Semantic compared cross impact analysis

The aim of cross impact analysis (CIA) is to predict the impact of a first event on a second. For organization’s strategic planning, it is helpful to identify the impacts among organization’s internal events and to compare these impacts to the corresponding impacts of external events from organization’s competitors. For this, literature has introduced compared cross impact analysis (CCIA) that depicts advantages and disadvantages of the relationships between organization’s events to the relationships between competitors’ events. However, CCIA is restricted to the use of patent data as representative for competitors’ events and it applies a knowledge structure based text mining approach that does not allow considering semantic aspects from highly unstructured textual information. In contrast to related work, we propose an internet based environmental scanning procedure to identify textual patterns represent competitors’ events. To enable processing of this highly unstructured textual information, the proposed methodology uses latent semantic indexing (LSI) to calculate the compared cross impacts (CCI) for an organization. A latent semantic subspace is built that consists of semantic textual patterns. These patterns are selected that represent organization’s events. A web mining approach is used for crawling textual information from the internet based on keywords extracted from each selected pattern. This textual information is projected into the same latent semantic subspace. Based on the relationships between the semantic textual patterns in the subspace, CCI is calculated for different events of an organization. A case study shows that the proposed approach successfully calculates the CCI for technologies processed by a governmental organization. This enables decision makers to direct their investments more targeted.     

基于互信息约束聚类的图像语义标注

提出一种基于互信息约束聚类的图像标注算法。采用语义约束对信息瓶颈算法进行改进,并用改进的信息瓶颈算法对分割后的图像区域进行聚类,建立图像语义概念和聚类区域之间的相互关系;对未标注的图像,提出一种计算语义概念的条件概率的方法,同时考虑训练图像的先验知识和区域的低层特征,最后使用条件概率最大的语义关键字对图像区域语义自动标注。对一个包含500幅图像的图像库进行实验,结果表明,该方法比其他方法更有效。     

On a Role for Copula's in Jeffrey's Rule with An Application to Decision Making

We introduce Jeffrey's rule of conditioning. We explain how it enables us to determine the current probability of an event using a collection of conditional probabilities of the event determined from past experiences and the current probabilities of the conditioning events. We note the importance of the joint probabilities of the event of interest and the conditioning events in obtaining the required conditional probabilities. We investigate the use of copula's to help obtain these required joint probabilities. We then apply our results to a problem of financial decision making in which the success of the stock issue of a new company depends on the quality of management of company. Here, past history tells information about the success of a typical company based on its quality of management and our own observations provides information about the quality of the current companies management.     

基于概率粗糙集模型的信息检索

针对经典粗糙集模型难以分类标引空间以及体现类间关联的缺陷,将条件概率关系结合粗糙集理论引入信息检索,提出一种基于概率粗糙集的信息检索模型。定义标引词空间的条件概率关系,自动挖掘概念相似类形成概念空间。定义文档与查询、文档与文档间语义贴近度的计算方法。根据贴近度实现检索匹配结果的排序输出。仿真实例表明了该方法的可行性和有效性。     

Weak signal identification with semantic web mining

We investigate an automated identification of weak signals according to Ansoff to improve strategic planning and technological forecasting. Literature shows that weak signals can be found in the organization’s environment and that they appear in different contexts. We use internet information to represent organization’s environment and we select these websites that are related to a given hypothesis. In contrast to related research, a methodology is provided that uses latent semantic indexing (LSI) for the identification of weak signals. This improves existing knowledge based approaches because LSI considers the aspects of meaning and thus, it is able to identify similar textual patterns in different contexts. A new weak signal maximization approach is introduced that replaces the commonly used prediction modeling approach in LSI. It enables to calculate the largest number of relevant weak signals represented by singular value decomposition (SVD) dimensions. A case study identifies and analyses weak signals to predict trends in the field of on-site medical oxygen production. This supports the planning of research and development (R&D) for a medical oxygen supplier. As a result, it is shown that the proposed methodology enables organizations to identify weak signals from the internet for a given hypothesis. This helps strategic planners to react ahead of time.     

