语义特征使得基于BERT的文本相似性匹配模型的性能优于当前先进的其它模型.

基于多尺度特征的地基云图分类检测算法

地基云的自动识别方法和技术为气象分析中的云状识别和云量估计任务提供了重要的手段和依据。然而,对这两种任务的研究往往独立,互不相干,导致地基云图的分类与分割技术无法有效地结合使用。特别是当云图中出现多类云状时,现有技术难以按不同云类分别划分区域并进行云量计算。为了解决这一问题,提出用基于深度学习的语义分割方法实现对地基云图的按类分割。首先,构建了地基云图语义分割数据集GBCSS,该数据集包含3000幅云图,共计11个类别。在此基础上,提出了一种基于U型神经网络的改进方案UNet-PPM作为地基云图语义分割模型。为了增强网络对云的轮廓特征提取能力,引入了金字塔池化模块。该模块提取并聚合了不同尺度的图像特征,提升了网络获取全局信息的能力。最后,将设计的网络在GBCSS上进行了训练以及评估,其在测试集上达到了91.5%的像素准确率。与U-Net相比,UNet-PPM在像素准确率上有5.4%的提升,表明该网络对云的轮廓特征提取的能力更强,以及语义分割应用在地基云图中的可行性。

面向知识库协同演进的知识通信

面向语义通信中模型化知识库更新的需求,针对庞大而精确的模型参数传输对通信资源和信道条件要求过高的问题,对知识通信问题进行建模和简化,并以此提出一种基于载体数据的知识库协同演进方法。该方法以数据为知识载体,通过基于载体数据采样、传输和学习分别实现知识的表述、传输与更新,并以最小化知识差异为目标,使用遗传算法优化载体数据的采样。与基于模型传输方法的对比实验结果表明,本方法只用十分之一的传输量就能实现更低的知识差异,且具有抗噪声能力强、可兼容原有知识和异构知识库的优势。

Development of an Ontology-Based Knowledge Network by Interconnecting Soil/Water Concepts/Properties, Derived from Standards Methods and Published Scientific References Outlining Infiltration/Percolation Process of Contaminated Water

The present work deals with the development of an Ontology-Based Knowledge Network of soil/water physicochemical & biological properties (soil/water concepts), derived from ASTM Standard Methods (ASTMi,n) and relevant scientific/applicable references (published papers—PPi,n) to fill up/bridge the gap of the information science between cited Standards and infiltration discipline conceptual vocabulary providing accordingly a dedicated/internal Knowledge Base (KB). This attempt constitutes an innovative approach, since it is based on externalizing domain knowledge in the form of Ontology-Based Knowledge Networks, incorporating standardized methodology in soil engineering. The ontology soil/water concepts (semantics) of the developed network correspond to soil/water physicochemical & biological properties, classified in seven different generations that are distinguished/located in infiltration/percolation process of contaminated water through soil porous media. The interconnections with arcs between corresponding concepts/properties among the consecutive generations are defined by the relationship of dependent and independent variables. All these interconnections are documented according to the below three ways: 1) dependent and independent variables interconnected by using the logical operator “depends on” quoting existent explicit functions and equations;2) dependent and independent variables interconnected by using the logical operator “depends on” quoting produced implicit functions, according to Rayleigh’s method of indices;3) dependent and independent variables interconnected by using the logical operator “related to” based on a logical dependence among the examined nodes-concepts-variables. The aforementioned approach provides significant advantages to semantic web developers and web users by means of prompt knowledge navigation, tracking, retrieval and usage.

基于List-Based转移的英语语义分析翻译系统研究

针对智能语义分析方法中的非投射弧问题,在List-Based转移算法的基础上,文中结合抽象语义关系图,提出了一种语义分析翻译系统。利用对比大量词语对的方法,List-Based转移算法可以获取这些词语的语义关系,且同时还具有较小的时间复杂度,再结合改进的JAMR对齐器和Oracle分析器,最终提出了基于List-Baesd转移的语义分析翻译系统。该翻译系统可以准确识别出英语等语言的词语顺序变化,从而避免出现智能语义分析方法的非投射弧问题。相关的仿真结果表明,与常用的智能语义分析方法相比,基于List-Based转移算法的语义分析翻译系统具有更加优秀的分析和翻译准确度。