Study on association rules mining based on semantic relativity

An association rules mining method based on semantic relativity is proposed to solve the problem that there are more candidate item sets and higher time complexity in traditional association rules mining.Semantic relativity of ontology concepts is used to describe complicated relationships of domains in the method.Candidate item sets with less semantic relativity are filtered to reduce the number of candidate item sets in association rules mining.An ontology hierarchy relationship is regarded as a directed acyclic graph rather than a hierarchy tree in the semantic relativity computation.Not only direct hierarchy relationships,but also non-direct hierarchy relationships and other typical semantic relationships are taken into account.Experimental results show that the proposed method can reduce the number of candidate item sets effectively and improve the efficiency of association rules mining.

Knowledge acquisition, semantic text mining, and security risks in health and biomedical informatics

Computational techniques have been adopted in medi-cal and biological systems for a long time. There is no doubt that the development and application of computational methods will render great help in better understanding biomedical and biological functions. Large amounts of datasets have been produced by biomedical and biological experiments and simulations. In order for researchers to gain knowledge from origi- nal data, nontrivial transformation is necessary, which is regarded as a critical link in the chain of knowledge acquisition, sharing, and reuse. Challenges that have been encountered include: how to efficiently and effectively represent human knowledge in formal computing models, how to take advantage of semantic text mining techniques rather than traditional syntactic text mining, and how to handle security issues during the knowledge sharing and reuse. This paper summarizes the state-of-the-art in these research directions. We aim to provide readers with an introduction of major computing themes to be applied to the medical and biological research.

A Novel Cross-Media Layered Semantic Mining Model

This paper presents a cross-media semantic mining model （CSMM） based on object semantic. This model obtains object-level semantic information in terms of maximum probability principle. Then semantic templates are trained and constructed with STTS （Semantic Template Training System）, which are taken as the bridge to realize the transition from various low-level media feature to object semantic. Furthermore, we put forward a kind of double layers metadata structure to efficaciously store and manage mined low-level feature and high-level semantic. This model has broad application in lots of domains such as intelligent retrieval engine, medical diagnoses, multimedia design and so on.

A State-of-the-Art Survey on Semantic Web Mining

The integration of the two fast-developing scientific research areas Semantic Web and Web Mining is known as Semantic Web Mining. The huge increase in the amount of Semantic Web data became a perfect target for many researchers to apply Data Mining techniques on it. This paper gives a detailed state-of-the-art survey of on-going research in this new area. It shows the positive effects of Semantic Web Mining, the obstacles faced by researchers and propose number of approaches to deal with the very complex and heterogeneous information and knowledge which are produced by the technologies of Semantic Web.

Semantic network based component organization model for program mining

Based on the definition of component ontology, an effective component classification mechanism and a facet named component relationship are proposed. Then an application domain oriented, hierarchical component organization model is established. At last a hierarchical component semantic network (HCSN) described by ontology interchange language(OIL) is presented and then its function is described. Using HCSN and cooperating with other components retrieving algorithms based on component description, other components information and their assembly or composite modes related to the key component can be found. Based on HCSN, component directory library is catalogued and a prototype system is constructed. The prototype system proves that component library organization based on this model gives guarantee to the reliability of component assembly during program mining.

Tag clustering algorithm LMMSK: improved K-means algorithm based on latent semantic analysis

With the wide application of Web-2.0 and social software, there are more and more tag-related studies and applications. Because of the randomness and the personalization in users' tagging, tag research continues to encounter data space and semantics obstacles. With the min-max similarity (MMS) to establish the initial centroids, the traditional K-means clustering algorithm is firstly improved to the MMSK-means clustering algorithm, the superiority of which has been tested; based on MMSK-means and combined with latent semantic analysis (LSA), here secondly emerges a new tag clustering algorithm, LMMSK. Finally, three algorithms for tag clustering, MMSK-means, tag clustering based on LSA (LSA-based algorithm) and LMMSK, have been run on Matlab, using a real tag-resource dataset obtained from the Delicious Social Bookmarking System from 2004 to 2009. LMMSK's clustering result turns out to be the most effective and the most accurate. Thus, a better tag-clustering algorithm is found for greater application of social tags in personalized search, topic identification or knowledge community discovery. In addition, for a better comparison of the clustering results, the clustering corresponding results matrix (CCR matrix) is proposed, which is promisingly expected to be an effective tool to capture the evolutions of the social tagging system. © 2017 Beijing Institute of Aerospace Information.

