Search

The proposed research work aims to discuss and explore various constraints of traditional web page search and ranking systems primarily in the present generation of big data. The primary objective is to facilitate a web user by presenting a most personalized web page ranking as a response to a user’s search query by considering tastes and browsing history of the user while previously searching on the web. This research intends to design and develop a machine learning based next generation of web page ranking algorithm, i.e., Advanced Cluster Vector Page Ranking algorithm (ACVPR). This ACVPR algorithm is implemented in the form of an Intelligent Mata Search System-Personalized tool to evaluate the performance of the algorithm. The ACVPR algorithm arm the user with a powerful meta-search tool to facilitate the user by providing a web page ranking order to quickly satisfy the personalized needs especially when the search query is erroneous or incomplete. An extensive mathematical and experimental evaluation of the developed logistic regression model by calculating and comparing various evaluation metrics such as specificity, sensitivity, precision, recall using R statistical tool shows the improved efficiency as compared to other popular search engines.

Purpose/Objective(s): Cancer patients are increasingly using the Internet to educate themselves about COVID-19. Recent studies have shown that cancer patients are at risk of more serious outcomes of COVID-19 compared to the general population. Some cancer treatments such as chemotherapy can impact the immune system, which may make COVID-19 infection more dangerous. This study looks to systematically examine the quality of web resources available for cancer patients about COVID-19. Materials/Methods: The term “COVID-19 Risk and Cancer” was searched in Google and metasearch engines Yippy and Dogpile. URLs were recorded from each search and inclusion and exclusion criteria were applied. The results from the 3 lists were combined to come up with a final list based on overall average rank order. This list was analyzed using a previously validated structured rating tool with respect to accountability, currency, interactivity, readability, and content coverage and accuracy. Results: 398 websites were identified prior (this includes overlap between the three search sites used), 37 websites were included for analysis. Out of 37 websites, only 43% disclosed authorship and 24% cited sources. Most websites (76%) revealed date of creation, and 32% were updated less than 3 months before the date of search. 68% of websites enabled questions to be sent to the author or webmaster regarding COVID-19 risk queries. 54% of websites had high school readability (8.0-12.0), 43% were at university level or above, and only one website demonstrated the recommended reading level for general public (below 8.0). Topics most commonly discussed were special consideration for cancer patients in COVID-19 (84%), COVID-19 risk factors (73%), and infection prevention (62%), while topics least covered were COVID-19 incidence/prevalence (5%), prognosis (8%), and treatment (16%). Conclusion: There is some COVID-19 in cancer risk information available online, but quality is variable. The total number of sites with relevant information related to COVID-19 and cancer was relatively low and many sites lacked markers for accountability. Some information may not be up to date and content may be difficult to comprehend. Healthcare professionals may direct cancer patients to the most reliable online resources about COVID-19 and cancer shown in this study. In addition, this may be helpful to consider when designing comprehensive web resources regarding COVID-19.

The rank aggregation problem has received significant recent attention within the computer science community. Its applications today range far beyond the original aim of building metasearch engines to problems in machine learning, recommendation systems and more. Several algorithms have been proposed for these problems, and in many cases approximation guarantees have been proven for them. However, it is also known that some Markov chain based algorithms (MC1, MC2, MC3, MC4) perform extremely well in practice, yet had no known performance guarantees. We prove supra-constant lower bounds on approximation guarantees for all of them. We also raise the lower bound for sorting by Copeland score from 3/2 to 2 and prove an upper bound of 11, before showing that in particular ways, MC4 can nevertheless be seen as a generalization of Copeland score.

A Retrieval System requires several components that define its functionality and behavior. In the case of a meta-search engine for the retrieval of scientific data, a schema that defines the way to store such data is considered a necessary element for its evolution. Unified profiles have been developed for the data storage of the entities involved in the scientific data management, generated from the fact of publishing a scientific paper. Such profiles are considered the beginning of the generation of new components for the meta-search engine that, using the proprietary information, can deliver information relevant for the user of the tool. To this end, the use of an intelligent distributed data warehouse is proposed.

Evaluating Information Retrieval systems is crucial to making progress in search technologies. Evaluation is often based on assembling reference collections consisting of documents, queries and relevance judgments done by humans. In large-scale environments, exhaustively judging relevance becomes infeasible. Instead, only a pool of documents is judged for relevance. By selectively choosing documents from the pool we can optimize the number of judgments required to identify a given number of relevant documents. We argue that this iterative selection process can be naturally modeled as a reinforcement learning problem and propose innovative and formal adjudication methods based on multi-armed bandits. Casting document judging as a multi-armed bandit problem is not only theoretically appealing, but also leads to highly effective adjudication methods. Under this bandit allocation framework, we consider stationary and non-stationary models and propose seven new document adjudication methods (five stationary methods and two non-stationary variants). Our paper also reports a series of experiments performed to thoroughly compare our new methods against current adjudication methods. This comparative study includes existing methods designed for pooling-based evaluation and existing methods designed for metasearch. Our experiments show that our theoretically grounded adjudication methods can substantially minimize the assessment effort.

