Thesis (MPhil)--Stellenbosch University, 2004. / ENGLISH ABSTRACT: A growing body of studies is developing approaches to evaluate human interaction with Web search engines. Measuring the information retrieval effectiveness of World Wide Web search engines is costly because of the human relevance judgements involved. However, both for business enterprises and people it is important to know the most effective Web search engine, since such search engines help their users find a higher number of relevant Web pages with less effort. Furthermore, this information can be used for several practical purposes. This study does not attempt to describe all the currently available search engines, but provides a comparison of some, which are deemed to be among the most useful. It concentrates on search engines and their characteristics only. The goal is to help a new user get the most useful "hits" when using the various tools. / AFRIKAANSE OPSOMMING: Al hoe meer studies word gedoen om benaderings te ontwikkel vir die evaluasie van menslike interaksie met Web-soekenjins. Om te meet hoe effektief 'n soekenjin inligting op die Wêreldwye Web kan opspoor, is duur vanweë die mens se relevansiebeoordeling wat daarby betrokke is. Dit is egter belangrik dat die bestuurders van sake-ondememings en ander mense sal weet watter die mees doeltreffende soekenjins is, aangesien sulke soekenjins hulle gebruikers help om 'n hoër aantal relevante Webblaaie met minder inspanning te vind. Hierdie inligting kan ook gebruik word om 'n paar praktiese doelwitte te verwesenlik. Daar word nie gepoog om al die soekenjins wat tans beskikbaar is, te beskryf nie, maar sommige van die soekenjins wat as die nuttigste beskou word, word vergelyk. Daar word alleenlik op soekenjins en hulle kenmerke gekonsentreer. Die doel is om die nuwe gebruiker te help om die nuttigste inligting te verkry deur gebruik te maak van verskeie hulpmiddels.

Thesis submitted in fulfilment of the requirements for the degree Magister Technologiae in Information Technology in the Faculty of Informatics and Design at the CAPE PENINSULA UNIVERSITY OF TECHNOLOGY 2007 / The objective of this study was to investigate and report on user preference of Search Engine Optimisation (SEO), versus Pay Per Click (PPC) results. This will assist online advertisers to identify their optimal Search Engine Marketing (SEM) strategy for their specific target market. Research shows that online advertisers perceive PPC as a more effective SEM strategy than SEO. However, empirical evidence exists that PPC may not be the best strategy for online advertisers, creating confusion for advertisers considering a SEM campaign. Furthermore, not all advertisers have the funds to implement a dual strategy and as a result advertisers need to choose between a SEO and PPC campaign. In order for online advertisers to choose the most relevant SEM strategy, it is of importance to understand user perceptions of these strategies. A quantitative research design was used to conduct the study, with the purpose to collect and analyse data. A questionnaire was designed and hosted on a busy website to ensure maximal exposure. The questionnaire focused on how search engine users perceive SEM and their click response towards SEO and PPC respectively. A qualitative research method was also used in the form of an interview. The interview was conducted with representatives of a leading South African search engine, to verify the results and gain experts’ opinions. The data was analysed and the results interpreted. Results indicated that the user perceived relevancy split is 45% for PPC results, and 55% for SEO results, regardless of demographic factors. Failing to invest in either one could cause a significant loss of website traffic. This indicates that advertisers should invest in both PPC and SEO. Advertisers can invest in a PPC campaign for immediate results, and then implement a SEO campaign over a period of time. The results can further be used to adjust a SEM strategy according to the target market group profile of an advertiser, which will ensure maximum effectiveness.

