scholarly journals Semantic Sense Annotation from User Query by using Web Search Techniques

2018 ◽  
Vol 0 (0) ◽  
pp. 156005
Author(s):  
Sunita Mahajan ◽  
Dr. Vijay Rana
Keyword(s):  
Web Search ◽  
Search Techniques ◽  
User Query

Application of Genetic Algorithm and Back Propagation Neural Network for Effective Personalize Web Search-Based on Clustered Query Sessions

2016 ◽  
Vol 7 (1) ◽  
pp. 33-49 ◽  
Author(s):  
Suruchi Chawla
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Web Search ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Information Need ◽  
Data Set ◽  
User Query ◽  
User Queries

In this paper novel method is proposed using hybrid of Genetic Algorithm (GA) and Back Propagation (BP) Artificial Neural Network (ANN) for learning of classification of user queries to cluster for effective Personalized Web Search. The GA- BP ANN has been trained offline for classification of input queries and user query session profiles to a specific cluster based on clustered web query sessions. Thus during online web search, trained GA –BP ANN is used for classification of new user queries to a cluster and the selected cluster is used for web page recommendations. This process of classification and recommendations continues till search is effectively personalized to the information need of the user. Experiment was conducted on the data set of web user query sessions to evaluate the effectiveness of Personalized Web Search using GA optimized BP ANN and the results confirm the improvement in the precision of search results.

An Intelligent Web Search Using Multi-Document Summarization

2016 ◽  
Vol 6 (2) ◽  
pp. 41-65 ◽  
Author(s):  
Sheetal A. Takale ◽  
Prakash J. Kulkarni ◽  
Sahil K. Shah
Keyword(s):  
Search Engine ◽  
Web Search ◽  
Document Clustering ◽  
The Internet ◽  
Web Pages ◽  
Extractive Summarization ◽  
Text Understanding ◽  
User Query ◽  
Sentence Clustering

Information available on the internet is huge, diverse and dynamic. Current Search Engine is doing the task of intelligent help to the users of the internet. For a query, it provides a listing of best matching or relevant web pages. However, information for the query is often spread across multiple pages which are returned by the search engine. This degrades the quality of search results. So, the search engines are drowning in information, but starving for knowledge. Here, we present a query focused extractive summarization of search engine results. We propose a two level summarization process: identification of relevant theme clusters, and selection of top ranking sentences to form summarized result for user query. A new approach to semantic similarity computation using semantic roles and semantic meaning is proposed. Document clustering is effectively achieved by application of MDL principle and sentence clustering and ranking is done by using SNMF. Experiments conducted demonstrate the effectiveness of system in semantic text understanding, document clustering and summarization.

COMPARATIVE ANALYSIS OF IMAGE RETRIEVAL TECHNIQUES IN CYBERSPACE

2020 ◽  
Vol 8 (1) ◽  
pp. 01-10
Author(s):  
Rajeev Gupta ◽  
Virender Singh
Keyword(s):  
Comparative Analysis ◽  
Image Retrieval ◽  
Search Engines ◽  
Web Search ◽  
Content Based Image Retrieval ◽  
Retrieval Time ◽  
User Query ◽  
Web Search Engines ◽  
Research Challenge ◽  
The Web

Purpose: With the popularity and remarkable usage of digital images in various domains, the existing image retrieval techniques need to be enhanced. The content-based image retrieval is playing a vital role to retrieve the requested data from the database available in cyberspace. CBIR from cyberspace is a popular and interesting research area nowadays for a better outcome. The searching and downloading of the requested images accurately based on meta-data from the cyberspace by using CBIR techniques is a challenging task. The purpose of this study is to explore the various image retrieval techniques for retrieving the data available in cyberspace.  Methodology: Whenever a user wishes to retrieve an image from the web, using present search engines, a bunch of images is retrieved based on a user query. But, most of the resultant images are unrelated to the user query. Here, the user puts their text-based query in the web-based search engine and compute the related images and retrieval time. Main Findings:  This study compares the accuracy and retrieval-time of the requested image. After the detailed analysis, the main finding is none of the used web-search engines viz. Flickr, Pixabay, Shutterstock, Bing, Everypixel, retrieved the accurate related images based on the entered query.   Implications: This study is discussing and performs a comparative analysis of various content-based image retrieval techniques from cyberspace. Novelty of Study: Research community has been making efforts towards efficient retrieval of useful images from the web but this problem has not been solved and it still prevails as an open research challenge. This study makes some efforts to resolve this research challenge and perform a comparative analysis of the outcome of various web-search engines.

