Ontology mapping approach using web search engine

A new mapping approach for automated ontology mapping using web search engines （such as Google） is presented. Based on lexico-syntactic patterns, the hyponymy relationships between ontology concepts can be obtained from the web by search engines and an initial candidate mapping set consisting of ontology concept pairs is generated. According to the concept hierarchies of ontologies, a set of production rules is proposed to delete the concept pairs inconsistent with the ontology semantics from the initial candidate mapping set and add the concept pairs consistent with the ontology semantics to it. Finally, ontology mappings are chosen from the candidate mapping set automatically with a mapping select rule which is based on mutual information. Experimental results show that the F-measure can reach 75% to 100% and it can effectively accomplish the mapping between ontologies.

Weighted PageRank Algorithm Search Engine Ranking Model for Web Pages

As data grows in size,search engines face new challenges in extracting more relevant content for users’searches.As a result,a number of retrieval and ranking algorithms have been employed to ensure that the results are relevant to the user’s requirements.Unfortunately,most existing indexes and ranking algo-rithms crawl documents and web pages based on a limited set of criteria designed to meet user expectations,making it impossible to deliver exceptionally accurate results.As a result,this study investigates and analyses how search engines work,as well as the elements that contribute to higher ranks.This paper addresses the issue of bias by proposing a new ranking algorithm based on the PageRank(PR)algorithm,which is one of the most widely used page ranking algorithms We pro-pose weighted PageRank(WPR)algorithms to test the relationship between these various measures.The Weighted Page Rank(WPR)model was used in three dis-tinct trials to compare the rankings of documents and pages based on one or more user preferences criteria.Thefindings of utilizing the Weighted Page Rank model showed that using multiple criteria to rankfinal pages is better than using only one,and that some criteria had a greater impact on ranking results than others.

Design and implementation of semantic search engine Smartch

To integrate reasoning and text retrieval, the architecture of a semantic search engine which includes several kinds of queries is proposed, and the semantic search engine Smartch is designed and implemented. Based on a logical reasoning process and a graphic user-defined process, Smartch provides four kinds of search services. They are basic search, concept search, graphic user-defined query and association relationship search. The experimental results show that compared with the traditional search engine, the recall and precision of Smartch are improved. Graphic user-defined queries can accurately locate the information of user needs. Association relationship search can find complicated relationships between concepts. Smartch can perform some intelligent functions based on ontology inference.

Application of a Web Search Engine in Translating Business Terms in English-Chinese Dictionaries

Web search engines are important tools for lexicography.This paper takes translation of business terms("e-commerce"and"e-business")as an example to illustrate the application of web search engines in English-Chinese dictionary translation,including the methods of(1)finding the potential Chinese equivalents of the English business terms,and(2)selecting typical and proper Chinese equivalents in accordance with the frequencies and the meanings of the English business terms respectively.

Website Search Engine Optimization: Geographical and Cultural Point of View

The concept of Webpage visibility is usually linked to search engine optimization (SEO), and it is based on global in-link metric [1]. SEO is the process of designing Webpages to optimize its potential to rank high on search engines, preferably on the first page of the results page. The purpose of this research study is to analyze the influence of local geographical area, in terms of cultural values, and the effect of local society keywords in increasing Website visibility. Websites were analyzed by accessing the source code of their homepages through Google Chrome browser. Statistical analysis methods were selected to assess and analyze the results of the SEO and search engine visibility (SEV). The results obtained suggest that the development of Web indicators to be included should consider a local idea of visibility, and consider a certain geographical context. The geographical region that the researchers are considering in this research is the Hashemite kingdom of Jordan (HKJ). The results obtained also suggest that the use of social culture keywords leads to increase the Website visibility in search engines as well as localizes the search area such as google.jo, which localizes the search for HKJ.

Current Application of Search Engines and Their Developing Trend

FAN Xiao-wen School of Pharmacy, Shenyang Pharmaceutical University, Shenyang,110016The basic types of current search engines which can help users to perfume laborious information-gathering tasks on Internet is proposed. Basically, the search engines can be classified into index engine, directory engine and agent engine on WWW information service. The key technologies of web mine, automatic classifying of documents and ordering regulation of feedback information are discussed. Finally, the developing trend of search engines is pointed out by analyzing their practical application on World Wide Web.

Web search engine:characteristics of user behaviors and their implication

In this paper, first studied are the distribution characteristics of user behaviors based on log data from a massive web search engine. Analysis shows that stochastic distribution of user queries accords with the characteristics of power-law function and exhibits strong similarity, and the user' s queries and clicked URLs present dramatic locality, which implies that query cache and 'hot click' cache can be employed to improve system performance. Then three typical cache replacement policies are compared, including LRU, FIFO, and LFU with attenuation. In addition, the distribution character-istics of web information are also analyzed, which demonstrates that the link popularity and replica pop-ularity of a URL have positive influence on its importance. Finally, variance between the link popularity and user popularity, and variance between replica popularity and user popularity are analyzed, which give us some important insight that helps us improve the ranking algorithms in a search engine.

