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The thesis describes a search engine of Google company and its method of web pages evaluating, which is used for sorting them on the search engine result page. In addition to the description of how search engines work in general, the thesis targets on differencies between Google and other search engines, as well as it mentions characteristics and principles designed by its founders, Larry Page and Sergey Brin, which make it unique. In the second part there are outlined some of many signals which are rated for every single web page. The most concerned signal is PageRank as the leading idea of pages evaluating, based on their link scheme which is founded on principles of citation analysis. Factors for evaluating are analysed using particular examples, and the topic is ended by the description of Google search engines changes from its beginning in 1998 until present.

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Current Web image search engines, such as Google or Bing Images, adopt a hybrid search approach in which a text-based query (e.g. "apple") is used to retrieve a set of relevant images, which are then refined by the user (e.g. by re-ranking the retrieved images based on similarity to a selected example). This approach makes it possible to use both text information (e.g. the initial query) and image features (e.g. as part of the refinement stage) to identify images which are relevant to the user. One limitation of these current systems is that text and image features are treated as independent components and are often used in a decoupled manner. This work proposes to develop an integrated hybrid search method which leverages the synergies between text and image features. Recently, there has been tremendous progress in the computer vision community in learning models of visual concepts from collections of example images. While impressive performance has been achieved on standardized data sets, scaling these methods so that they are capable of working at web scale remains a significant challenge. This work will develop approaches to visual modeling that can be scaled to address the task of retrieving billions of images on the Web. Specifically, we propose to address two research issues related to integrated text- and image-based retrieval. First, we will explore whether models of visual concepts which are learned from collections of web images can be utilized to improve the image ranking associated with a text-based query. Second, we will investigate the hypothesis that the click-patterns associated with standard web image search engines can be utilized to learn query-specific image similarity measures that support improved query-refinement performance. We will evaluate our research by constructing a prototype integrated hybrid retrieval system based on the data from 300K real-world image queries. We will conduct user-studies to evaluate the effectiveness of our learned similarity measures and quantify the benefit of our method in real world search tasks such as target search.

This thesis focuses on index and retrieval mechanism of an ontology based medical domain search engine. First, indexing techniques and retrieval methods are reviewed. Then, a special indexing and retrieval mechanism are introduced. This thesis also specifies the functional requirements of these mechanisms. Finally, an evaluation is given by indicating the positive and negative aspects of mechanisms.

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This research is an integral part of the effort aimed at overcoming the limitations of the classic search engines. This thesis is concerned with the investigation of the impact of different modes of mediation on the web search process. Conceptually, it is divided into three main parts. The first part details the investigation of methods and mechanisms in user profile generation and in filtering search results. The second part deals with the presentation of an approach and its application in the development of a mediation framework between the user and the classic Web Search engines. This involved the integration of the explicit, implicit and hybrid modes of mediation within a content-based method, and was facilitated by the adoption of the Vector Space Model. The third part presents an extensive comparative evaluation of the impact of the different types of mediation systems on web search, in terms of precision, recall and F-measure. The thesis concludes by identifying the contribution of the research programme and the satisfaction of the stated objectives.

This thesis explores the use of multimodality to communicate retrieved results of Internet search engines. The investigation aimed to investigate suitable multimodal metaphors which would increase the level of usability of Internet search engine interfaces and enhance users` experience in the search activity. The study consisted of three experiments based on questionnaires and Internet search activities with a set of text-based and multimodal interfaces. These interfaces were implemented in two browsing platforms, named AVBRO and AVBRO II. In the first experiment, the efficiency of specific multimodal metaphors to communicate additional information of retrieved results was investigated. The experiment also sought to obtain users` views of these metaphors with a questionnaire. An experimental multimodal interface of the AVBRO platform, which communicated additional information with a combination of three 2D graphs and musical stimuli, was used as a basis for the experiment, together with the Google search engine. The results obtained led to the planning of a second experiment. The aim of this experiment was to obtain and compare the level of usability of four different experimental multimodal interfaces and one traditional text-based interface, all implemented in the AVBRO II platform. Effectiveness, efficiency and users` satisfaction were used as criteria to evaluate the usability of these interfaces. In the third and final experiment the usability analysis of a traditional text-based interface and the two most suitable experimental multimodal interfaces of the AVBRO II platform was further investigated. Learnability, errors rate, efficiency, memorability and users` satisfaction were used as criteria to evaluate the usability of these interfaces. The analysis of the results obtained from these experiments provided the basis for a set of design guidelines for the development of usable interfaces based on a multimodal approach.

<p>This thesis discusses the possibilities to visualise the presentation layer of a search engine on a mobile device in an alternative way. Previous work in the area has shown that the use of text-based-lists can be problematic when accessed on a device with a limited display. In the scope of the thesis and in order to tackle the current problems when displaying the results a literature review was carried out. The findings of the review formed the basis for a requirement definition on which a mock-up was developed. The mock-up was then evaluated and tested during a usability test where a number of users got to experience the alternative presentation layer that uses a visualisation technique called tree- map. The results from the test show that the mock-up could be seen as a alternative to the current presentation of results. The mock-up also shows that a future implementation could also include the use of categories and sorting of information in order to provide content with a meaning.</p>