The Smart Drug Search is publicly accessible at http://sing.ei.uvigo.es/sds/. The BIOMedical Search Engine Framework is freely available for non-commercial use at https://github.com/agjacome/biomsef, Background and Objectives: Text mining and semantic analysis approaches can be applied to the construction of biomedical domain-specific search engines and provide an attractive alternative to create personalized and enhanced search experiences. Therefore, this work introduces the new open-source BIOMedical Search Engine Framework for the fast and lightweight development of domain-specific search engines. The rationale behind this framework is to incorporate core features typically available in search engine frameworks with flexible and extensible technologies to retrieve biomedical documents, annotate meaningful domain concepts, and develop highly customized Web search interfaces. Methods: The BIOMedical Search Engine Framework integrates taggers for major biomedical concepts, such as diseases, drugs, genes, proteins, compounds and organisms, and enables the use of domain-specific controlled vocabulary. Technologies from the Typesafe Reactive Platform, the AngularJS JavaScript framework and the Bootstrap HTML/CSS framework support the customization of the domain-oriented search application. Moreover, the RESTful API of the BIOMedical Search Engine Framework allows the integration of the search engine into existing systems or a complete web interface personalization. Results The construction of the Smart Drug Search is described as proof-of-concept of the BIOMedical Search Engine Framework. This public search engine catalogs scientific literature about antimicrobial resistance, microbial virulence and topics alike. The keyword-based queries of the users are transformed into concepts and search results are presented and ranked accordingly. The semantic graph view portraits all the concepts found in the results and the researcher may look into the relevance of different concepts, the strength of direct relations, and non-trivial, indirect relations. The number of occurrences of the concept shows its importance to the query, and the frequency of concept co-occurrence is indicative of biological relations meaningful to that particular scope of research. Conversely, indirect concept associations, i.e. concepts related by other intermediary concepts, can be useful to integrate information from different studies and look into non-trivial relations. Conclusions The BIOMedical Search Engine Framework supports the development of domain-specific search engines. The key strengths of the framework are modularity and extensibility in terms of software design, the use of open-source consolidated Web technologies, and the ability to integrate any number of biomedical text mining tools and information resources. Currently, the Smart Drug Search keeps over 1,186,000 documents, containing more than 11,854,000 annotations for 77,200 different concepts., This work was partially funded by the [14VI05] ContractProgramme from the University ofVigo and theAgrupamento INBIOMED from DXPCTSUG-FEDER unha maneira de facer Europa(2012/273).The research leading to these results has also received funding from the European Union Seventh Framework Programme FP7/REGPOT-2012-2013.1 under grant agreement n° 316265,BIOCAPS.This document reflects only the author’s views, and the European Union is not liable for any use that may be made of the information contained herein

We propose a preference relations based unsupervised rank aggregation algorithm.The algorithm gives weights to input rankers depending on their qualities.Ranker quality is estimated in unsupervised way using a variant of majority opinion.Performed experimental evaluation using supervised and unsupervised metrics.Kendall-Tau distance not suitable for evaluating metasearch algorithms. Rank aggregation mechanisms have been used in solving problems from various domains such as bioinformatics, natural language processing, information retrieval, etc. Metasearch is one such application where a user gives a query to the metasearch engine, and the metasearch engine forwards the query to multiple individual search engines. Results or rankings returned by these individual search engines are combined using rank aggregation algorithms to produce the final result to be displayed to the user. We identify few aspects that should be kept in mind for designing any rank aggregation algorithm for metasearch. For example, generally equal importance is given to the input rankings while performing the aggregation. However, depending on the indexed set of web pages, features considered for ranking, ranking functions used etc. by the individual search engines, the individual rankings may be of different qualities. So, the aggregation algorithm should give more weight to the better rankings while giving less weight to others. Also, since the aggregation is performed when the user is waiting for response, the operations performed in the algorithm need to be light weight. Moreover, getting supervised data for rank aggregation problem is often difficult. In this paper, we present an unsupervised rank aggregation algorithm that is suitable for metasearch and addresses the aspects mentioned above.We also perform detailed experimental evaluation of the proposed algorithm on four different benchmark datasets having ground truth information. Apart from the unsupervised Kendall-Tau distance measure, several supervised evaluation measures are used for performance comparison. Experimental results demonstrate the efficacy of the proposed algorithm over baseline methods in terms of supervised evaluation metrics. Through these experiments we also show that Kendall-Tau distance metric may not be suitable for evaluating rank aggregation algorithms for metasearch.

This paper presents a Chinese topic crawler focused on customer development, in order to meet the needs of users for more accurate and particular Internet information. The concept of meta-search engine is introduced, and the keywords are expanded by the ontology of HowNet. Through the web crawler, preprocessing and classification, the information on customer relations can be divided into three categories: company, platform and meaningless. Numerical experiments show that satisfactory results can be obtained in some particular information-seeking areas. The average accuracy for classification is more than 80%, which can meet customer needs in most cases.

A Web database typically responds to a query with a Web page, which encodes the query results into semi-structured data objects using HTML tags. We call such data objects Web data records or data records. Mining Web data records is very important for many applications, e.g., meta search, comparative shopping, etc. This paper proposes a new effective approach called AutoRM, which mines data records from single Web page automatically. AutoRM involves three major steps: (1) constructing the DOM tree of the given Web page; (2) mining all sets of adjacent similar C-Records (Candidate data Records) from the constructed DOM tree; (3) mining actual data records from C-Records. In many Web pages, similar data records are distributed in bigger and adjacent similar objects. Existing approaches typically identify such objects as data records. Conversely, AutoRM views such objects as C-Records, and mines actual data records from them. One key issue for mining similar data records is the boundary detection of each data record. Existing approaches typically make some brittle assumptions for handling this issue. By making more robust assumptions, AutoRM tends to detect data record boundaries more accurately. Experimental results show that AutoRM is highly effective, and outperforms state-of-the-art approaches.