Thesis Submitted in fulfilment of the requirements for the degree MAGISTER TECHNOLOGIAE In BUSINESS INFORMATION SYSTEMS in the FACULTY OF BUSINESS at the CAPE PENINSULA UNIVERSITY OF TECHNOLOGY 2011 / With over a billion Internet users surfing the Web daily in search of information, buying, selling and accessing social networks, marketers focus intensively on developing websites that are appealing to both the searchers and the search engines. Millions of webpages are submitted each day for indexing to search engines. The success of a search engine lies in its ability to provide accurate search results. Search engines’ algorithms constantly evaluate websites and webpages that could violate their respective policies. For this reason some websites and webpages are subsequently blacklisted from their index. Websites are increasingly being utilised as marketing tools, which result in major competition amongst websites. Website developers strive to develop websites of high quality, which are unique and content rich as this will assist them in obtaining a high ranking from search engines. By focusing on websites of a high standard, website developers utilise search engine optimisation (SEO) strategies to earn a high search engine ranking. From time to time SEO practitioners abuse SEO techniques in order to trick the search engine algorithms, but the algorithms are programmed to identify and flag these techniques as spamdexing. Search engines do not clearly explain how they interpret keyword stuffing (one form of spamdexing) in a webpage. However, they regard spamdexing in many different ways and do not provide enough detail to clarify what crawlers take into consideration when interpreting the spamdexing status of a website. Furthermore, search engines differ in the way that they interpret spamdexing, but offer no clear quantitative evidence for the crossover point of keyword dense website text to spamdexing. Scholars have indicated different views in respect of spamdexing, characterised by different keyword density measurements in the body text of a webpage. This raised several fundamental questions that form the basis of this research. This research was carried out using triangulation in order to determine how the scholars, search engines and SEO practitioners interpret spamdexing. Five websites with varying keyword densities were designed and submitted to Google, Yahoo! and Bing. Two phases of the experiment were done and the results were recorded. During both phases almost all of the webpages, including the one with a 97.3% keyword density, were indexed. The aforementioned enabled this research to conclusively disregard the keyword stuffing issue, blacklisting and any form of penalisation. Designers are urged to rather concentrate on usability and good values behind building a website. The research explored the fundamental contribution of keywords to webpage indexing and visibility. Keywords used with or without an optimum level of measurement of richness and poorness result in website ranking and indexing. However, the focus should be on the way in which the end user would interpret the content displayed, rather than how the search engine would react towards the content. Furthermore, spamdexing is likely to scare away potential clients and end users instead of embracing them, which is why the time spent on spamdexing should rather be used to produce quality content.

by Wong, Wai Ching. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 91-94). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgments --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Semistructured Data --- p.2 / Chapter 1.2 --- Problem Addressed in the Thesis --- p.4 / Chapter 1.2.1 --- Labels and Values --- p.4 / Chapter 1.2.2 --- Discover Labels for the Same Attribute --- p.5 / Chapter 1.2.3 --- Classifying A Web Page --- p.6 / Chapter 1.3 --- Organization of the Thesis --- p.8 / Chapter 2 --- Background --- p.8 / Chapter 2.1 --- Related Work on Web Data --- p.8 / Chapter 2.1.1 --- Object Exchange Model (OEM) --- p.9 / Chapter 2.1.2 --- Schema Extraction --- p.11 / Chapter 2.1.3 --- Discovering Typical Structure --- p.15 / Chapter 2.1.4 --- Information Extraction of Web Data --- p.17 / Chapter 2.2 --- Automatic Text Processing --- p.19 / Chapter 2.2.1 --- Stopwords Elimination --- p.19 / Chapter 2.2.2 --- Stemming --- p.20 / Chapter 3 --- Web Data Definition --- p.22 / Chapter 3.1 --- Web Page --- p.22 / Chapter 3.2 --- Problem Description --- p.27 / Chapter 4 --- Hierarchical Structure --- p.32 / Chapter 4.1 --- Types of HTML Tags --- p.33 / Chapter 4.2 --- Tag-tree --- p.36 / Chapter 4.3 --- Hierarchical Structure Construction --- p.41 / Chapter 4.4 --- Hierarchical Structure Statistics --- p.50 / Chapter 5 --- Similar Labels Discovery --- p.53 / Chapter 5.1 --- Expression of Hierarchical Structure --- p.53 / Chapter 5.2 --- Labels Discovery Algorithm --- p.55 / Chapter 5.2.1 --- Phase 1: Remove Non-label Nodes --- p.57 / Chapter 5.2.2 --- Phase 2: Identify Label Nodes --- p.61 / Chapter 5.2.3 --- Phase 3: Discover Similar Labels --- p.66 / Chapter 5.3 --- Performance Evaluation of Labels Discovery Algorithm --- p.76 / Chapter 5.3.1 --- Phase 1 Results --- p.75 / Chapter 5.3.2 --- Phase 2 Results --- p.77 / Chapter 5.3.3 --- Phase 3 Results --- p.81 / Chapter 5.4 --- Classifying a Web Page --- p.83 / Chapter 5.4.1 --- Similarity Measurement --- p.84 / Chapter 5.4.2 --- Performance Evaluation --- p.86 / Chapter 6 --- Conclusion --- p.89

