Traditional Search Engines Research Articles

Introducing Web 2.0: social search for health librarians

Although social searching is not a new phenomenon, its features of collaboration, conversation, and interac- tion in a social space among users make it an imperative element of Web 2.0 technologies. The emerging popularity of folksonomies, with users generating their own labeling system, has allowed social searching to distinguish itself from traditional search engines such as Google and Yahoo. This paper discusses a number of social search tools, including Google Custom Search, del.icio.us, YouTube, and Flickr, tools that the authors find useful for the practice of a health information professional.

Concept-based search and questionnaire systems

World Wide Web search engines including Google, Yahoo and MSN have become the most heavily-used online services (including the targeted advertising), with millions of searches performed each day on unstructured sites. In this presentation, we would like to go beyond the traditional web search engines that are based on keyword search and the Semantic Web which provides a common framework that allows data to be shared and reused across application. For this reason, our view is that "Before one can use the power of web search the relevant information has to be mined through the concept-based search mechanism and logical reasoning with capability to Q&A representation rather than simple keyword search". In this paper, we will first present the state of the search engines. Then we will focus on development of a framework for reasoning and deduction in the web. A new web search model will be presented. One of the main core ideas that we will use to extend our technique is to change terms-documents-concepts (TDC) matrix into a rule-based and graph-based representation. This will allow us to evolve the traditional search engine (keyword-based search) into a concept-based search and then into Q&A model. Given TDC, we will transform each document into a rule-based model including it's equivalent graph model. Once the TDC matrix has been transformed into maximally compact concept based on graph representation and rules based on possibilistic relational universal fuzzy-type II (pertaining to composition), one can use Z(n)-compact algorithm and transform the TDC into a decision-tree and hierarchical graph that will represents a Q&A model. Finally, the concept of semantic equivalence and semantic entailment based on possibilistic relational universal fuzzy will be used as a basis for question-answering (Q&A) and inference from fuzzy premises. This will provide a foundation for approximate reasoning, language for representation of imprecise knowledge, a meaning representation language for natural languages, precisiation of fuzzy propositions expressed in a natural language, and as a tool for Precisiated Natural Language (PNL) and precisation of meaning. The maximally compact documents based on Z(n)-compact algorithm and possibilistic relational universal fuzzy-type II will be used to cluster the documents based on concept-based query-based search criteria.

An Intelligent Information Retrieval System using Automatic Word Sense Disambiguation

This paper aims to establish that an intelligent contextual infonnation retrieval (IR) system can improve the quality of search results by retrieving more relevant results than those obtained with traditional search engines. Search engines capable of implicit, explicit, and no contextual retrieval were designed and implemented and their performances studied. Experimental results showed that search engines with contextual IR produce results that are more relevant, and the outcomes further indicate that there is no perceived gain in choosing specifically any one of the two approaches of implicit or explicit. The performance of the indexing mechanism, as it classifies document tokens with their appropriate contexts/word sense, was evaluated. The effectiveness of the word sense disambiguation process was found to depend to a great extent on the process (implementation) as well as the raw data (thesaurus).

Design and implementation of an intelligent metasearch engine based on agent

It is difficult for users to search for information on the internet because of the rapid expanding of information resource. Traditional search engines cannot deal with that very well. Based on this situation, the paper presented agent and metasearch engine technology. The authors believed that the combination of agent and metasearch engine technology can satisfy the demand for individuation and intelligence while users search information. The architecture of intelligent metasearch engine system based on agent was proposed in this paper. The authors expounded the idea of the design, introduced the work principle of the system, and explained the work mechanism of each module.

Novel peptide identification from tandem mass spectra using ESTs and sequence database compression

Peptide identification by tandem mass spectrometry is the dominant proteomics workflow for protein characterization in complex samples. Traditional search engines, which match peptide sequences with tandem mass spectra to identify the samples' proteins, use protein sequence databases to suggest peptide candidates for consideration. Although the acquisition of tandem mass spectra is not biased toward well-understood protein isoforms, this computational strategy is failing to identify peptides from alternative splicing and coding SNP protein isoforms despite the acquisition of good-quality tandem mass spectra. We propose, instead, that expressed sequence tags (ESTs) be searched. Ordinarily, such a strategy would be computationally infeasible due to the size of EST sequence databases; however, we show that a sophisticated sequence database compression strategy, applied to human ESTs, reduces the sequence database size approximately 35-fold. Once compressed, our EST sequence database is comparable in size to other commonly used protein sequence databases, making routine EST searching feasible. We demonstrate that our EST sequence database enables the discovery of novel peptides in a variety of public data sets.

