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.     

Theory of search engines

Four different stochastic matrices, useful for ranking the pages of the web are defined. The theory is illustrated with examples.     

基于PageRank算法的搜索引擎优化策略

在介绍Google等搜索引擎最常用的PageRank搜索结果排名算法的基础上，详细阐述了各种网页链接结构对基于PageRank算法的网站搜索引擎排名结果可能产生的影响，并分析了实际应用中网站针对PageRank算法的各种优化策略，讨论了各自的优点。     

Improving the labeling accuracy by a new probabilistic relaxation labeling

The updating rule of probabilistic relaxation labeling (PRL) is analyzed. A new updating rule is derived by replacing a simplifying assumption used in the derivation of the conventional updating rule with a more relevant one. The PRL with the new updating rule does not show the labeling degradation phase which appears in the conventional PRL.     

基于增量式FHCAM的入侵检测方法

面对网络环境中的各类攻击,急需研究良好的入侵检测方法,使其对于各种网络攻击行为都有较高的检全率和检准率,并具备自动识别新的异常行为的能力.通过对基于数据挖掘的入侵检测技术进行研究,结合网络数据源的特征,提出了一种面向混合类型数据的、基于增量式快速启发聚类算法(增量式FHCAM)的入侵检测方法,可以实现无指导学习,明显提高聚类速度,能快速识别正常行为和异常行为,并具备自动识别新的异常行为的能力.     

An evolutionary approach for combining different sources of evidence in search engines

Modern Web search engines use different strategies to improve the overall quality of their document rankings. Usually the strategy adopted involves the combination of multiple sources of relevance into a single ranking. This work proposes the use of evolutionary techniques to derive good evidence combination functions using three different sources of evidence of relevance: the textual content of documents, the reputation of documents extracted from the connectivity information available in the processed collection and the anchor text concatenation. The combination functions discovered by our evolutionary strategies were tested using a collection containing 368 queries extracted from a real nation-wide search engine query log with over 12 million documents. The experiments performed indicate that our proposal is an effective and practical alternative for combining sources of evidence into a single ranking. We also show that different types of queries submitted to a search engine can require different combination functions and that our proposal is useful for coping with such differences.     

User reactions to search engines logos: investigating brand knowledge of web search engines

In this work, we investigate consumer reaction to web search engine logos. Our research is motivated by a small number of search engines dominating a market in which there are little switching costs. The major research goal is to investigate the effect that brand logos have on search engine brand knowledge, which includes brand image and brand awareness. To investigate this goal, we employ a survey of 207 participants and use a mixed method approach of sentiment analysis and mutual information statistic to investigate our research questions. Our findings reveal that some search engines have logos that do not communicate a clear meaning, resulting in a confused brand message. Brand image varies among the top search engines, with consumers possessing generally extremely positive or negative brand opinions. Google elicited a string of positive comments from the participants, to the point of several uses of the term ‘love.’ This is in line with the ultimate brand equity that Google has achieved (i.e., the generic term for web search). Most of the other search engines, including Microsoft, had primarily negative terms associated with them, although AOL, Ask, and Yahoo! had a mix of both positive and negative comments. Implications are that the brand logo may be an important interplay component with the technology for both established search engines and those entering the market.     

Analyzing the emotional outcomes of the online search behavior with search engines

The affective component has been acknowledged as critical to understand information search behavior and user-computer interactions. There is a lack of studies that analyze the emotions that the user feels when searching for information about products with search engines. The present study analyzes the emotional outcomes of the online search process, taking into account the user’s (a) perceptions of success and effort exerted on the search process, (b) initial affective state, and (c) emotions felt during the search process. In addition, we identify profiles of online searchers based on the emotional outcomes of the search process, which allow us to differentiate the emotional processes and behavioral patterns that lead to such emotions. The results of the study stress the importance of the affective component of the online search behavior, given that these emotional outcomes are likely to influence all the subsequent actions that users perform on the Web.     

Aggregation of web search engines based on users’ preferences in WebFusion

The required information of users is distributed in the databases of various search engines. It is inconvenient and inefficient for an ordinary user to invoke multiple search engines and identify useful documents from the returned results. Meta-search engines could provide a unified access for their users. In this paper, a novel meta-search engine, named as WebFusion, is introduced. WebFusion learns the expertness of the underlying search engines in a certain category based on the users’ preferences. It also uses the “click-through data concept” to give a content-oriented ranking score to each result page. Click-through data concept is the implicit feedback of the users’ preferences, which is also used as a reinforcement signal in the learning process, to predict the users’ preferences and reduces the seeking time in the returned results list. The decision lists of underling search engines have been fused using ordered weighted averaging (OWA) approach and the application of optimistic operator as weightening function has been investigated. Moreover, the results of this approach have been compared with those achieve by some popular meta-search engines such as ProFusion and MetaCrawler. Experimental results demonstrate a significant improvement on average click rate, and the variance of clicks as well as average relevancy criterion.     

An incremental training method for the probabilistic RBF network

The probabilistic radial basis function (PRBF) network constitutes a probabilistic version of the RBF network for classification that extends the typical mixture model approach to classification by allowing the sharing of mixture components among all classes. The typical learning method of PRBF for a classification task employs the expectation-maximization (EM) algorithm and depends strongly on the initial parameter values. In this paper, we propose a technique for incremental training of the PRBF network for classification. The proposed algorithm starts with a single component and incrementally adds more components at appropriate positions in the data space. The addition of a new component is based on criteria for detecting a region in the data space that is crucial for the classification task. After the addition of all components, the algorithm splits every component of the network into subcomponents, each one corresponding to a different class. Experimental results using several well-known classification data sets indicate that the incremental method provides solutions of superior classification performance compared to the hierarchical PRBF training method. We also conducted comparative experiments with the support vector machines method and present the obtained results along with a qualitative comparison of the two approaches.     