Most of the existing Web search solutions are built for satisfying broad set of users regardless whether naive or professionals. Further, with the emergence of high speed internet applications and advanced Web 2.0 based Rich Internet Applications (i.e. blogs, wikis, etc.), it has become much easier for users to publish data over the Web. This brings a challenge for Web search solutions to let individual users find the right information as per their preferences. Different users of the Web may have different preferences. Search results for the same query from different users may differ in priority for individual users. In this paper, we describe our approach of enabling personalized Web search for users based on their preferences. It is a challenge in itself to have the preferences of the users known to and considered by search engines. We have designed and developed our unique approach of finding the preferences of users from the relevant parts of their social networks and communities. We believe that the information related to the queries posed by users may have strong correlation with relevant information in their social networks. In order to find out the personal interests and the social-contexts, we find out (1) activities of users in their social-networks, and (2) relevant information from user's social networks, based on our proposed trust and relevance matrices. We have developed a mechanism that extracts information from a user's social network and uses it to re-rank the results from a search engine. We have also discussed the implementation and evaluation of our proposed solution by emphasizing how it improves the Web search.

External search and internal search cohere searching sessions.The referral query from external search can be predictive of internal searching.There are common patterns that one can use to classify internal searchers. We analyze the transitions from external search, searching on web search engines, to internal search, searching on websites. We categorize 295,571 search episodes composed of a query submitted to web search engines and the subsequent queries submitted to a single website search by the same users. There are a total of 1,136,390 queries from all searches, of which 295,571 are external search queries and 840,819 are internal search queries. We algorithmically classify queries into states and then use n-grams to categorize search patterns. We cluster the searching episodes into major patterns and identify the most commonly occurring, which are: (1) Explorers (43% of all patterns) with a broad external search query and then broad internal search queries, (2) Navigators (15%) with an external search query containing a URL component and then specific internal search queries, and (3) Shifters (15%) with a different, seemingly unrelated, query types when transitioning from external to internal search. The implications of this research are that external search and internal search sessions are part of a single search episode and that online businesses can leverage these search episodes to more effectively target potential customers.

It is common knowledge that the web has been continuously evolving, from a read medium to a read/write scheme and, lately, to a read/write/infer corpus. To follow the evolution, Search Engines have been undergoing continuous updates in order to provide the user with a well-targeted, personalized and improved experience of the web. Along with this focus on content quality and user preferences, search engines have also been striving to integrate Semantic Web primitives, in order to enhance their intelligence. Current work discusses the evolution of search engine ranking factors in a Web 2.0 and Web 3.0 context. A benchmark crawler LSHrank, has been developed, which employs known search engine APIs and evaluates results against various already established metrics, in different domains and types of web content. The ultimate LSHrank objective is the development of a Search Engine Optimization (SEO) mechanism that will enrich and alter the content of a website in order to achieve its optimal ranking in search engine result pages (SERPs). Graphical abstractDisplay Omitted HighlightsA mechanism that delivers quality SEO based on LDA and state-of-the-art Search Engine (SE) metrics.A New factors are identified that affect SE rankings are evaluated against Web 2.0 and Web 3.0 content.A Bing/Google exhibit similar behavior and are influenced by the semantic web, while Yahoo! differs.A Domain of websites is a crucial parameter that influences SE ranking.

ResumenEn la práctica médica pediátrica es frecuente encontrar a pacientes en circunstancias que representan un dilema ético para los profesionales de la salud. Un dilema corresponde a una situación en la que los preceptos morales o las obligaciones de similar obligatoriedad ética se encuentran en conflicto, de forma que cualquier solución posible al dilema es moralmente intolerable.Una revisión de la literatura permitió identificar diferentes modelos que abordan esta clase de dilemas. Se localizaron artículos utilizando las bases de datos Ebsco Host, ProQuest, Ovid e InMex, así como metabuscadores como metacrawler. Algunos de los modelos analizados fueron los siguientes: el Modelo de Anne Davis, el Método de Nijmegen, el Método de Diego Gracia, el Método Integral, el Modelo del Centro de Ética Médica de Bochum, el Modelo de Brody y Payton, el Modelo de Curtin y Flaherty, el Modelo de Thompson y Thompson, la Fórmula SAD, el Modelo de Javier Morata, el Modelo de Elaine Congress, el Modelo IFSW, el Modelo de Loewenberg y Dolgoff, el Modelo de la Ley Social, el Método DOER, el Modelo de Brommer, el Modelo de Corey y Callanan, el Modelo de Pope y Vasquez, el Modelo de Bush, Connell y Denney, el Modelo de Ferrell, Gresham y Fraedrich y el Modelo de Hunt y Vitell.Los criterios compartidos entre los diferentes modelos fueron los siguientes: a) la especificación del dilema ético; b) la descripción de los hechos a considerar; c) la definición de valores, principios y la postura ética que será tomada en consideración; y d) la toma de decisiones con la identificación de alternativas de solución. De acuerdo con la literatura revisada, se explican algunos modelos con el fin de identificar y ejemplificar elementos críticos que pudieran ser utilizados de manera práctica por los Comités de Ética Clínica u Hospitalaria en las instituciones de salud pediátrica en México.AbstractIn pediatric medical practice it is common to encounter situations that represent a dilemma for health professionals. A dilemma occurs when ethical problems found in professional practice cause serious internal conflicts because they imply actions that contradict their colleagues, employees, or their own personal values and are classified as personal value conflicts, conflicts with other professionals, conflicts with clients and with organizations.A literature review allowed identifying different models to debate these types of dilemmas. The present work is a review of the search of scientific articles using databases such as Ebsco Host, ProQuest, Ovid, and InMex as well as metasearch tools such as metacrawler. The models found are as follows: Model of Anne Davis, Nijmegen method, Method of Diego Gracia, Integral method, Bochum Center Ethics model, Model of Brody and Payton, Model of Curtin and Flaherty, Model of Thompson and Thompson, SAD method, Model of Javier Morata, Model of Elaine Congress, IFSW model, Model of Loewenberg and Dolgoff, Ley Social Model, DOER method, Model of Brommer, Model of Corey and Callanan, Model of Pope and Vasquez, Model of Bush, Connell and Denney, Model of Ferrell, Gresham and Fraedrich, and Model of Hunt and Vitell.The key criteria shared in the different models are a) specifying the ethical dilemma, b) description of the facts, c) value definition, moral code and facts, decision making and d) identifying alternative solutions. In order to review the literature, some models are explained with the purpose of identifying and representing critical elements that clinical ethics committees could use in a practical manner in pediatric health institutions in Mexico.