Architecture of a grid-enabled Web search engine

Search Engine for South-East Europe (SE4SEE) is a socio-cultural search engine running on the grid infrastructure. It offers a personalized, on-demand, country-specific, category-based Web search facility. The main goal of SE4SEE is to attack the page freshness problem by performing the search on the original pages residing on the Web, rather than on the previously fetched copies as done in the traditional search engines. SE4SEE also aims to obtain high download rates in Web crawling by making use of the geographically distributed nature of the grid. In this work, we present the architectural design issues and implementation details of this search engine. We conduct various experiments to illustrate performance results obtained on a grid infrastructure and justify the use of the search strategy employed in SE4SEE.

Finding out: a system for providing rapid and reliable answers to questions in the construction sector

The construction sector is notorious for the dichotomy between its intensive use of information in its decision‐making processes and its limited access to, and insufficient use of, the pertinent information that is potentially available, e.g. on the internet. This paper seeks to examine this issue. To solve this problem (the ‘problem of information aboutinformation’), a multidisciplinary team developed an online question‐answering (Q.‐A.)system that uses natural language for the query and the reply. The system provides a direct answer to questions posed by building industry participants, instead of providing a list of references (as is the case with most online information retrieval systems), much as if onewere asking a question of, and receiving a response from, an expert.It has the capabilitiesto process questions in natural language, to find appropriate fragments of answers indifferent web sites and to condense them into a paragraph, also written in natural language. The main features of the system are that it uses domain‐specific knowledge (in the form ofa hierarchical specialized thesaurus complemented by terms of fieldwork parlance),semantic categorization, a database of filtered and indexed web sites, and an online interface that is adapted to different profiles of actors in the construction sector. The testing process shows that the system goes beyond the lists of references and links provided by traditional search engines on the web.The Q.‐A.system already gives 70% of satisfactory answers. The Q.‐A.system can be applied to other business domains apart from information retrieval and decision‐making in the building sector. It is also possible to apply it to the exploitation of in‐house knowledge management database.

A prototype Semantic Web‐based digital content exchange for schools in Singapore

AbstractSingapore has many large and educationally valuable digital collections and is planning the development of many more. These digital collections contain historical, cultural and scientific multimedia objects, along with learning objects. At present, school teachers and pupils find it hard to locate many of these resources using traditional search engines. This paper describes a research investigation into the design and subsequent development of a prototype digital resource discovery portal (Digital Content Exchange) based on Semantic Web technologies, and explores some of the educational issues raised. The research project explored how the features of the Semantic Web might offer valuable additional educational benefits and affordances beyond that of those based on existing information retrieval technologies.

Dubious hit counts and cuckoo's eggs

PurposeThe purpose of this paper is to discuss dubious hit counts in search results.Design/methodology/approachUses not only traditional databases and search engines but also unique citation databases to gain informative hit figures.FindingsSearches are finding materials that are clearly not scholarly materials.Originality/valueShows the importance of a more defined search, thus less distortion of counts.

A web-page recommender system via a data mining framework and the Semantic Web concept

Traditional search engines require users to form the keyword based query which can accurately depict the search topic. More importantly, search engines are generally unable to customise the results according to the users' preferences. Recently, an alternative approach of retrieving the information, known as the recommender system is proposed. A recommender system is an intermediary program that intelligently generates a list of information which matches the users' preferences. In this paper, a new recommender system framework based on data mining techniques and the Semantic Web concept is proposed. Two information filtering methods for providing the recommended information (i.e., content-based and collaborative filtering) are considered. Both filtering techniques are based on data mining algorithms which provide efficiency in handling large data sets. In addition, the Semantic Web concept, in which the information is given well-defined meaning, is incorporated into the framework in order to provide the users with semantically-enhanced information. To demonstrate the potential use of the proposed framework, a system prototype for recommending the University of Miami's Web pages was implemented to enhance the performance of the traditional query-based information retrieval approach provided on the website.