Through their role of content proxy, in particular on search engine result pages, Web summaries play an essential part in the discovery of information and services on the Web. In their simplest form, Web summaries are snippets based on a user-query and are obtained by extracting from the content of Web pages. The focus of this work, however, is on indicative Web summarization, that is, on the generation of summaries describing the purpose, topics and functionalities of Web pages. In many scenarios — e.g. navigational queries or content-deprived pages — such summaries represent a valuable commodity to concisely describe Web pages while circumventing the need to produce snippets from inherently noisy, dynamic, and structurally complex content. Previous approaches have identified linking pages as a privileged source of indicative content from which Web summaries may be derived using traditional extractive methods. To be reliable, these approaches require sufficient anchortext redundancy, ultimately showing the limits of extractive algorithms for what is, fundamentally, an abstractive task. In contrast, we explore the viability of abstractive approaches and propose a nearest-neighbors summarization framework leveraging summaries of conceptually related (neighboring) Web pages. We examine the steps that can lead to the reuse and adaptation of existing summaries to previously unseen pages. Specifically, we evaluate two Text-to-Text transformations that cover the main types of operations applicable to neighbor summaries: (1) ranking, to identify neighbor summaries that best fit the target; (2) target adaptation, to adjust individual neighbor summaries to the target page based on neighborhood-specific template-slot models. For this last transformation, we report on an initial exploration of the use of slot-driven compression to adjust adapted summaries based on the confidence associated with token-level adaptation operations. Overall, this dissertation explores a new research avenue for indicative Web summarization and shows the potential value, given the diversity and complexity of the content of Web pages, of transferring, and, when necessary, of adapting, existing summary information between conceptually similar Web pages.

Cheng Tung Yin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 136-141). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.1.1 --- Information Retrieval --- p.1 / Chapter 1.1.2 --- Search Engines --- p.2 / Chapter 1.1.3 --- Data Merging --- p.3 / Chapter 1.2 --- Meta-search Engines --- p.3 / Chapter 1.2.1 --- Framework and Techniques Employed --- p.3 / Chapter 1.2.2 --- Advantages of meta-searching --- p.8 / Chapter 1.3 --- Contribution of the Thesis --- p.10 / Chapter 1.4 --- Organization of the Thesis --- p.12 / Chapter 2 --- Literature Review --- p.14 / Chapter 2.1 --- Preliminaries --- p.14 / Chapter 2.2 --- Fusion Methods --- p.15 / Chapter 2.2.1 --- Fusion methods based on a document's score --- p.15 / Chapter 2.2.2 --- Fusion methods based on a document's ranking position --- p.23 / Chapter 2.2.3 --- Fusion methods based on a document's URL title and snippets --- p.30 / Chapter 2.2.4 --- Fusion methods based on a document's entire content --- p.40 / Chapter 2.3 --- Comparison of the Fusion Methods --- p.42 / Chapter 2.4 --- Relevance Feedback --- p.46 / Chapter 3 --- Research Methodology --- p.48 / Chapter 3.1 --- Investigation of the features of the retrieved results from the search engines --- p.48 / Chapter 3.2 --- Types of relationships --- p.53 / Chapter 3.3 --- Order of Strength of the Relationships --- p.64 / Chapter 3.3.1 --- Derivation of the weight for each kind of relationship (criterion) --- p.68 / Chapter 3.4 --- Observation of the relationships between retrieved objects and the effects of these relationships on the relevance of objects --- p.69 / Chapter 3.4.1 --- Observation on the relationships existed in items that are irrelevant and relevant to the query --- p.68 / Chapter 3.5 --- Proposed re-ranking algorithms --- p.89 / Chapter 3.5.1 --- Original re-ranking algorithm (before modification) --- p.91 / Chapter 3.5.2 --- Modified re-ranking algorithm (after modification) --- p.95 / Chapter 4 --- Evaluation Methodology and Experimental Results --- p.101 / Chapter 4.1 --- Objective --- p.101 / Chapter 4.2 --- Experimental Design and Setup --- p.101 / Chapter 4.2.1 --- Preparation of data --- p.101 / Chapter 4.3 --- Evaluation Methodology --- p.104 / Chapter 4.3.1 --- Evaluation of the relevance of a document to the corresponding query --- p.104 / Chapter 4.3.2 --- Performance Measures of the Evaluation --- p.105 / Chapter 4.4 --- Experimental Results and Interpretation --- p.106 / Chapter 4.4.1 --- Precision --- p.107 / Chapter 4.4.2 --- Recall --- p.107 / Chapter 4.4.3 --- F-measure --- p.108 / Chapter 4.4.4 --- Overall evaluation results for the ten queries for each evaluation tool --- p.110 / Chapter 4.4.5 --- Discussion --- p.123 / Chapter 4.5 --- Degree of difference between the performance of systems --- p.124 / Chapter 4.5.1 --- Analysis using One-Way ANOVA --- p.124 / Chapter 4.5.2 --- Analysis using paired samples T-test --- p.126 / Chapter 5 --- Conclusion --- p.131 / Chapter 5.1 --- "Implications, Limitations, and Future Work" --- p.131 / Chapter 5.2 --- Conclusions --- p.133 / Bibliography --- p.134 / Chapter A --- Paired samples T-test for F-measures of systems retrieving all media's items --- p.140