In this paper, we present the idea of an ‘Analytic Search Engine Model’ that utilizes the regularly generated social media data for opinion formation. It combines the user ratings from different SNS platforms and analyzes them to form a collective opinion about a local business and ranks results in terms of this opinion formation, which ensures the reliability and quality of search results. This new mode of ranking web pages is termed as social rank, which is explained in this paper in details. It also gives the introduction to analytic search engine model, its architecture and the problems it may meet in future are presented.

The same query submitted to different search engine agents can lead to dissimilar suggestions and results. Comparing these results can be time consuming especially with some queries generating millions of URLs. Thus an efficient tool to compare the diverse suggestions and determine which search engine is the best for a particular query is useful. In this study, we applied feedback collation Algorithm to create a tool that shall assist the user on which search engine to deploy for a particular query.

Google has been steadily increasing its market share in the US, although its main competitor, Yahoo, began developing a successful knowledge-sharing service in 2005. To verify whether a knowledge-sharing service may increase a search engine’s competitiveness, this study considers the competition between an inferior search engine that has an option of introducing a knowledge-sharing service and a superior search engine without this service. We specifically investigate the conditions under which it would be more profitable for the inferior search engine to introduce a knowledge-sharing service rather than increase its search quality. We show that the inferior search engine’s profit-maximizing strategy mainly depends on both the amount of information available on the Internet and the difference in search quality between it and the superior search engine. When the search quality difference is small, the inferior search engine should introduce a knowledge-sharing service keeping its answer database inaccessible to the superior search engine. When the search quality difference is large, the inferior search engine generally had better improve its search technology. We also show the inferior search engine’s market-share-maximizing strategy when it introduces a knowledge-sharing service.

Most-favoured-nation clauses have been widely used by online hotel booking platforms before they were challenged by competition authorities in recent years. Following these antitrust decisions, there is a great interest in evaluating their impact on the market outcome. A convenient data source for that purpose may be hotel price meta-search sites. These data have been used in several econometric studies delivering diverging results. However, the study presented in this paper shows that data retrieved automatically from meta-search sites is typically unreliable for evaluating the impact of the prohibition of most-favoured-nation clauses. The key limitation of meta-search price data is their recurrent inability to distinguish between price differentiation and product differentiation.

In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24million pages is available at http://google.stanford.edu/ To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from 3years ago. This paper provides an in-depth description of our large-scale web search engine - the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections, where anyone can publish anything they want.

Chemical formulations are compositions of active ingredients and inert formulation components (adjuvants), which are more effective in combination than the active ingredients alone. Thus, formulation technology synergistically can improve the properties of active ingredients with regards to various aspects like application, uptake, safety or storage. Chemical formulations have a broad scope of uses ranging from pharmaceuticals and agrochemicals to cosmetics and materials protection. Since they represent the product as it is finally brought to market they have a high commercial and patent value. Comprehensive search and retrieval of chemical formulations both in patent and non-patent literature builds the basis of a successful reinforcement of patent protection, avoidance of infringement of third party patent rights and competitive analysis. The paper discusses the various aspects of chemical formulations to be considered in chemical formulation searching, and presents best practice strategies for search, retrieval, and analysis of both patent and non-patent literature as well as internet content preferably related to agrochemical and pharmaceutical formulations.

Nowadays, modern search engines quite satisfactorily answer users' queries, but the top results returned are not always relevant to the data the user is actually looking for. Hence, considerable efforts are made by search engines in order to rank the most relevant to the query results at the top. This work addresses the above problem and improves the performance of a search engine, especially when it comes to queries which have for example twofold meanings. The matter which the user is interested in is identified based on the results that he/she chooses, and then the most relevant ones are ranked higher. In addition, the results are recognized not only as text but also as semantic entities, which contain various semantic features. The semantic relation between results and text coverage are used as the main tool to achieve an optimized ranking, as opposed to other research papers so far. As a result, a new meta search application is developed, which, given a set of terms, combines Google results and then reorganizes (re-ranks) them based on the disambiguation offered by user clicks. In particular, after a ranking is achieved, the user makes a choice (click), the ranking is updated and the process is repeated. In order to prove our claims, apart from the description of the algorithm for refining the ranking of results, a web application has been developed, which was used to test the effectiveness of the system proposed.