A geospatial search engine for discovering multi-format geospatial data across the web

The volume of publically available geospatial data on the web is rapidly increasing due to advances in server-based technologies and the ease at which data can now be created.However,challenges remain with connecting individuals searching for geospatial data with servers and websites where such data exist.The objective of this paper is to present a publically available Geospatial Search Engine(GSE)that utilizes a web crawler built on top of the Google search engine in order to search the web for geospatial data.The crawler seeding mechanism combines search terms entered by users with predefined keywords that identify geospatial data services.A procedure runs daily to update map server layers and metadata,and to eliminate servers that go offline.The GSE supports Web Map Services,ArcGIS services,and websites that have geospatial data for download.We applied the GSE to search for all available geospatial services under these formats and provide search results including the spatial distribution of all obtained services.While enhancements to our GSE and to web crawler technology in general lie ahead,our work represents an important step toward realizing the potential of a publically accessible tool for discovering the global availability of geospatial data.

Intelligent search on integrated knowledge base of traditional Chinese medicine

To semantically integrate heterogeneous resources and provide a unified intelligent access interface, semantic web technology is exploited to publish and interlink machineunderstandable resources so that intelligent search can be supported. TCMSearch, a deployed intelligent search engine for traditional Chinese medicine （TCM）, is presented. The core of the system is an integrated knowledge base that uses a TCM domain ontology to represent the instances and relationships in TCM. Machine-learning techniques are used to generate semantic annotations for texts and semantic mappings for relational databases, and then a semantic index is constructed for these resources. The major benefit of representing the semantic index in RDF/OWL is to support some powerful reasoning functions, such as class hierarchies and relation inferences. By combining resource integration with reasoning, the knowledge base can support some intelligent search paradigms besides keyword search, such as correlated search, semantic graph navigation and concept recommendation.

The Study on China’s Flu Prediction Model Based on Web Search Data

Influenza is a kind of infectious disease, which spreads quickly and widely. The outbreak of influenza has brought huge losses to society. In this paper, four major categories of flu keywords, “prevention phase”, “symptom phase”, “treatment phase”, and “commonly-used phrase” were set. Python web crawler was used to obtain relevant influenza data from the National Influenza Center’s influenza surveillance weekly report and Baidu Index. The establishment of support vector regression (SVR), least absolute shrinkage and selection operator (LASSO), convolutional neural networks (CNN) prediction models through machine learning, took into account the seasonal characteristics of the influenza, also established the time series model (ARMA). The results show that, it is feasible to predict influenza based on web search data. Machine learning shows a certain forecast effect in the prediction of influenza based on web search data. In the future, it will have certain reference value in influenza prediction. The ARMA(3,0) model predicts better results and has greater generalization. Finally, the lack of research in this paper and future research directions are given.

Towards More Efficient Image Web Search

With the flood of information on the Web, it has become increasingly necessary for users to utilize automated tools in order to find, extract, filter, and evaluate the desired information and knowledge discovery. In this research, we will present a preliminary discussion about using the dominant meaning technique to improve Google Image Web search engine. Google search engine analyzes the text on the page adjacent to the image, the image caption and dozens of other factors to determine the image content. To improve the results, we looked for building a dominant meaning classification model. This paper investigated the influence of using this model to retrieve more efficient images, through sequential procedures to formulate a suitable query. In order to build this model, the specific dataset related to an application domain was collected;K-means algorithm was used to cluster the dataset into K-clusters, and the dominant meaning technique is used to construct a hierarchy model of these clusters. This hierarchy model is used to reformulate a new query. We perform some experiments on Google and validate the effectiveness of the proposed approach. The proposed approach is improved for in precision, recall and F1-measure by 57%, 70%, and 61% respectively.

基于图匹配的Web实体抽取算法研究

现今Web中存在大量缺失、不一致及不精确的数据,而传统的搜索引擎只能根据关键词返回文档片段,无法直接获取目标实体。提出一种新的基于图匹配的实体抽取算法GMEE(Graph Matching Based Entity Extraction),首先将片段按词语分割,进行实体的初步筛选;然后根据各实体之间的结构和语义关系建立“加权语义实体关联图”;最后利用“最大公共子图匹配”策略抽取目标实体。实验结果表明,提出的算法在不需要大量参数训练及传递的情况下,能够对抽取的实体集进行有效的精简,既保证了召回率、准确率,又提高了抽取过程的可解释性。