Task-based support in search engines

2020 ◽  
Vol 54 (1) ◽  
pp. 1-2
Author(s):  
Darío Garigliotti
Keyword(s):  
Search Engines ◽  
Web Search ◽  
Information Access ◽  
Semantic Search ◽  
Systematic Evaluation ◽  
Task Completion ◽  
Information Need ◽  
User Query ◽  
Entity Retrieval ◽  
Knowledge Intensive

Web search has become a key technology on which people rely daily for getting information about almost everything. The evolution of the search experience has also shaped the expectations of people about it. Many users seem to expect today's web search engines to behave like a kind of "wise interpreter," capable of understanding the meaning behind a search query, realizing its current context, and responding to it directly and appropriately. Search by meaning, or semantic search, encompasses a large portion of information retrieval (IR) research devoted to study more meaningful representations of the information need expressed by the user query. Entity cards, direct displays, and verticals are examples of how major commercial search engines have indeed responded to user expectations, capitalizing on query understanding. Search is usually performed with a specific goal underlying the query. In many cases, this goal consists of a nontrivial task to be completed. Current search engines support a small set of basic tasks, and most of the knowledge-intensive workload for supporting more complex tasks is left to the user. Task-based search can be viewed as an information access paradigm that aims to enhance search engines with functionalities for recognizing the underlying tasks in searches and providing support for task completion. The research presented in this thesis focuses on utilizing and extending methods and techniques from semantic search in the next stage of the evolution of search engines, namely, to support users in achieving their tasks. Our work can be grouped in three grand themes: (1) Entity type information for entity retrieval : we conduct a systematic evaluation and analysis of methods for type-aware entity retrieval, in terms of three main dimensions. Also, we revisit the problem of hierarchical target type identification, present a state-of-the-art supervised learning method, and analyze the usage of automatically identified target entity types for type-aware entity retrieval; (2) Entity-oriented search intents : we propose a categorization scheme for entity-oriented search intents, and study the distributions of entity intent categories per entity type. We further develop a method for constructing a knowledge base of entity-oriented search intents; and (3) Task-based search : we design a probabilistic generative framework for task-based query suggestion, and principledly estimate each of its components. Furthermore, we introduce the problems of query-based task recommendation and mission-based task recommendation, and establish respective methods as suitable baselines.

scholarly journals Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 156
Author(s):  
Abdullah Bin Shams ◽  
Ehsanul Hoque Apu ◽  
Ashiqur Rahman ◽  
Md. Mohsin Sarker Raihan ◽  
Nazeeba Siddika ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Real Time ◽  
Search Engine ◽  
Language Processing ◽  
Web Search ◽  
Training Data ◽  
Small Data ◽  
Web Search Engine ◽  
User Query

Misinformation such as on coronavirus disease 2019 (COVID-19) drugs, vaccination or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities have deployed several surveillance tools to detect and slow down the rapid misinformation spread online. Large quantities of unverified information are available online and at present there is no real-time tool available to alert a user about false information during online health inquiries over a web search engine. To bridge this gap, we propose a web search engine misinformation notifier extension (SEMiNExt). Natural language processing (NLP) and machine learning algorithm have been successfully integrated into the extension. This enables SEMiNExt to read the user query from the search bar, classify the veracity of the query and notify the authenticity of the query to the user, all in real-time to prevent the spread of misinformation. Our results show that SEMiNExt under artificial neural network (ANN) works best with an accuracy of 93%, F1-score of 92%, precision of 92% and a recall of 93% when 80% of the data is trained. Moreover, ANN is able to predict with a very high accuracy even for a small training data size. This is very important for an early detection of new misinformation from a small data sample available online that can significantly reduce the spread of misinformation and maximize public health safety. The SEMiNExt approach has introduced the possibility to improve online health management system by showing misinformation notifications in real-time, enabling safer web-based searching on health-related issues.