In this paper, we consider a flowshop scheduling problem with sequence-dependent setup times and a bicriteria objective to minimize the work-in-process inventory for the producer and to maximize the customers' service level. The use of a bicriteria objective is motivated by the fact that successful companies in today's environment not only try to minimize their own cost but also try to fulfill their customers' need. Two main approaches, permutation and non-permutation schedules, are considered in finding the optimal schedule for a flowshop. In permutation schedules the sequence of jobs remains the same on all machines whereas in non-permutation schedule, jobs can have different sequence on different machines. A linear mathematical model for solving the non-permutation flowshop is developed to comply with all of the operational constraints commonly encountered in the industry, including dynamic machine availabilities, dynamic job releases, and the possibility of jobs skipping one or more machines, should their operational requirements deem that it was necessary. As the model is shown to be NP-hard, a metasearch heuristic, employing a newly developed concept known as the Tabu search with embedded progressive perturbation (TSEPP) is developed to solve, in particular, industry-size problems efficiently. The effectiveness and efficiency of the search algorithm are assessed by comparing the search algorithmic solutions with that of the optimal solutions obtained from CPLEX in solvable small problem instances.

Internet search has become an important part in people's daily life. People can find many types of information to meet different needs through search engines on the Internet. There are two issues for the current search engines: first, the users should predetermine the types of information they want and then change to the appropriate types of search engine interfaces. Second, most search engines can support multiple kinds of search functions, each function has its own separate search interface. While users need different types of information, they must switch between different interfaces. In practice, most queries are corresponding to various types of information results. These queries can search the relevant results in various search engines, such as query “Palace” contains the websites about the introduction of the National Palace Museum, blog, Wikipedia, some pictures and video information. This paper presents a new aggregative algorithm for all kinds of search results. It can filter and sort the search results by learning three aspects about the query words, search results and search history logs to achieve the purpose of detecting user's intention. Experiments demonstrate that this rank-based method for multi-types of search results is effective. It can meet the user's search needs well, enhance user's satisfaction, provide an effective and rational model for optimizing search engines and improve user's search experience.

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. Highlights We analyse user queries to highlight the main factors affecting search engine choice. We evaluate the reasons of varying the length of queries by different search engines. Results can assist whether to offer topic specific search to gain market share. Results could be used to develop strategies for designing advanced search engine. Results could also be used to develop search engine service provider business.

Existing meta-search engines return web search results based on the page relevancy to the query, their popularity and content. It is necessary to provide a meta-search engine capable of ranking results considering the user’s field of interest. Social networks can be useful to find the users’ tendencies, favorites, skills, and interests. In this paper we propose MSE, a meta-search engine for document retrieval utilizing social information of the user. In this approach, each user is assumed to have a profile containing his fields of interest. MSE extracts main phrases from the title and short description of receiving results from underlying search engines. Then it clusters the main phrases by a Self-Organizing Map neural network. Generated clusters are then ranked on the basis of the user’s field of interest. We have compared the proposed MSE against two other meta-search engines. The experimental results show the efficiency and effectiveness of the proposed method.

In information retrieval, data fusion (also known as meta-search) has been investigated by many researchers. Previous investigation and experimentation demonstrate that the linear combination method is an effective data fusion method for combining multiple information retrieval results. One advantage is its flexibility, since different weights can be assigned to different component systems so as to obtain better fusion results. The key issue is how to assign good weights to all the component retrieval systems involved. Surprisingly, research in this field is limited and it is still an open question. In this paper, we use the multiple linear regression technique with estimated relevance scores and judged scores to obtain suitable weights. Although the multiple linear regression technique is not new, the way of using it in this paper has never been attempted before for the data fusion problem in information retrieval. Our experiments with five groups of runs submitted to TREC show that the linear combination method with such a weighting strategy steadily outperforms the best component system and other data fusion methods including CombSum, CombMNZ, PosFuse, MAPFuse, SegFuse, and the linear combination method with performance level/performance square weighting schemes by large margins.

Faced with the magnanimous information, users have to use search engine. This article firstly introduces the concept, current situation and process of natural language processing (NLP) and the effect of natural language processing in search engine. Then, in terms of concepts and classifications, traditional search engine has many deficiencies that can not completely satisfy the requirements of users. The key technology of intelligent search engine and the development trend of the combination of natural language processing and search engine technology in the future are discussed.

When a query is passed to multiple search engines, each search engine returns a ranked list of documents. Researchers have demonstrated that combining results, in the form of a ''metasearch engine'', produces a significant improvement in coverage and search effectiveness. This paper proposes a linear programming mathematical model for optimizing the ranked list result of a given group of Web search engines for an issued query. An application with a numerical illustration shows the advantages of the proposed method.

Incorporating further information into the ordered weighted averaging (OWA) operator weights is investigated in this paper. We first prove that for a constant orness the minimax disparity model [13] has unique optimal solution while the modified minimax disparity model [16] has alternative optimal OWA weights. Multiple optimal solutions in modified minimax disparity model provide us opportunity to define a parametric aggregation OWA which gives flexibility to decision makers in the process of aggregation and selecting the best alternative. Finally, the usefulness of the proposed parametric aggregation method is illustrated with an application in metasearch engine.

This paper presents CONQUIRO a cluster based information retrieval engine. The main task of CONQUIRO is to organize documents in groups/clusters relevant to the request or query. The main purpose of CONQUIRO is to help to manage information in an efficient manner. CONQUIRO uses Machine learning algorithms (Clustering methods) as underlying technology. It has been equipped with hierarchical and non-hierarchical clustering algorithms both using Euclidean and cosine similarity as distance measures. Authors believe that CONQUIRO represents a solution to the problem of information management since CONQUIRO goes beyond just a ranked list of documents (Google like).