Semantic Sentence Similarity Using Finite State Machine

In this paper, a finite state machine approach is followed in order to find the semantic similarity of two sentences. The approach exploits the concept of bi-directional logic along with a semantic ordering approach. The core part of this approach is bi-directional logic of artificial intelligence. The bi-directional logic is implemented using Finite State Machine algorithm with slight modification. For finding the semantic similarity, keyword has played climactic importance. With the help of the keyword approach, it can be found easily at the sentence level according to this algorithm. The algorithm is proposed especially for Nepali texts. With the polarity of the individual keywords, the finite state machine is made and its final state determines its polarity. If two sentences are negatively polarized, they are said to be coherent, otherwise not. Similarly, if two sentences are of a positive nature, they are said to be coherence. For measuring the coherence (similarity), contextual concept is taken into consideration. The semantic approach, in this research, is a totally contextual based method. Two sentences are said to be semantically similar if they bear the same context. The total accuracy obtained in this algorithm is 90.16%.

基于模糊DEMATEL-ISM的矿山企业安全生产系统关键影响因素研究

探讨了我国矿山企业安全生产系统风险影响因素及相关影响机制,从人的行为、安全环境、安全管理、设备设施、科学技术5个层面建立了矿山企业安全生产系统风险因素体系。结合模糊集理论构建决策实验室分析和解释结构模型,对矿山企业安全生产系统风险因素的传递强度和传递结构进行可视化研究,量化矿山企业安全生产系统各风险因素之间的逻辑关系及其影响机制。计算结果表明,模糊集理论可以有效解决计算过程中存在模糊性和不确定性问题;安全监督管理和安全生产关键技术的影响度最高,分别为2.728和2.702;安全理论基础研究和安全投入费用被影响度较高,分别为2.443和2.321,属于重要风险因素;安全理论基础研究的中心值为4.987,属于根本因素,对矿山企业安全生产具有根本性和深远性的影响。

数智时代的档案文化:样态、挖掘与传播

随着数字技术的持续演进,档案文化资源的数字化整合、智能化发掘、可视化展示、立体化传播将成为新常态,呼唤着档案工作在理念、路径、方法等层面进行系统性变革。系统解构了数智时代档案文化的新样态,阐释了档案文化价值的挖掘路径,提出了创新档案文化传播策略。展望未来,需要通过技术创新与机制创新的协同联动,构建与时代发展相适应的档案文化治理体系,全面提升档案文化服务效能,以更好地服务新时代中国特色社会主义文化建设。

大语言模型赋能的知识挖掘与文档整合研究

随着大数据、人工智能技术的不断发展,大语言模型(Large Language Model,LLM)在知识挖掘、文档整合等领域显示出巨大的潜力。该文通过知识图谱构建、文本分类、信息检索等方法,对大语言模型的架构及其在不同场景下的应用进行探讨,并对知识的提炼和整合进行深入探讨。研究如何提高多文档协同处理的效率,通过标准化的结构和语义的融合技术。并结合实际案例分析,展示大语言模型在复杂知识体系中的应用效果,以供实际运用大语言模型时参考。

基于SMS的旅游咨询信息系统的设计与实现

通过介绍国内旅游业的背景引出旅游咨询信息系统,基于SMS模式阐述系统设计原则。以云南省丽江市的旅游现状为列,说明系统总体设计、网关逻辑结构设计及系统模块设计。在系统开发过程中,主要应用数据挖掘技术和短信网关技术。

Construction and application of formal ontology for mine

Digital mine is the only way for the development of mining industry in China. Due to lack of appropriate standards and norms, and different awareness in the field of digital mine among academia and industry insiders, the meaning for digital mine is still unclear. Starting from the nature of mining and removing of views of specialized fields, this paper constructs formal ontology for digital mine and proposes the four levels for it. The ontology clarifies the concept world for digital mine, defines the meaning of concepts and relations clearly, provides a reference for the standard construction for digital mine and provides a unified semantic framework for the integration of heterogeneous mine data. Meanwhile, it can provide formal reasoning knowledge for expert system of digital mine and improve the intelligence and automation while the machine automatically interpreting and processing mine spatial data.

