一种基于用户标记的搜索结果排序算法

随着计算机网络的快速发展,网络上的信息量也日益纷繁复杂.如何准确、快速地帮助人们从海量网络数据中获取所需信息,这是目前搜索引擎首要解决的问题,为此,各种搜索排序算法应运而生.但是目前,网页信息的表达形式都十分简单,用户描述查询的形式更是十分简单,这就造成了在判断网页内容与用户查询相关性时十分困难.首先对现有的搜索引擎排序算法进行了分类总结,分析它们的优缺点.然后提出了一种基于用户反馈的语义标记的新方法,最后采用多种评估方法与Google搜索结果进行对比分析.实验结果表明,利用该方法所得到的排序结果比Google的排序结果更接近用户需求.     

关系数据库中支持语义的Top-K关键字搜索

为了增强关系数据库中的关键字搜索查询结果,考虑了多表之间以及元组之间的语义关系,提出了一种语义评分函数.该语义评分函数不仅涵盖了当前的评分思想,并且加入新指标来衡量查询结果与查询关键字之间的相关性.基于该评分函数,提出两种以数据块为处理单位的Top-K搜索算法,分别为BA(blocking algorithm)算法和EBA(early-stopping blocking algorithm)算法.EBA在BA基础上引入了过滤域值,以便尽早终止算法的迭代次数.最后实验结果显示语义评分函数保证了     

语义网站点的发现与排序

随着语义网中RDF数据的大量涌现,语义搜索引擎为用户搜索RDF数据带来了便利.但是,如何自动地发现包含语义网信息资源的站点,并高效地在语义网站点中收集语义网信息资源,一直是语义搜索引擎所面临的问题.首先介绍了语义网站点的链接模型.该模型刻画了语义网站点、语义网信息资源、RDF模型和语义网实体之间的关系.基于该模型讨论了语义网实体的归属问题,并进一步定义了语义网站点的发现规则;另外,从站点链接模型出发,定义了语义网站点依赖图,并给出了对语义网站点进行排序的算法.将相关算法在一个真实的语义搜索引擎中进行了初步测试.实验结果表明,所提出的方法可以有效地发现语义网站点并对站点进行排序.     

关系数据库中支持语义的Top-K关键字搜索

为了增强关系数据库中的关键字搜索查询结果,考虑了多表之间以及元组之间的语义关系,提出了一种语义评分函数.该语义评分函数不仅涵盖了当前的评分思想,并且加入新指标来衡量查询结果与查询关键字之间的相关性.基于该评分函数,提出两种以数据块为处理单位的Top-K搜索算法,分别为BA(blocking algorithm)算法和EBA(early-stopping blocking algorithm)算法.EBA在BA基础上引入了过滤域值,以便尽早终止算法的迭代次数.最后实验结果显示语义评分函数保证了搜索结果的高查准率和查全率,所提出的BA算法和EBA算法改善了现有方法的查询性能.     

Improving large-scale search engines with semantic annotations

Traditional search engines have become the most useful tools to search the World Wide Web. Even though they are good for certain search tasks, they may be less effective for others, such as satisfying ambiguous or synonym queries. In this paper, we propose an algorithm that, with the help of Wikipedia and collaborative semantic annotations, improves the quality of web search engines in the ranking of returned results. Our work is supported by (1) the logs generated after query searching, (2) semantic annotations of queries and (3) semantic annotations of web pages. The algorithm makes use of this information to elaborate an appropriate ranking. To validate our approach we have implemented a system that can apply the algorithm to a particular search engine. Evaluation results show that the number of relevant web resources obtained after executing a query with the algorithm is higher than the one obtained without it.     

Web2．0社区搜索模块排序算法的设计与实现

网页排序技术是搜索引擎的核心技术之一。描述Web2.0社区构建语义搜索的必要性,分析影响网页排序的因素,将搜索引擎的排序算法借鉴到基于Web2.0社区的搜索模块中,以改进的TF/IDF和PageRank算法为基础,在一个Web2.0开源社区开发平台上实现基于语义排序的搜索模块。测试结果表明,该排序算法具有内容定位精确、有效结果靠前的特点。     

EFFICIENT METHODOLOGIES TO HANDLE HANGING PAGES USING VIRTUAL NODE

In this article we first explain the knowledge extraction (KE) process from the World Wide Web (WWW) using search engines. Then we explore the PageRank algorithm of Google search engine (a well-known link-based search engine) with its hidden Markov analysis. We also explore one of the problems of link-based ranking algorithms called hanging pages or dangling pages (pages without any forward links). The presence of these pages affects the ranking of Web pages. Some of the hanging pages may contain important information that cannot be neglected by the search engine during ranking. We propose methodologies to handle the hanging pages and compare the methodologies. We also introduce the TrustRank algorithm (an algorithm to handle the spamming problems in link-based search engines) and include it in our proposed methods so that our methods can combat Web spam. We implemented the PageRank algorithm and TrustRank algorithm and modified those algorithms to implement our proposed methodologies.     