Real time search is an increasingly important area of information seeking on the Web. In this research, we analyze 1,005,296 user interactions with a real time search engine over a 190day period. Using query log analysis, we investigate searching behavior, categorize search topics, and measure the economic value of this real time search stream. We examine aggregate usage of the search engine, including number of users, queries, and terms. We then classify queries into subject categories using the Google Directory topical hierarchy. We next estimate the economic value of the real time search traffic using the Google AdWords keyword advertising platform. Results shows that 30% of the queries were unique (used only once in the entire dataset), which is low compared to traditional Web searching. Also, 60% of the search traffic comes from the search engine's application program interface, indicating that real time search is heavily leveraged by other applications. There are many repeated queries over time via these application program interfaces, perhaps indicating both long term interest in a topic and the polling nature of real time queries. Concerning search topics, the most used terms dealt with technology, entertainment, and politics, reflecting both the temporal nature of the queries and, perhaps, an early adopter user-based. However, 36% of the queries indicate some geographical affinity, pointing to a location-based aspect to real time search. In terms of economic value, we calculate this real time search stream to be worth approximately US $33,000,000 (US $33M) on the online advertising market at the time of the study. We discuss the implications for search engines and content providers as real time content increasingly enters the main stream as an information source.

Ranking aggregation is a task of combining multiple ranking lists given by several experts or simple rankers to get a hopefully better ranking. It is applicable in several fields such as meta search and collaborative filtering. Most of the existing work is under an unsupervised framework. In these methods, the performances are usually limited especially in unreliable case since labeled information is not involved in. In this paper, we propose a semi-supervised ranking aggregation method, in which preference constraints of several item pairs are given. In our method, the aggregation function is learned based on the ordering agreement of different rankers. The ranking scores assigned by this ranking function on the labeled data should be consistent with the given pairwise order constraints while the ranking scores on the unlabeled data obey the intrinsic manifold structure of the rank items. The experimental results on toy data and the OHSUMED data are presented to illustrate the validity of our method.

Given the growing importance of search in online travel planning, marketers need to better understand the behavioural aspect of search engines use. Built upon a number of previous studies, the goal of this research is to identify patterns in online travel queries across tourist destinations. Utilizing transaction log files from a number of search engines, the analysis shows important patterns in the way travel queries are constructed as well as the commonalities and differences in travel queries about different cities in the United States. The ratio of travel queries among all queries about a specific city seems to associate with the “touristic” level of that city. Also, keywords in travelers' queries reflect their knowledge about the city and its competitors. This paper offers insights into the way tourism destinations are searched online as well as implications for search engine marketing for destinations.

Nowadays, mashup services and especially metasearch engines play an increasingly important role on the Web. Most of users use them directly or indirectly to access and aggregate information from more than one data sources. Similarly to the rest of the search systems, the effectiveness of a metasearch engine is mainly determined by the quality of the results it returns in response to user queries. Since these services do not maintain their own document index, they exploit multiple search engines using a rank aggregation method in order to classify the collected results. However, the rank aggregation methods which have been proposed until now, utilize a very limited set of parameters regarding these results, such as the total number of the exploited resources and the rankings they receive from each individual resource. In this paper we present QuadRank, a new rank aggregation method, which takes into consideration additional information regarding the query terms, the collected results and the data correlated to each of these results (title, textual snippet, URL, individual ranking and others). We have implemented and tested QuadRank in a real-world metasearch engine, QuadSearch, a system developed as a testbed for algorithms related to the wide problem of metasearching. The name QuadSearch is related to the current number of the exploited engines (four). We have exhaustively tested QuadRank for both effectiveness and efficiency in the real-world search environment of QuadSearch and also, using a task from the recent TREC-2009 conference. The results we present in our experiments reveal that in most cases QuadRank outperformed all component engines, another metasearch engine (Dogpile) and two successful rank aggregation methods, Borda Count and the Outranking Approach.

The proliferation of advanced, online, three-dimensional mapping programs provides Web users with detailed aerial and satellite maps, high-powered graphics and animation, and customization capabilities. This mapping revolution can transform the landscape of the Web into a geographic Web, where information can be organized geographically, so that users can understand information in the proper context. In this paper, we describe a geographic information retrieval system that is able to search for location-specific information in Singapore-based Web sites. The user is able to view their search locations on a satellite map instead of the two-dimensional maps currently used in street directories. The Web-based search engine is able to search for locations based on area names, building names, and groups of landmark types, business names, and business categories. Furthermore, the user is also able to use their current coordinates as a parameter so that the search engine is able to return results in order of the distance from the user's current location.

To combat information overload, systems that are often referred to as information customization systems are needed. Such systems act on the user's behalf and can rely on existing information services like search engines that do the resource-intensive part of the work. These systems will be sufficiently lightweight to run on an average PC and serve as personal assistants. Since such an assistant has relatively modest resource requirements it can reside on an individual user's machine. If the assistant resides on the user's machine, there is no need to turn down intelligence. The system can have substantial local intelligence. In this paper, we propose an information customization system that combines meta-search and unsupervised learning. A meta-search engine simultaneously searches multiple search engines and returns a single list of results. The results retrieved by this engine can be highly relevant, since it is usually grabbing the first items from the relevancy-ranked list of hits returned by the individual search engines. The Kohonen Feature Map is then used to construct a self-organizing semantic map such that documents of similar contents are placed close to one another.