Personalized mining of web documents using link structures and fuzzy concept networks

Personalized search engines are important tools for finding web documents for specific users, because they are able to provide the location of information on the WWW as accurately as possible, using efficient methods of data mining and knowledge discovery. The types and features of traditional search engines are various, including support for different functionality and ranking methods. New search engines that use link structures have produced improved search results which can overcome the limitations of conventional text-based search engines. Going a step further, this paper presents a system that provides users with personalized results derived from a search engine that uses link structures. The fuzzy document retrieval system (constructed from a fuzzy concept network based on the user's profile) personalizes the results yielded from link-based search engines with the preferences of the specific user. A preliminary experiment with six subjects indicates that the developed system is capable of searching not only relevant but also personalized web pages, depending on the preferences of the user.

Information Foraging with a Proximity-Based Browsing Tool

This paper describes a user friendly, powerful information foraging tool. Document sets are presented through combinations of traditional ranked lists and 2-dimensional proximity-based visualisations, created by uniting graph-theoretic clustering and force-directed layout techniques, where article positions are determined by inter-document similarities. By using Gestalt principles and information encoding, the simple layout improves search efficiency by leveraging human cognitive strengths that have generally been under-utilised in commercial GUI development. In this paper, design and realisation of the layout technique are described in the context of an article browsing framework. Results of an indicative comparative laboratory study, which evaluates the client application --- and in particular Graph-Theoretic Force-Directed (GTFD) visualisations against traditional search engine interfaces --- are then presented. This study demonstrates the advantage of graphical presentations when browsing an article collection. Finally, potential improvements identified during the study are discussed, as are future directions for this approach to collection browsing

Combining Argumentation and Web Search Technology: Towards a Qualitative Approach for Ranking Results

Several techniques for improving web search have been developed over the last years. Most existing approaches are still limited, mainly due to the absence of qualitative criteria for ranking results and insensitivity to user preferences for guiding the search. At the same time, defeasible argumentation evolved as a successful approach in AI to model commonsense qualitative reasoning with applications in many areas, such as agent theory, knowledge engineering and legal reasoning. This paper presents ARGUENET, a recommender system that classifies search results according to preference criteria declaratively specified by the user. The proposed approach integrates a traditional web search engine with a defeasible argumentation framework.

A new customized document categorization scheme using rough membership

One of the problems that plague document ranking is the inherent ambiguity, which arises due to the nuances of natural language. Though two documents may contain the same set of words, their relevance may be very different to a single user, since the context of the words usually determines the relevance of a document. Context of a document is very difficult to model mathematically other than through user preferences. Since it is difficult to perceive all possible user interests a priori and install filters for the same at the server side, we propose a rough-set-based document filtering scheme which can be used to build customized filters at the user end. The documents retrieved by a traditional search engine can then be filtered automatically by this agent and the user is not flooded with a lot of irrelevant material. A rough-set-based classificatory analysis is used to learn the user's bias for a category of documents. This is then used to filter out irrelevant documents for the user. To do this we have proposed the use of novel rough membership functions for computing the membership of a document to various categories.

MAIS ­ Un système multi-agents pour la recherche d'information sur le web

In this paper we present a multi-agent system for searching the Internet. The search for interesting documents on the Internet is becoming increasingly difficult, mainly because search engines cannot cope with the expansion of the Internet. Consequently, the results of queries are often of very poor quality. Multi-agent systems can give more accurate results than traditional search engines because agents can be specialized in different tasks (search or filtering) and can personalize information. We implemented such an approach using an open multi-agent system containing personal assistants, library agents, filter agents and search agents. In this paper, we detail the elements of our multi-agent system and compare its performance with more traditional search engines. Our results yield a better accuracy.