Improved artificial bee colony algorithm for global optimization

The artificial bee colony algorithm is a relatively new optimization technique. This paper presents an improved artificial bee colony (IABC) algorithm for global optimization. Inspired by differential evolution (DE) and introducing a parameter M, we propose two improved solution search equations, namely “ABC/best/1” and “ABC/rand/1”. Then, in order to take advantage of them and avoid the shortages of them, we use a selective probability p to control the frequency of introducing “ABC/rand/1” and “ABC/best/1” and get a new search mechanism. In addition, to enhance the global convergence speed, when producing the initial population, both the chaotic systems and the opposition-based learning method are employed. Experiments are conducted on a suite of unimodal/multimodal benchmark functions. The results demonstrate the good performance of the IABC algorithm in solving complex numerical optimization problems when compared with thirteen recent algorithms.     

WSB: a broker‐centric framework for quality‐driven web service discovery

Web service interfaces can be discovered through several means, including service registries, search engines, service portals, and peer‐to‐peer networks. But discovering Web services in such heterogeneous environments is becoming a challenging task and raises several concerns, such as performance, reliability, and robustness. In this paper, we introduce the Web Service Broker (WSB) framework that provides a universal access point for discovering Web services. WSB uses a crawler to collect the plurality of Web services disseminated throughout the Web, continuously monitor the behavior of Web services in delivering the expected functionality, and enable clients to articulate service queries tailored to their needs. The framework features ranking algorithms we have developed which are capable of ranking services according to Quality of Web Service parameters. WSB can be seamlessly integrated into the existing service‐oriented architectures. Copyright © 2010 John Wiley & Sons, Ltd.     

Genetic algorithms to solve the cover printing problem

Inspired by successful application of evolutionary algorithms to solving difficult optimization problems, we explore in this paper, the applicability of genetic algorithms (GAs) to the cover printing problem, which consists in the grouping of book covers on offset plates in order to minimize the total production cost. We combine GAs with a linear programming solver and we propose some innovative features such as the “unfixed two-point crossover operator” and the “binary stochastic sampling with replacement” for selection. Two approaches are proposed: an adapted genetic algorithm and a multiobjective genetic algorithm using the Pareto fitness genetic algorithm. The resulting solutions are compared. Some computational experiments have also been done to analyze the effects of different genetic operators on both algorithms.     

Comparison of simple diversity mechanisms on plateau functions

It is widely assumed and observed in experiments that the use of diversity mechanisms in evolutionary algorithms may have a great impact on its running time. Up to now there is no rigorous analysis pointing out how different diversity mechanisms influence the runtime behavior. We consider evolutionary algorithms that differ from each other in the way they ensure diversity and point out situations where the right mechanism is crucial for the success of the algorithm. The considered evolutionary algorithms either diversify the population with respect to the search points or with respect to function values. Investigating simple plateau functions, we show that using the “right” diversity strategy makes the difference between an exponential and a polynomial runtime. Later on, we examine how the drawback of the “wrong” diversity mechanism can be compensated by increasing the population size.     

Webspam demotion: Low complexity node aggregation methods

Search engines result pages (SERPs) for a specific query are constructed according to several mechanisms. One of them consists in ranking Web pages regarding their importance, regardless of their semantic. Indeed, relevance to a query is not enough to provide high quality results, and popularity is used to arbitrate between equally relevant Web pages. The most well-known algorithm that ranks Web pages according to their popularity is the PageRank.The term Webspam was coined to denotes Web pages created with the only purpose of fooling ranking algorithms such as the PageRank. Indeed, the goal of Webspam is to promote a target page by increasing its rank. It is an important issue for Web search engines to spot and discard Webspam to provide their users with a nonbiased list of results. Webspam techniques are evolving constantly to remain efficient but most of the time they still consist in creating a specific linking architecture around the target page to increase its rank.In this paper we propose to study the effects of node aggregation on the well-known ranking algorithm of Google (the PageRank) in the presence of Webspam. Our node aggregation methods have the purpose to construct clusters of nodes that are considered as a sole node in the PageRank computation. Since the Web graph is way to big to apply classic clustering techniques, we present four lightweight aggregation techniques suitable for its size. Experimental results on the WEBSPAM-UK2007 dataset show the interest of the approach, which is moreover confirmed by statistical evidence.     

A convex parametrization of risk-adjusted stabilizing controllers

The focal point of this paper is the synthesis of controllers under risk-specifications. In recent years there has been a growing interest in the development of techniques for controller design where, instead of requiring that the performance specifications are met for every possible value of admissible uncertainty, it is required that the risk of performance violation is below a small well-defined risk level. In contrast to previous work, where the search for the controller gains is done using randomized algorithms, the results in this paper show that for a class of uncertain linear time invariant systems, the search for the “risk-adjusted” controller can be done efficiently using deterministic algorithms. More precisely, for the case when the characteristic polynomial of the closed loop system depends affinely on the uncertainty, we provide a convex parametrization of “risk-adjusted” stabilizing controllers.     