In this paper, we present a system LESSON for lecture notes searching and sharing, which is dedicated to both instructors and students for effectively supporting their Web-based teaching and learning activities. The LESSON system employs a metasearch engine for lecture notes searching from Web and a peer-to-peer (P2P) overlay network for lecture notes sharing among the users. A metasearch engine provides an unified access to multiple existing component search engines and has better performance than general-purpose search engines. With the help of a P2P overlay network, all computers used by instructors and students can be connected into a virtual society over the Internet and communicate directly with each other for lecture notes sharing, without any centralized server and manipulation. In order to merge results from multiple component search engines into a single ranked list, we design the RSF strategy that takes rank, similarity and features of lecture notes into account. To implement query routing decision for effectively supporting lecture notes sharing, we propose a novel query routing mechanism. Experimental results indicate that the LESSON system has better performance in lecture notes searching from Web than some popular general-purpose search engines and some existing metasearch schemes; while processing queries within the system, it outperforms some typical routing methods. Concretely, it can achieve relatively high query hit rate with low bandwidth consumption in different types of network topologies.

The Internet is one of the most important sources of knowledge in the present time. It offers a huge volume of information which grows dramatically every day. Web search engines (e.g. Google, Yahoo...) are widely used to find specific data among that information. However, these useful tools also represent a privacy threat for the users: the web search engines profile them by storing and analyzing all the searches that they have previously submitted. To address this privacy threat, current solutions propose new mechanisms that introduce a high cost in terms of computation and communication. In this paper, we propose a new scheme designed to protect the privacy of the users from a web search engine that tries to profile them. Our system uses social networks to provide a distorted user profile to the web search engine. The proposed protocol submits standard queries to the web search engine; thus it does not require any change in the server side. In addition to that, this scheme does not require the server to collaborate with the users. Our protocol improves the existing solutions in terms of query delay. Besides, the distorted profiles still allow the users to get a proper service from the web search engines.

This paper proposes a method for reducing the number of search nodes involved in the solution of queries arriving to a Web search engine. The method is applied by the query receptionist machine during situations of sudden peaks in query trafic to reduce the load on the search nodes. The experimental evaluation based on actual traces from users of a major search engine, shows that the proposed method outperforms alternative strategies. This is more evident for systems composed of a large number of search nodes which indicates that the method is also more scalable than the alternative strategies.

The total number of popular search engines has decreased over time from its peak of the late 1990s. However, when combining the three remaining major ones (Yahoo!, Google, MS Live) with large repositories of information (e.g. BBC.com, NYT.com, Gazeta.pl, etc.), the total number of important information sources can be seen as slowly increasing. Focusing on utilization of search engines only, it is easy to observe that the same query issued to each one of them results in different ''suggestions.'' The question thus arrives, is it possible (and worthy) to combine responses obtained from each one of them into a single answer set. In this paper, we look into three approaches of achieving this goal, which are based on: Game theory, Auction and Consensus methods, while our focus is to study (and compare) their ''performance.''

This paper reports results from a study exploring the multimedia search functionality of Chinese language search engines. Web searching in Chinese (Mandarin) is a growing research area and a technical challenge for popular commercial Web search engines. Few studies have been conducted on Chinese language search engines. We investigate two research questions: which Chinese language search engines provide multimedia searching, and what multimedia search functionalities are available in Chinese language Web search engines. Specifically, we examine each Web search engine's (1) features permitting Chinese language multimedia searches, (2) extent of search personalization and user control of multimedia search variables, and (3) the relationships between Web search engines and their features in the Chinese context. Key findings show that Chinese language Web search engines offer limited multimedia search functionality, and general search engines provide a wider range of features than specialized multimedia search engines. Study results have implications for Chinese Web users, Website designers and Web search engine developers.

Facing the huge amount of information available on the world wide web, people suffer from spending much time and effort to examine the results provided by a search engine. This is mainly because of searching without considering the users' preference and history of uses. Although some search engines have been developed and are currently used, without information regarding users' desire and profile, the search domain remains too large to be effective. This paper proposes a search method that operates on a database of e-documents with references of a common body of knowledge and the retrieval information of the users. By employing data mining and optimization techniques, a framework for an information retrieval system that can effectively search content-related documents is proposed. The concept with the proposed algorithm is described with an illustrative algorithm.

While small-scale search engines in specific domains and languages are increasingly used by Web users, most existing search engine development tools do not support the development of search engines in languages other than English, cannot be integrated with other applications, or rely on proprietary software. A tool that supports search engine creation in multiple languages is thus highly desired. To study the research issues involved, we review related literature and suggest the criteria for an ideal search tool. We present the design of a toolkit, called SpidersRUs, developed for multilingual search engine creation. The design and implementation of the tool, consisting of a Spider module, an Indexer module, an Index Structure, a Search module, and a Graphical User Interface module, are discussed in detail. A sample user session and a case study on using the tool to develop a medical search engine in Chinese are also presented. The technical issues involved and the lessons learned in the project are then discussed. This study demonstrates that the proposed architecture is feasible in developing search engines easily in different languages such as Chinese, Spanish, Japanese, and Arabic.

In this paper, an e-commerce site recommendation system that integrates multiple e-commerce sites is proposed. This system provides the users with a unified portal through which the users can search individual suppliers' product categories efficiently. The core part of the system is an intelligent product meta-search engine that has the following functions: (1) it provides category-based query, with which a buyer can describe his or her product search intention using superclass/subclass relationship, (2) by using WordNet, the buyer's query is semantically extended in order to increase product search accuracy, and (3) the meta-search engine decides a recommended priority of the suppliers by matching the buyer's query with the suppliers' product categories and computing a semantic relevancy measure. Experiments show that the performance of the meta-search is better than those of general keyword-based search and category-based search.