基于事件语义特征的中文文本蕴含识别

为了强化文本蕴含系统深层语义分析与推理能力,该文提出了基于事件语义特征的中文文本蕴含识别方法。该方法基于事件标注语料生成事件图,将文本间的蕴含关系转化为事件图间的蕴含关系;利用最大公共子图的事件图相似度算法计算事件语义特征,与统计特征、词汇语义特征和句法特征一起使用支持向量机进行分类,得到初步实验结果,再经过基于事件语义规则集合的修正处理得到最后的识别结果。实验结果表明基于事件语义特征的中文文本蕴含识别方法可以更有效地对中文文本蕴含关系进行识别。     

条件事件代数研究综述

综述了条件事件代数理论的原理、主要性质和应用．条件事件代数是一门新兴的解决不确定性、概率性和模糊性推理问题的学科,是在确保规则概率与条件概率相容的前提下,把布尔代数上的逻辑运算推广到条件事件(规则)集合中得到的代数系统,目的是为智能系统中的条件推理建立一个数学基础．该文也对比条件事件代数更一般的逻辑系统——关联事件代数理论进行了介绍．     

Semantic-aware top-k spatial keyword queries

The fast development of GPS equipped devices has aroused widespread use of spatial keyword querying in location based services nowadays. Existing spatial keyword query methodologies mainly focus on the spatial and textual similarities, while leaving the semantic understanding of keywords in spatial Web objects and queries to be ignored. To address this issue, this paper studies the problem of semantic based spatial keyword querying. It seeks to return the k objects most similar to the query, subject to not only their spatial and textual properties, but also the coherence of their semantic meanings. To achieve that, we propose novel indexing structures, which integrate spatial, textual and semantic information in a hierarchical manner, so as to prune the search space effectively in query processing. Extensive experiments are carried out to evaluate and compare them with other baseline algorithms.     

Joint probability for evaluating the schedule and cost of stochastic simulation models

The review of construction engineering and management literature shows that the occurrence of multi-performance indices in stochastic simulation models have been treated the same way as the occurrence of a single performance index. By doing so, the correlation between these indices and the impact they have on each other are ignored. Their occurrences have been treated as disjoint, which leads to errors in evaluating the probabilities of the performance indices of these models. The objectives of this paper are to present a new method that can: (1) quantify the impact of uncertainty on the project schedule and cost simultaneously; (2) calculate the conditional probability of the project cost given a specific project duration, and vice versa; (3) find the best project duration and cost that meet a specific joint probability; (4) estimate the project schedule and cost joint contingency using joint probability; and (5) generate a schedule representing a specific joint probability. The paper presents the implementation details and several case studies to demonstrate the feasibility of the proposed method. The proposed method shall provide a more accurate analysis to the output of stochastic simulation.     

基于概率超图的视频事件语义检测

视频事件类别的归属具有模糊性和不确定性,将超图的点边射入矩阵拓展成概率形式的软超图进行关联关系分析和语义分析,将会更有利于提高多事件检索检测的精准率和召回率。提出基于概率超图模型的视频事件语义检测算法（PHVESD）。 该方法首先将颜色、灰度共生矩阵、Tchebichef矩、局部二值模式（LBP）等四种底层视觉特征进行融合; 然后定义视频段的亲密度函数并利用亲密度的信息构建概率超图模型,其中每条超边对应一种事件语义;采用随机游走过程来预测视频段属于每条超边的概率;最后结合阈值采用条件概率模型对视频段进行事件语义分类。将该方法用于交通突发事件多语义检测中并与其他的识别算法相比较,实验结果表明,与基于超图模型的多标签随机游走算法（MLRW）相比,PHVESD的算法使多语义事件检测的准确率提高了10%,召回率提高了8%。     

Distributions of surfers' paths through the World Wide Web: Empirical characterizations

Surfing the World Wide Web (WWW) involves traversing hyperlink connections among documents. The ability to predict surfing patterns could solve many problems facing producers and consumers of WWW content. We analyzed WWW server logs for a WWW site, collected over ten days, to compare different path reconstruction methods and to investigate how past surfing behavior predicts future surfing choices. Since log files do not explicitly contain user paths, various methods have evolved to reconstruct user paths. Session times, number of clicks per visit, and Levenshtein Distance analyses were performed to show the impact of various reconstruction methods. Different methods for measuring surfing patterns were also compared. Markov model approximations were used to model the probability of users choosing links conditional on past surfing paths. Information‐theoretic (entropy) measurements suggest that information is gained by using longer paths to estimate the conditional probability of link choice given surf path. The improvements diminish, however, as one increases the length of path beyond one. Information‐theoretic (total divergence to the average entropy) measurements suggest that the conditional probabilities of link choice given surf path are more stable over time for shorter paths than longer paths. Direct examination of the accuracy of the conditional probability models in predicting test data also suggests that shorter paths yield more stable models and can be estimated reliably with less data than longer paths. This revised version was published online in August 2006 with corrections to the Cover Date.     