Smart Search and Analysis of ASCO Abstracts: The 2003 ASCO Pilot Breast Cancer Information Exchange (BCIE) project

6066 Background: ASCO abstracts are a leading source of cancer research information. The ASCO Breast Cancer Information Exchange (BCIE) project was developed to better organize and access abstracts in breast cancer for a faster, more precise, and targeted search. Methods: Traditional web search engines allow simple keyword-based queries. Improvements in precision and ease of use require software that “knows” the domain. We developed XML/Java models of breast cancer clinical studies, from molecular diagnostics to disease stages. From these models, we developed a study characterization form, included as a voluntary author step in the 2003 Annual Meeting abstract submission process, to capture the study clinical parameters. We also developed a corresponding user search form, publicly available onhttp://webapp.asco.org/bcie. Results: Almost 95% of breast cancer abstract first authors (352) participated, completing the web-based form to characterize their studies from a uniform descriptive variable set. Since ...

Smart Search and Analysis of ASCO Abstracts: The 2003 ASCO Pilot Breast Cancer Information Exchange (BCIE) project

6066 Background: ASCO abstracts are a leading source of cancer research information. The ASCO Breast Cancer Information Exchange (BCIE) project was developed to better organize and access abstracts in breast cancer for a faster, more precise, and targeted search. Methods: Traditional web search engines allow simple keyword-based queries. Improvements in precision and ease of use require software that “knows” the domain. We developed XML/Java models of breast cancer clinical studies, from molecular diagnostics to disease stages. From these models, we developed a study characterization form, included as a voluntary author step in the 2003 Annual Meeting abstract submission process, to capture the study clinical parameters. We also developed a corresponding user search form, publicly available onhttp://webapp.asco.org/bcie. Results: Almost 95% of breast cancer abstract first authors (352) participated, completing the web-based form to characterize their studies from a uniform descriptive variable set. Since 8/02, www.asco.org visitors have used the BCIE search tool to formulate queries. Analysis of a subset of queries (N=691) revealed that 90% selected form-based search variables exclusively or combined with traditional open keyword search terms. On average, 7 search variables were entered per search. The three most selected search variables were study phase, patient sex, and chemotherapeutic agent. In addition, the repository of characterized studies created a detailed index of author-selected consistent-variable descriptors, offering potential for examination of abstract trends within one disease-type, including study types, prognostic factors, therapies, diagnostics, and biomarkers. Heretofore, such analysis has been time consuming, performed manually, and not reflective of authors direct involvement. Conclusions: The BCIE project demonstrates the feasibility of a new technology and process for uniform XML-Java indexing, search, and analysis of published material. The project continues in 2004. Author Disclosure Employment or Leadership Consultant or Advisory Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration Medstory Medstory

Approach in high precision topic-specific resource discovery on the web

The Internet presents numerous sources of useful information nowadays. However, these resources are drowning under the dynamic Web, so accurate finding user-specific information is very difficult. In this paper we discuss a Semantic Graph Web Search (SGWS) algorithm in topic-specific resource discovery on the Web. This method combines the use of hyperlinks, characteristics of Web graph and semantic term weights. We implement the algorithm to find Chinese medical information from the Internet. Our study showed that it has better precision than traditional IR (Information Retrieval) methods and traditional search engines.

SIGIR 2003 workshop on distributed information retrieval

During the last decade companies, governments, and research groups worldwide have directed significant effort towards the creation of sophisticated digital libraries across a variety of disciplines. As digital libraries proliferate, in a variety of media, and from a variety of sources, problems of resource selection and data fusion become major obstacles. Traditional search engines, even very large systems such as Google, are unable to provide access to the “Hidden Web” of information that is only available via digital library search interfaces. Effective, reliable information retrieval also requires the ability to pose multimedia queries across many digital libraries. The answer to a query about the lyrics to a folk song might be text or an audio recording, but few systems today could deliver both data types in response to a single, simple query. Distributed Information Retrieval addresses issues that arise when people have routine access to thousands of multimedia digital libraries.

Personalized information ordering: a case study in online recruitment

Traditional search engine techniques are inadequate when it comes to helping the average user locate relevant information online. The key problem is their inability to recognize and respond to the implicit preferences of a user that are typically unstated in a search query. In this paper we describe CASPER, an online recruitment search engine, which attempts to address this issue by extending traditional search techniques with a personalization technique that is capable of taking account of user preferences as a means of classifying retrieved results as relevant or irrelevant. We evaluate a number of different classification strategies with respect to their accuracy and noise tolerance. Furthermore we argue that because CASPER transfers its personalization process to the client-side it offers significant efficiency and privacy advantages over more traditional server-side approaches.