Search engines play a significant role in improving the effectiveness with which internet users retrieve information. There are various search engines available on the internet with Google and Bing being among the most popular ones. The study, therefore, sought to compare the effectiveness of Google and Bing search engines in an effort to determine the one among them that is more preferred by users. Both qualitative and quantitative aspects were considered in the study where the quantitative methods revealed the statistics while qualitative technique relied on the feedback from users. The findings indicated that Google is preferred in as far as navigational and informational queries are concerned hence implying that customers have more confidence in Google search engine compared to Bing. Google search engines tend to give better precision in cases where simple few words’ queries are involved. Bing on the other hand could be relied upon for better precision in cases where complex multi-word queries are involved.

The Internet has become the primary source of information for a large number of consumers. Thus, search engines as the mediators between consumers and online information is an increasingly important topic of research. The present study contributes specifically to the research in consumer search and decision-making by investigating how a search engine's support for topical organization of search results can help improve search performance. Search term formulation is an integral part of the online search process that consumers conduct. Past research shows that the actual search terms used in search queries do matter for improving search performance and also that users often mis-specify search terms, thereby reducing the efficacy of their online search and decision-making. The results of our experimental study offer evidence that topical organization using clustering support, which is provided by some search engines, helps increase relevance and usefulness of search results. The strongest performance improvements were achieved when clustering support was used in combination with terms that were under-specified.

Purpose Retrieving sufficient relevant information online is difficult for many people because they use too few keywords to search and search engines do not provide many support tools. To further complicate the search, users often ignore support tools when available. Our goal is to evaluate in a realistic setting when users use support tools and how they perceive these tools. Methods We compared three medical search engines with support tools that require more or less effort from users to form a query and evaluate results. We carried out an end user study with 23 users who were asked to find information, i.e., subtopics and supporting abstracts, for a given theme. We used a balanced within-subjects design and report on the effectiveness, efficiency and usability of the support tools from the end user perspective. Conclusions We found significant differences in efficiency but did not find significant differences in effectiveness between the three search engines. Dynamic user support tools requiring less effort led to higher efficiency. Fewer searches were needed and more documents were found per search when both query reformulation and result review tools dynamically adjust to the user query. The query reformulation tool that provided a long list of keywords, dynamically adjusted to the user query, was used most often and led to more subtopics. As hypothesized, the dynamic result review tools were used more often and led to more subtopics than static ones. These results were corroborated by the usability questionnaires, which showed that support tools that dynamically optimize output were preferred.

This paper addresses the problem of how to rank retrieval systems without the need for human relevance judgments, which are very resource intensive to obtain. Using TREC 3, 6, 7 and 8 data, it is shown how the overlap structure between the search results of multiple systems can be used to infer relative performance differences. In particular, the overlap structures for random groupings of five systems are computed, so that each system is selected an equal number of times. It is shown that the average percentage of a system's documents that are only found by it and no other systems is strongly and negatively correlated with its retrieval performance effectiveness, such as its mean average precision or precision at 1000. The presented method uses the degree of consensus or agreement a retrieval system can generate to infer its quality. This paper also addresses the question of how many documents in a ranked list need to be examined to be able to rank the systems. It is shown that the overlap structure of the top 50 documents can be used to rank the systems, often producing the best results. The presented method significantly improves upon previous attempts to rank retrieval systems without the need for human relevance judgments. This ''structure of overlap'' method can be of value to communities that need to identify the best experts or rank them, but do not have the resources to evaluate the experts' recommendations, since it does not require knowledge about the domain being searched or the information being requested.

The Authority and Ranking Effects play a key role in data fusion. The former refers to the fact that the potential relevance of a document increases exponentially as the number of systems retrieving it increases and the latter to the phenomena that documents higher up in ranked lists and found by more systems are more likely to be relevant. Data fusion methods commonly use all the documents returned by the different retrieval systems being compared. Yet, as documents further down in the result lists are considered, a document's probability of being relevant decreases significantly and a major source of noise is introduced. This paper presents a systematic examination of the Authority and Ranking Effects as the number of documents in the result lists, called the list depth, is varied. Using TREC 3, 7, 8, 12 and 13 data, it is shown that the Authority and Ranking Effects are present at all list depths. However, if the systems in the same TREC track retrieve a large number of relevant documents, then the Ranking Effect only begins to emerge as more systems have found the same document and/or the list depth increases. It is also shown that the Authority and Ranking Effects are not an artifact of how the TREC test collections have been constructed.

Now no web search engine can cover more than 60 percent of all the pages on Internet. The update interval of most pages database is almost one month. This condition hasn't changed for many years. Converge and recency problems have become the bottleneck problem of current web search engine. To solve these problems, a new system, search engine based on DNS is proposed in this paper. This system adopts the hierarchical distributed architecture like DNS, which is different from any current commercial search engine. In theory, this system can cover all the web pages on Internet. Its update interval could even be one day. The original idea, detailed content and implementation of this system all are introduced in this paper., 9 pages,2 figures

Despite the effectiveness of search engines, the persistently increasing amount of web data continuously obscures the search task. Efforts have thus concentrated on personalized search that takes account of user preferences. A new concept is introduced towards this direction; search based on ranking of local set of categories that comprise a user search profile. New algorithms are presented that utilize web page categories to personalize search results. Series of user-based experiments show that the proposed solutions are efficient. Finally, we extend the application of our techniques in the design of topic-focused crawlers, which can be considered an alternative personalized search.