Due to the information explosion on the Internet, effective information search techniques are required to retrieve the desired information from the Web. Based on much analysis on users' search intention and the variant forms of Web content, we find that both the query and the indexed web content are often associated with various context information, which can provide much essential information to indicate the ranking relevance in Web search. This dissertation seeks to develop new search algorithms and techniques by taking advantage of rich context information to improve search quality and consists of two major parts. In the first part, we study the context of the query in terms of various ranking objectives of different queries. In order to improve the ranking relevance, we propose to incorporate such query context information into the ranking model. Two general approaches will be introduced in the following of this dissertation. The first one proposes to incorporate query difference into ranking by introducing query-dependent loss functions, by optimizing which we can obtain better ranking model. Then, we investigate another approach which applies a divide-and-conquer framework for ranking specialization. The second part of this dissertation investigates how to extract the context of specific Web content and explore them to build more effective search system. This study is based on the new emerging social media content. Unlike traditional Web content, social media content is inherently associated with much new context information, including content semantics and quality, user reputation, and user interactions, all of which provide useful information for acquiring knowledge from social media. In this dissertation, we seek to develop algorithms and techniques for effective knowledge acquisition from collaborative social media environments by using the dynamic context information. We first propose a new general framework for searching social media content, which integrates both the content features and the user interactions. Then, a semi-supervised framework is proposed to explicitly compute content quality and user reputation, which are incorporated into the search framework to improve the search quality. Furthermore, this dissertation also investigates techniques for extracting the structured semantics of social media content as new context information, which is essential for content retrieval and organization.

Thesis (M.S.)--University of Florida, 2002. / Title from title page of source document. Includes vita. Includes bibliographical references.

Google Inc. struggles arduously on the digital battlefield in China’s Internet search engine market. In China, Baidu.com has been described as China’s Google for years and challenged Google’s expansion. This study provides an overview of the Internet service development in China, an illustration of the search engines’ profitability models, and an evaluation of Guge (Google China) and Baidu’s service designs. Overall, the research shows an attempt to understand the possible advantages and disadvantages when a multinational Internet service company enters China. Two notions emerge. First, standardization and adaptation may need to be nicely balanced for the subsidiary company in order to profit in China’s Internet market. Second, Google’s operation in China, Guge, stands strong on the service design end, especially in the area of “ease of use,” “informativeness,” and “fulfillment/reliability.” However, Guge’s major rival, Baidu, shows its advantage on a wider selection of online services. Therefore, in the long run, which company will win at the finishing line is still too early to tell / Google in China -- Google, Baidu and Guge -- Search engine's revenue model in China. / Department of Telecommunications

Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2005. / Includes bibliographical references (leaves 102-119). Also available online.