Exploiting the Small-Worlds of the Semantic Web to Connect Heterogeneous,Local Ontologies

In this paper, a parallel is drawn between the semantic Web search problem of software agents finding the right ontology definition to how people are able to find strangers using a surprisingly short chain of acquaintances – a result from the six degrees of separation experiment. The experiment relied on shared understanding of the phrase, someone you know on a first name basis to define an acquaintance relationship. Web searching relies on standardized use of the hyperlink relationship. Hyperlinks are constituted from universally accepted meta-data: Anchor and bookmark HTML markups. Say that heterogeneous local ontologies are all marked-up using standard meta-data. Then, the meta-data and some universally accepted semantics constitute a shared ontology, which can be used to bridge local ontologies, much as highly connected people who belonged to many cliques (small-worlds) were used disproportionately often in the search for strangers. This paper outlines the framework for approaching the semantic Web search problem using meta-data based shared ontologies inspired from small-worlds theory of sociology. This approach is exciting because it (1) enables data sharing over the semantic Web without post hoc modifications to local ontologies, and (2) uses meta-data, which in many situations are already commonly available and implemented in XML.     

Vanishing point detection using cascaded 1D Hough Transform from single images

Vanishing point detection algorithms based on 2D histogramming techniques have been employed in a variety of computer vision systems. Previous algorithms achieved some good results but still failed to maintain a balanced performance in both accuracy and time. Recent research (Li et al., 2010) shows that, vanishing point detection could be converted to a 1D histogram search problem, which largely accelerates the procedure. In this paper, we further improve this idea and propose a complete scheme for vanishing point detection from images of the so called “Manhattan world”. We test our algorithm and some commonly used vanishing point detection methods on public database YorkUrbanDB and our own implemented database PKUCampusDB. Our algorithm shows significant performance improvements.     

Visual search reranking via adaptive particle swarm optimization

Visual search reranking involves an optimization process that uses visual content to recover the “genuine” ranking list from the helpful but noisy one generated by textual search. This paper presents an evolutionary approach, called Adaptive Particle Swarm Optimization (APSO), for unsupervised visual search reranking. The proposed approach incorporates the visual consistency regularization and the ranking list distance. In addition, to address the problem that existing list distance fails to capture the genuine disagreement between two ranking lists, we propose a numerical ranking list distance. Furthermore, the parameters in APSO are self-tuned adaptively according to the fitness values of the particles to avoid being trapped in local optima. We conduct extensive experiments on automatic search task over TRECVID 2006-2007 benchmarks and show significant and consistent improvements over state-of-the-art works.     

一种基于网页质量的PageRank算法改进

随着Web技术的发展和Web上越来越多的各种信息,如何提供高质量、相关的查询结果成为当前Web搜索引擎的一个巨大挑战.PageRank和HITS是两个最重要的基于链接的排序算法并在商业搜索引擎中使用.然而,在PageRank算法中,每个网页的PR值被平均地分配到它所指向的所有网页,网页之间的质量差异被完全忽略.这样的算法很容易被当前的Web SPAM攻击.基于这样的认识,提出了一个关于PageRank算法的改进,称为Page Quality Based PageRank(QPR)算法.QPR算法动态地评估每个网页的质量,并根据网页的质量对每个网页的PR值做相应公平的分配.在多个不同特性的数据集上进行了全面的实验,实验结果显示,提出的QPR算法能大大提高查询结果的排序,并能有效减轻SPAM网页对查询结果的影响.     

A simulated annealing for multi-criteria network path problems

This paper investigates an oriented spanning tree (OST) based simulated annealing (SA) for solving the multi-criteria shortest path problem (MSPP) as well as the multi-criteria constrained shortest path problem (MCSPP), especially for those with nonlinear objectives. As a popular search algorithm, because of “search-from-a-point” searching mechanism, there have been only a few attempts in extending SA to multi-criteria optimization, particularly, for various MSPPs. In contrast with the existing evolutionary algorithms (EAs), by representing a path as an OST, the designed SA provides an entirely new searching mechanism in sense of “search from a paths set to another paths set” such that both of its local and global search capabilities are greatly improved. Because the possibility of existing a feasible path in a paths set is usually larger than that of one path being feasible, the designed SA has much predominance for solving MCSPPs. Some computational comparisons are discussed and the test results are compared with those obtained by a recent EA of which the representing approach and the ideas of evolution operators such as mutation and crossover are adopted in most of the existing EAs for the shortest path problems. The test results indicate that the new algorithm is available for both of MSPPs and MCSPPs.     

Inclusive social tagging and its support in Web 2.0 services

This paper investigates the Web 2.0 phenomenon of social tagging in the context of existing approaches to semantic data structuring. Social tagging is embedded into the space spanned by current structuring approaches like taxonomies, meta-data, and ontologies in order to identify its semantic and pragmatic foundations. Thereby, we use the Inclusive Universal Access paradigm to assess social tagging with respect to socio-technical criteria for inclusive and barrier-free provision and usage of web services. As a result of this analysis we propose a concept we chose to call “Inclusive Social Tagging”. We subsequently use the requirements set forth by this concept to assess the tagging functionality of currently popular Web 2.0 services. We found that these services differ significantly in their implementation of tagging functionality, and we did not discover any service providing full compliance with Inclusive Social Tagging requirements.