Automatic Clustering of User Behaviour Profiles for Web Recommendation System

Web usage mining,content mining,and structure mining comprise the web mining process.Web-Page Recommendation(WPR)development by incor-porating Data Mining Techniques(DMT)did not include end-users with improved performance in the obtainedfiltering results.The cluster user profile-based clustering process is delayed when it has a low precision rate.Markov Chain Monte Carlo-Dynamic Clustering(MC2-DC)is based on the User Behavior Profile(UBP)model group’s similar user behavior on a dynamic update of UBP.The Reversible-Jump Concept(RJC)reviews the history with updated UBP and moves to appropriate clusters.Hamilton’s Filtering Framework(HFF)is designed tofilter user data based on personalised information on automatically updated UBP through the Search Engine(SE).The Hamilton Filtered Regime Switching User Query Probability(HFRSUQP)works forward the updated UBP for easy and accuratefiltering of users’interests and improves WPR.A Probabilistic User Result Feature Ranking based on Gaussian Distribution(PURFR-GD)has been developed to user rank results in a web mining process.PURFR-GD decreases the delay time in the end-to-end workflow for SE personalization in various meth-ods by using the Gaussian Distribution Function(GDF).The theoretical analysis and experiment results of the proposed MC2-DC method automatically increase the updated UBP accuracy by 18.78%.HFRSUQP enabled extensive Maximize Log-Likelihood(ML-L)increases to 15.28%of User Personalized Information Search Retrieval Rate(UPISRT).For feature ranking,the PURFR-GD model defines higher Classification Accuracy(CA)and Precision Ratio(PR)while uti-lising minimum Execution Time(ET).Furthermore,UPISRT's ranking perfor-mance has improved by 20%.

基于Web挖掘的智能门户搜索引擎的研究

搜索引擎是人们在Internet上快速获得信息的重要工具之一,但是由于中文自身的特点,使得检索结果的准确性和相关性不是很高,将Web挖掘技术应用到搜索引擎领域,从而产生智能搜索引擎,将会给用户提供一个高效、准确的Web检索工具。文章首先介绍了搜索引擎的工作原理和相关概念,然后介绍了Web挖掘的定义、分类和应用。最后,详细讨论了Web挖掘技术在智能搜索引擎的重要应用。

Web信息检索研究进展

Web上大量、分布、动态的信息造成了“信息过载”,如何在传统信息检索技术的基础上开展针对 Web的检索工作已经成为一项重要的研究课题 .但是 ,繁多的 Web信息检索系统和各种模糊的概念给用户的选择和研究人员的讨论带来了不便 .同时 ,有关 Web信息检索最新技术的比较完整的分析又十分缺乏 .在此 ,对 Web信息检索技术进行了综述 ,从 Web信息检索系统的层次化分类 (搜索引擎与目录、元搜索引擎、信息检索 agent)、一般机制和关键新技术 (基于超链的相关度排序、检索结果的联机聚类、基于概念的检索、相关度反馈 )等方面加以阐述 。

Web信息采集研究进展

As a basic component of search engine and a series of other services on Web,Web crawler is playing an important role. Roughly,a Web crawler is a program which automatically traverses the Web by downloading documents and following links from page to page. This article detailedly explains the principles and difficulties on the Web crawler,comprehensively argues several hot directions of Web crawler,and at last views the new direction of Web crawler.

基于元数据与Z39.50的分布协作式Web信息检索

Web上大量的异质、分布、动态的信息造成了“信息过载”.如何有效地为用户提供 Web信息检索已经成为一项重要的研究课题 .Web搜索引擎部分地解决了信息检索问题 ,然而其效果却远远不能令人满意 .提出了 Web信息检索的分布协作策略以取代传统的集中式信息检索方式 ;给出了一种新的 Web信息检索系统模型 ,该模型支持对 Web文档的元数据进行检索 ,并采用 Z39.50协议作为接口标准 ,以克服不同信息检索系统之间的访问异构性 .在此基础上 ,设计了一个分布协作式 Web信息检索框架 ,用以帮助用户有效地进行 Web信息检索 .

网络爬虫在Web信息搜索与数据挖掘中应用

分析了万维网不良网络信息对网络文化安全带来的挑战,提出了Web信息搜索与数据挖掘体系结构,并介绍了该体系结构中的关键技术和运行原理。分析了普通爬虫所实现的功能和不足之后,重点论述了该爬虫的工作原理、实现方式和性能分析以及该爬虫不同于其它爬虫的功能和在Web信息搜索与数据挖掘体系中应用。通过试验测试表明,该爬虫能够很好地获取万维网上的各种信息资源,有助于网络文化内容监测与管理。

Web结构挖掘

概述了Web结构挖掘的有关概念和相关应用，详细分析和比较了基于超链接结构分析的网页排序的最权威算法，提出了有关Web结构挖掘技术的几个新的研究方向,供感兴趣的同行参考。