LOD Cloud Mining for Prognosis Model(Case Study: Native App for Drug Recommender System)

The goal of this project is to use the Semantic Web Technologies and Data Mining for disease diagnosis to assist health care professionals regarding the possible medication and drug to prescribe (Drug recommendation) according to the features of the patient. Numerous Decision Support Systems (DSS) and Expert Systems allow medical collaboration, like in the differential diagnosis specific or general. But, a medical recommendation system using both Semantic Web technologies and Data mining has not yet been developed which initiated this work. However, it should be mentioned that there are several system references about medicine or active ingredient interactions, but their final goal is not the Drug recommendation which uses above technologies. With this project we try to provide an assistant to the doctor for better recommendations. The patient will also able to use this system for explanation of drugs, food interaction and side effects of corresponding drugs.

An Opinion Mining Task in Turkish Language： A Model for Assigning Opinions in Turkish Blogs to the Polarities

Global changes took place at a neck-breaking speed in lots of fields along with the Web 2.0 era, which can be stated as the new Internet trend. Web pages which once were a statical structure that can be said to become dynamic pages created by users, and in this regard they can be said to have been democratized by evolving. Social media, which were structured alongside with this era, by providing a large data flow for businesses, present new and improvable opportunities in the field of creating effective strategies. There are lots of blogs in today＇s Internet environment which includes customer ideas regarding the products/services that they possess. This environment, which in a way globalizes the customer ideas, is a new medium suitable for examination in terms of its increasing the business-customer interaction and due to its transporter nature; it provides the text data that may be analyzed in the field of Customer Relationship Management to businesses. Thus, businesses should follow blog environments to see how the product/service they provide is greeted in terms of the customer focus and it should be seen as an important job on which they can conduct effective analyses. For this purpose, a model proposal that will assign the ideas to the Turkish blogs was given in the study. Opinion mining methods were used in the model, and so to perceive a general look-on about products/services, a methodology was devised, which will assign the text based opinion data on the Turkish blogs to the poles. Success of the pole assignment of the model is evaluated with the precision measure.

Environmental complaint insights through text mining based on the driver,pressure,state,impact,and response(DPSIR)framework:Evidence from an Italian environmental agency

Individuals,local communities,environmental associations,private organizations,and public representatives and bodies may all be aggrieved by environmental problems concerning poor air quality,illegal waste disposal,water contamination,and general pollution.Environmental complaints represent the expressions of dissatisfaction with these issues.As the timeconsuming of managing a large number of complaints,text mining may be useful for automatically extracting information on stakeholder priorities and concerns.The paper used text mining and semantic network analysis to crawl relevant keywords about environmental complaints from two online complaint submission systems:online claim submission system of Regional Agency for Prevention,Environment and Energy(Arpae)(“Contact Arpae”);and Arpae's internal platform for environmental pollution(“Environmental incident reporting portal”)in the Emilia-Romagna Region,Italy.We evaluated the total of 2477 records and classified this information based on the claim topic(air pollution,water pollution,noise pollution,waste,odor,soil,weather-climate,sea-coast,and electromagnetic radiation)and geographical distribution.Then,this paper used natural language processing to extract keywords from the dataset,and classified keywords ranking higher in Term Frequency-Inverse Document Frequency(TF-IDF)based on the driver,pressure,state,impact,and response(DPSIR)framework.This study provided a systemic approach to understanding the interaction between people and environment in different geographical contexts and builds sustainable and healthy communities.The results showed that most complaints are from the public and associated with air pollution and odor.Factories(particularly foundries and ceramic industries)and farms are identified as the drivers of environmental issues.Citizen believed that environmental issues mainly affect human well-being.Moreover,the keywords of“odor”,“report”,“request”,“presence”,“municipality”,and“hours”were the most influential and meaningful concepts,as demonstrated by their high degree and betweenness centrality values.Keywords connecting odor(classified as impacts)and air pollution(classified as state)were the most important(such as“odor-burnt plastic”and“odor-acrid”).Complainants perceived odor annoyance as a primary environmental concern,possibly related to two main drivers:“odor-factory”and“odorsfarms”.The proposed approach has several theoretical and practical implications:text mining may quickly and efficiently address citizen needs,providing the basis toward automating(even partially)the complaint process;and the DPSIR framework might support the planning and organization of information and the identification of stakeholder concerns and priorities,as well as metrics and indicators for their assessment.Therefore,integration of the DPSIR framework with the text mining of environmental complaints might generate a comprehensive environmental knowledge base as a prerequisite for a wider exploitation of analysis to support decision-making processes and environmental management activities.