无监督的中文商品属性结构化方法

从非结构化商品描述文本中抽取结构化属性信息,对于电子商务实现商品的对比与推荐及用户需求预测等功能具有重要意义.现有结构化方法大多采用监督或半监督的分类方法抽取属性值与属性名,通过文法分析器分析属性值与属性名之间的文法依存关系,并根据关联规则实现属性值与属性名的匹配.这些方法存在以下不足：（1）需要人工标记部分属性值、属性名及它们之间的对应关系;（2）属性值-属性名匹配的准确度受到语言习惯、句意逻辑、语料库及属性名候选集质量的严重制约.提出了一种无监督的中文商品属性结构化方法.该方法借助搜索引擎,基于小概率事件原理分析文法关系来抽取属性值与属性名.同时,提出相对不选取条件概率场,并使用Page Rank算法来计算属性值与属性名的配对概率.该方法无需人工标记的开销,且无论商品描述中是否显式地包含相应的属性名,该方法都能自动抽取到属性值并匹配相应的属性名.使用百度搜索引擎上的真实语料,针对4类商品的中文描述进行了实验.实验结果验证了对于候选属性名的自动生成,所提出的基于搜索引擎搜索属性值,并在包含属性值的搜索结果中抽取一般名词的候选属性名生成方法与只在描述句中抽取一般名词的候选属性名生成方法相比,查全率提高了20%以上;对于非量化类属性,所提出的基于相对不选取条件概率场的属性值-属性名匹配方法与基于依存关联的方法相比,Rank-1的准确率提高了30%以上,平均MRR提高了0.3以上.     

一种基于文档内容的语义标注方法

互联网上存在海量数据,如何在大量的信息中查找到有用信息就变成了一个至关重要的问题。语义网为解决这一问题带来了曙光。然而当今网络现状与语义网之间存在巨大差距,即海量非结构化的页面内容难直接转化为语义的知识。提出了一种基于文档内容的语义标注方法,利用本体所表达的语义环境,即本体知识相关词汇及其所处的语义上下文环境在文档中出现频率,实现对文档的语义标注。实验显示方法取得良好的效果,但受本体知识质量和标注文档质量两个因素影响较大。     

Semantic weak signal tracing

The weak signal concept according to Ansoff has the aim to advance strategic early warning. It enables to predict the appearance of events in advance that are relevant for an organization. An example is to predict the appearance of a new and relevant technology for a research organization. Existing approaches detect weak signals based on an environmental scanning procedure that considers textual information from the internet. This is because about 80% of all data in the internet are textual information. The texts are processed by a specific clustering approach where clusters that represent weak signals are identified. In contrast to these related approaches, we propose a new methodology that investigates a sequence of clusters measured at successive points in time. This enables to trace the development of weak signals over time and thus, it enables to identify relevant weak signal developments for organization’s decision making in strategic early warning environment.     

Fuzzy independence and extended conditional probability

In many applications, the use of Bayesian probability theory is problematical. Information needed to feasibility calculate is unavailable. There are different methodologies for dealing with this problem, e.g., maximal entropy and Dempster-Shafer Theory. If one can make independence assumptions, many of the problems disappear, and in fact, this is often the method of choice even when it is obviously incorrect. The notion of independence is a 0–1 concept, which implies that human guesses about its validity will not lead to robust systems. In this paper, we propose a fuzzy formulation of this concept. It should lend itself to probabilistic updating formulas by allowing heuristic estimation of the “degree of independence.” We show how this can be applied to compute a new notion of conditional probability (we call this “extended conditional probability”). Given information, one typically has the choice of full conditioning (standard dependence) or ignoring the information (standard independence). We list some desiderata for the extension of this to allowing degree of conditioning. We then show how our formulation of degree of independence leads to a formula fulfilling these desiderata. After describing this formula, we show how this compares with other possible formulations of parameterized independence. In particular, we compare it to a linear interpolant, a higher power of a linear interpolant, and to a notion originally presented by Hummel and Manevitz [Tenth Int. Joint Conf. on Artificial Intelligence, 1987]. Interestingly, it turns out that a transformation of the Hummel-Manevitz method and our “fuzzy” method are close approximations of each other. Two examples illustrate how fuzzy independence and extended conditional probability might be applied. The first shows how linguistic probabilities result from treating fuzzy independence as a linguistic variable. The second is an industrial example of troubleshooting on the shop floor.     