一种基于概率的超声波模型的设计

超声波检测迅速、方便、计算简单、易于做到实时控制,且其测量精度能达到工业实用的要求。在超声波硬件电路基础上,重点对声纳的测量特性及其信息不确定性进行了分析,并提出了一种新的超声波传感器模型。该模型在均匀分布超声波模型的基础上,利用概率方法能准确描述声纳扇形范围之内的障碍物位置,通过仿真验证了该模型的有效性和实用性。     

An analysis of web proxy logs with query distribution pattern approach for search engines

This study presents an analysis of users' queries directed at different search engines to investigate trends and suggest better search engine capabilities. The query distribution among search engines that includes spawning of queries, number of terms per query and query lengths is discussed to highlight the principal factors affecting a user's choice of search engines and evaluate the reasons of varying the length of queries. The results could be used to develop long to short term business plans for search engine service providers to determine whether or not to opt for more focused topic specific search offerings to gain better market share.     

Defining the Web: the politics of search engines

Although the Web itself might truthfully claim a sovereign disinterested and unbiased attitude toward the people who use it, the authors claim that search engines, the tools that navigate the astronomical number of pages (800 million and counting), favor popular, wealthy, and powerful sites at the expense of others. Some researchers have estimated that, taken individually, none of the Web search engines studied indexes more than 16 percent of the total indexable Web. Combined, the results from all search engines they studied covered only about 42 percent of the Web. But what about those portions of the Web that remain hidden from view? The article looks at how search engine developers, designers, and producers grapple with the technical limits that restrict what their engines can find. The authors also examine influences that may determine systematic inclusion and exclusion of certain sites, and the wide-ranging factors that dictate systematic prominence for some sites while relegating others to systematic invisibility     

Improving the efficiency of a hyperlinking-based theorem prover by incremental evaluation with network structures

Recently Lee and Plaisted proposed a theorem-proving method, the hyperlinking proof procedure, to eliminate duplication of instances of clauses during the process of inference. A theorem prover, CLIN, which implements the procedure was also constructed. In this implementation, redundant work on literal unification checking, partial unification checking, and duplicate instance checking is performed repetitively, resulting in a large overhead when many rounds of hyperlinking are needed for an input problem. We propose a technique that maintains information across rounds in shared network structures, so that the redundant work in each hyperlinking round can be avoided. Empirical performance comparison has been done between CLIN and CLIN-net, which is the theorem prover with shared network structures, and some results are shown. Problems related to memory overhead and literal ordering are discussed.Supported by National Science Council under grants NSC 81-0408-E-110-509 and NSC-82-0408-E-110-045. A preliminary version of this paper appeared in Proceedings of International Conference on Computing and Information (Sudbury, Ontario Canada, May 1993).     

Market penetration - a probabilistic approach

The focus of this paper is the development of a probabilistic steady-state (long-term) characterization of the market allocation process under stochastic variation in prices of delivered products. The generic method has been developed with a view toward incorporation of the methodology within large-scale energy models.     

主题搜索引擎中爬虫搜索策略的研究

为了解决传统主题爬虫效率偏低的问题,传统主题爬虫会选择最有价值的链接进行访问,仅简单地计算链接的相关性,却忽视待分析URL之间的相关性关系,致使主题爬虫爬取效率较低。提出一种基于链接模型的相关性判别算法,综合利用有标种子URL和无标的待判别URL实现对无标URL的相关性判别,并推导出迭代初值选取对结果的不敏感性。实验结果表明,与传统的网络爬虫算法相关性判别方法相比,提出的方法效率更高。     

A probabilistic approach to pronunciation by analogy

The relationship between written and spoken words is convoluted in languages with a deep orthography such as English and therefore it is difficult to devise explicit rules for generating the pronunciations for unseen words. Pronunciation by analogy (PbA) is a data-driven method of constructing pronunciations for novel words from concatenated segments of known words and their pronunciations. PbA performs relatively well with English and outperforms several other proposed methods. However, the method inherently generates several candidate pronunciations and its performance depends critically on a good scoring function to choose the best one of them.Previous PbA algorithms have used several different scoring heuristics such as the product of the frequencies of the component pronunciations of the segments, or the number of different segmentations that yield the same pronunciation, and different combinations of these methods, to evaluate the candidate pronunciations. In this article, we instead propose to use a probabilistically justified scoring rule. We show that this principled approach alone yields better accuracy than any previously published PbA algorithm. Furthermore, combined with certain ad hoc modifications motivated by earlier algorithms, the performance can in some cases be further increased.     

Attitudes toward search engines as a learning assisted tool: approach of Liaw and Huang’s research model

The purpose of this research is to develop new approach to investigate individual attitudes toward search engines as a learning assisted tool. This research is based on Liaw and Huang’s research model that included motivation perspective, social cognitive theory, and technology acceptance model. After factor analysis and regression analysis, the results show that the factor of perceived satisfaction of system’s quality and the factor of experience using the Internet and search engines will influence the factor of perceived enjoyment and the factor of self-efficacy. In addition, perceived enjoyment and self-efficacy can predict of the factor of individual intention to use search engines as a learning assisted tool. Furthermore, based on [Liaw and Huang’s model (2003). An investigation of user attitudes toward search engines as an information retrieval tool. Computers in Human Behavior, 19(6) 751–765] and the results of present study, we propose a new approach – three-Tier Use Model (3-TUM), the tier of individual experience and system quality can influence the affective and cognitive tier, and the affective and cognitive tier has positive effects on the behavioral intention tier.