一种两阶段的中文专利语义检索方法

专利检索系统主要以传统的术语匹配方式提供检索服务,语义扩展性不足,使得具有语义相似的专利在Top_N的检出率较低.为了提升相似专利的Top_N检出率,该文提出了一种两阶段的中文专利语义检索方法.第1阶段基于Sentence-BERT进行语义编码,然后基于近似最近邻算法进行语义匹配,能够从海量专利文献库中快速匹配到语义相似的专利.第2阶段以BERT为基础模型,基于交叉编码器(Cross-Encoder)捕获专利文本之间更细粒度的语义相关性,对第1阶段的候选专利集进行重新排序.此外,该文还提出了难负例(hard negative)采样和白化转换(whitening)两种简单有效的模型训练优化策略,使模型从简单的训练数据逐渐过度到复杂的训练数据,提高模型区分相似专利的能力.实验表明,该文提出的方法相比于主流的方法在检出率上均有提升,且相比市面上现有的检索系统同样具有优势.

地下矿道无人车可行驶区域检测算法

地下矿道可行驶区域检测是地下矿山自动驾驶系统的关键感知技术,然而地下矿道光照强度低、工况复杂的特点给该任务带来极大挑战。鉴于此,本文提出一种地下矿道可行驶区域检测算法。首先,为解决地下矿道细节退化导致图像特征难以提取的问题,提出一种双分支特征提取骨干网络;然后,针对地下矿道可行驶区域检测不完整问题,提出一种自适应多尺度空间空洞池化金字塔特征增强模块;最后,为解决地下矿道边界提取不准确的问题,设计一种双分支通道注意力机制融合模块。在自制矿道可行驶区域数据集上进行实验,相较于Deeplabv3+、UNet、DDRNet-23、PIDNet,本文算法取得最佳效果,在MIoU分数上分别提升2.07、2.39、1.87、1.92个百分点,在mAcc分数上分别提升1.78、2.45、1.84、1.86。本文算法已成功应用于地下无人驾驶矿车,验证了其在真实矿道场景下的有效性。

基于语义特征挖掘的图书馆文献资源智能检索方法

为提升图书馆文献资源检索精度,使检索结果与索引标准、用户需求适配,文章提出基于语义特征挖掘的图书馆文献资源智能检索方法。构建图书馆元数据特征空间,计算特征空间量化结果与特征信息的聚类范围;对图书馆文献资源信息进行编码重构与分布采集,提取图书馆文献资源检索特征;计算特征相似度分数,提取显性关键词;利用加权向量组合控制方法优化图书馆文献检索,实现文献资源智能检索输出。实验结果表明,利用所提方法获取的检索结果,适配度均在98%以上,最高检索精度达到0.97,有较好实际应用效果。

融合边界注意力的特征挖掘息肉小目标网络

从结肠图像中分割息肉小目标病变区域对于预防结直肠癌至关重要,它可以为结直肠癌的诊断提供有价值的信息。然而目前现有的方法存在2个局限性:一是不能稳健捕获全局上下文信息,二是未能充分挖掘细粒度细节特征信息。因此,提出融合边界注意力的特征挖掘息肉小目标网络(transformer feature boundary network,TFB-Net)。该网络主要包括3个核心模块:首先,采用Transformer辅助编码器建立长程依赖关系,补充全局信息;其次,设计特征挖掘模块进一步细化特征,学习到更好的特征;最后,使用边界反转注意力模块加强对边界语义空间的关注,提高区域辨别能力。在5个息肉小目标数据集上进行广泛实验,实验结果表明TFBNet具有优越的分割性能。

基于图神经网络的人工自然语言语义挖掘仿真

语义挖掘工具可从批量非结构化人工自然语言文本数据中准确提取有用信息,但是由于网络环境文本具备半结构化、多尺度、海量、复杂关联等属性,导致文本数据通常维度较高,且仅有小部分节点存在明确标签,因此语义挖掘难度较大。提出基于图神经网络的人工自然语言语义挖掘方法。结合多头注意力机制和半监督图卷积神经网络对人工自然语言文本降维处理。联合改进的模糊C均值聚类算法和免疫单亲遗传算法,构建人工自然语言语义挖掘算法。实验结果表明,研究方法的聚类纯度、准确率和召回率均高于95%,说明上述方法的应用性能较优。