Review of Information Retrieval models (original) (raw)

Information Retrieval on the web and its evaluation

Internet is one of the main sources of information for millions of people. One can find information related to practically all matters on internet. Moreover if we want to retrieve information about some particular topic we may find thousands of Web Pages related to that topic. But our main concern is to find relevant Web Pages from among that collection. So in this paper I have discussed that how information is retrieved from the web and the efforts required for retrieving this information in terms of system and users efforts.

EVALUATION OF INFORMATION RETRIEVAL SYSTEMS USING VARIOUS MEASURES

Our world revolves around technology and information. From a computer system present on desk to smart phone carried everywhere, the use of technology to aid human life has increased enormously. This leads to the production of massive amount of data; be it files belonging to an organization or a person's heartbeat rate. All data is stored. The main challenge is to retrieve information out of it. Additionally, a user specific information retrieval is also needed. Information Retrieval Systems is one of the most used applications in today's life, ranging from search engine searching for a given query to intelligently analyzing and retrieving accurate details of a particular disease. Along with predefined retrieval items, a user can give a new query to the system and relevant information will be retrieved. Since, the usage is wide; the need for evaluating such systems becomes a priority. Federated search is an information retrieval technology that allows the simultaneous search of multiple searchable resources and aggregates the results that are received from the search engines for presentation to the user. It has data for numerous queries and search engines. In this paper, various applications of Information Retrieval Systems are discussed, followed by different approaches used for the evaluation. The dataset used is Federated Web Search track TREC 2014 of FedWeb Greatest Hits collection which allows combining results of multiple search engines. The methods used for evaluation along with the results are provided.

Improving the Effectiveness of Information Retrieval System

American Scientific Research Journal for Engineering, Technology, and Sciences, 2016

With the rapid growth of information and easy access of information, in particular the boom of the World Wide Web, the problem of finding useful information and knowledge becomes one of the most important topics in information and computer science. Information Retrieval (IR) systems, also called text retrieval systems, facilitate users to retrieve information which is relevant or close to their information needs. This research provides an effective IR system for retrieving not only relevant but also related documents. For retrieving relevant documents, Probabilistic Model is applied. For retrieving related documents, the related indexed table is built including extracted keywords and related documents lists. In constructing related index table in the database, Shannon’s entropy difference between intrinsic and extrinsic mode is used to extract the highly significant keywords. Entropy threshold value was assigned to 0.5 of normalized entropy difference square ( ) according to the an...

Information retrieval: an overview of system characteristics

International Journal of Medical Informatics, 1997

The paper gives an overview of characteristics of information retrieval (IR) systems. The characteristics are identified from the descriptions of 23 IR systems. Four IR models are discussed: the Boolean model, the vector model, the probabilistic model and the connectionistic model. Twelve other characteristics of IR models are identified: search intermediary, domain knowledge, relevance feedback, natural language interface, graphical query language, conceptual queries, full-text IR, field searching, fuzzy queries, hyptertext integration, machine learning, and ranked output. Finally, the relevance of IR systems for the World Wide Web is established. © 1997 Elsevier Science B.V.

Information Retrieval on the World Wide Web

IEEE Internet Computing, 1997

T he World Wide Web is a very large distributed digital information space. From its origins in 1991 as an organization-wide collaborative environment at CERN for sharing research documents in nuclear physics, the Web has grown to encompass diverse information resources: personal home pages; online digital libraries; virtual museums; product and service catalogs; government information for public dissemination; research publications; and Gopher, FTP, Usenet news, and mail servers. Some estimates suggest that the Web currently includes about 150 million pages and that this number doubles every four months.

A survey in traditional information retrieval models

2008 2nd IEEE International Conference on Digital Ecosystems and Technologies, 2008

As a matter of fact, many so-called semantic search algorithms are derived from the traditional indexterm-based search models. In this paper, we survey the traditional information retrieval models by categorizing them into three main classes and eleven subclasses, and analyse their benefits and issues of them.

A Comparison on Intelligent Web Information Retrieval Systems

The key technology for accessing relevant data from large volume is Information retrieval. Information retrieval technology gives assurance to access large data. The major challenge of information retrieval is to find and manage all existing information in the web. So it became the elementary skill behind web search tool. Knowing the relevant information at the time of requirement is important for people. They considered information as one of the most valuable and strategic goods. But the availability of information nowadays increases tremendously, so this cause information oversupplies and results in time-consumption and difficulty in accessing relevant. Aimed to overcome these difficulties in the beginning itself several automated tools are used for searching information relevant to the user needs. The responsibility of IR is to collect and represent the information and allows retrieving the relevant information to exact problems at real time through wired or wireless devices. The intention to find as much possible as additional background information will help an information retrieval system to improve the retrieval accuracy. This scenario requires new advanced tools, which covering in a better way the various phases of the information streams and capable of surviving with the severe limitations of existing tools for information retrieval on the web. So the main intention of this research is to finding out the techniques which can improve the effectiveness of information retrieval.

WEB BASED INFORMATION RETRIEVAL

Final year Project that evaluates retrieval methods from internet content describes the software development cycle and methodologies. It goes through Google algorithms and techniques. Finally, it demonstrates a set of tools (created as part pf the final year project in Java) for retrieval and ranking information using neural networks.

A Survey on Performance Evaluation Measures for Information Retrieval System

2015

information to the users. To make the search effective, a tool called search engine has been introduced. These engines crawl the web for the given users query and display the results to the user based on the relevance score (ranking). Different search engine employs different ranking algorithm. Many ranking algorithm is being introduced frequently by several researchers. Several metrics are available to assess the quality of the ranked web pages. This paper presents a survey on different evaluation measures that are available for information retrieval systems and search engines. Several illustrations are provided for all these metrics.