Revising ranked probabilities: a Bayesian approach to incomplete knowledge

Incomplete knowledge refers to situations in which a decision maker can rank the probabilities of occurrence for events of interest, but cannot specify these probabilities exactly. In this paper a Monte Carlo approach is used to investigate how physicians can arrive at revised or posterior rankings of disease probability given only rank order information about both the patient's prior probabilities of disease and the conditional probabilities of specific clinical findings for each disease. Computer-generated estimates of the expected frequencies of the posterior rankings are presented for the cases of 2, 3, and 4 disease states. The application of probability revision under conditions of incomplete knowledge to therapeutic decision making is discussed.     

基于分类融合和关联规则挖掘的图像语义标注

图像语义自动标注问题是现阶段一个具有挑战性的难题。在跨媒体相关模型基础上,提出了融合图像类别信息的图像语义标注新方法,并利用关联规则挖掘算法改善标注结果。首先对图像进行低层特征提取,用“视觉词袋”描述图像;然后对图像特征分别进行K-means聚类和基于支持向量机的多类别分类,得到图像相似性关系和类别信息;计算语义标签和图像之间的概率关系,并将图像类别信息作为权重融合到标签的统计概率中,得到候选标注词集;最后以候选标注词概率为依据,利用改善的关联规则挖掘算法挖掘文本关联度,并对候选标注词集进行等频离散化处理,从而得到最终标注结果。在图像集Corel上进行的标注实验取得了较为理想的标注结果。     

面向汉维机器翻译的双语关联度优化模型

针对汉语—维吾尔语的统计机器翻译系统中存在的语义无关性问题,提出基于神经网络机器翻译方法的双语关联度优化模型。该模型利用注意力机制捕获词对齐信息,引入双语短语间的语义相关性和内部词汇匹配度,预测双语短语的生成概率并将其作为双语关联度,以优化统计翻译模型中的短语翻译得分。在第十一届全国机器翻译研讨会（CWMT 2015）汉维公开机器翻译数据集上的实验结果表明,与基线系统相比,在使用较小规模的训练数据和词汇表的条件下,所提方法可以有效地同时提高短语级别和句子级别的机器翻译任务性能,分别获得最高2.49和0.59的BLEU值提升。     

Protecting research and technology from espionage

In recent years, governmental and industrial espionage becomes an increased problem for governments and corporations. Especially information about current technology development and research activities are interesting targets for espionage. Thus, we introduce a new and automated methodology that investigates the information leakage risk of projects in research and technology (R&T) processed by an organization concerning governmental or industrial espionage. Latent semantic indexing is applied together with machine based learning and prediction modeling. This identifies semantic textual patterns representing technologies and their corresponding application fields that are of high relevance for the organization’s strategy. These patterns are used to estimate organization’s costs of an information leakage for each project. Further, a web mining approach is processed to identify worldwide knowledge distribution within the relevant technologies and corresponding application fields. This information is used to estimate the probability that an information leakage occur. A risk assessment methodology calculates the information leakage risk for each project. In a case study, the information leakage risk of defense based R&T projects is investigated. This is because defense based R&T is of particularly interest by espionage agents. Overall, it can be shown that the proposed methodology is successful in calculation the espionage information leakage risk of projects. This supports an organization by processing espionage risk management.     

基于可达概率区间的不确定决策树

针对不确定数据的概率分布难以获取的客观实际,讨论了缺失概率分布的值不确定离散对象的决策树。定义了(条件)概率区间,并证明了(条件)概率区间是可达概率区间;基于可达概率区间,定义了(条件)熵区间,并给出了求解(条件)熵区间的上/下界的方法;采用条件熵区间作为属性选择度量,提出了一种新的不确定决策树,将以0-1划分对象的决策树扩展到以概率区间分配对象的决策树,这样不仅可以处理缺失概率分布的值不确定离散对象,也可以处理确定离散对象。通过在基于UCI数据集的不确定数据集上的实验,证实了不确定决策树是有效的。