scholarly journals Improved PageRank Algorithm for Web Structure Mining

2013 ◽  
Vol 10 (9) ◽  
pp. 1969-1976
Author(s):  
Sathya Bama ◽  
M.S.Irfan Ahmed ◽  
A. Saravanan
Keyword(s):  
Search Engines ◽  
Web Mining ◽  
Web Search ◽  
Relevant Information ◽  
Research Area ◽  
Pagerank Algorithm ◽  
Web Structure ◽  
User Query ◽  
Web Structure Mining ◽  
The Web

The growth of internet is increasing continuously by which the need for improving the quality of services has been increased. Web mining is a research area which applies data mining techniques to address all this need. With billions of pages on the web it is very intricate task for the search engines to provide the relevant information to the users. Web structure mining plays a vital role by ranking the web pages based on user query which is the most essential attempt of the web search engines. PageRank, Weighted PageRank and HITS are the commonly used algorithm in web structure mining for ranking the web page. But all these algorithms treat all links equally when distributing initial rank scores. In this paper, an improved page rank algorithm is introduced. The result shows that the algorithm has better performance over PageRank algorithm.

Query Rewriting Using Monolingual Statistical Machine Translation

2010 ◽  
Vol 36 (3) ◽  
pp. 569-582 ◽  
Author(s):  
Stefan Riezler ◽  
Yi Liu
Keyword(s):  
Machine Translation ◽  
Query Expansion ◽  
Web Search ◽  
Query Language ◽  
State Of The Art ◽  
Statistical Machine Translation ◽  
Target Language ◽  
Translation Model ◽  
User Query ◽  
Query Logs

Long queries often suffer from low recall in Web search due to conjunctive term matching. The chances of matching words in relevant documents can be increased by rewriting query terms into new terms with similar statistical properties. We present a comparison of approaches that deploy user query logs to learn rewrites of query terms into terms from the document space. We show that the best results are achieved by adopting the perspective of bridging the “lexical chasm” between queries and documents by translating from a source language of user queries into a target language of Web documents. We train a state-of-the-art statistical machine translation model on query-snippet pairs from user query logs, and extract expansion terms from the query rewrites produced by the monolingual translation system. We show in an extrinsic evaluation in a real-world Web search task that the combination of a query-to-snippet translation model with a query language model achieves improved contextual query expansion compared to a state-of-the-art query expansion model that is trained on the same query log data.

Searching web documents using a summarization approach

2016 ◽  
Vol 12 (1) ◽  
pp. 83-101 ◽  
Author(s):  
Rani Qumsiyeh ◽  
Yiu-Kai Ng
Keyword(s):  
Search Engines ◽  
Web Search ◽  
Specific Information ◽  
Information Need ◽  
Search Query ◽  
Content Type ◽  
Additional Information ◽  
Search Results ◽  
User Query ◽  
Web Search Engines

Purpose The purpose of this paper is to introduce a summarization method to enhance the current web-search approaches by offering a summary of each clustered set of web-search results with contents addressing the same topic, which should allow the user to quickly identify the information covered in the clustered search results. Web search engines, such as Google, Bing and Yahoo!, rank the set of documents S retrieved in response to a user query and represent each document D in S using a title and a snippet, which serves as an abstract of D. Snippets, however, are not as useful as they are designed for, i.e. assisting its users to quickly identify results of interest. These snippets are inadequate in providing distinct information and capture the main contents of the corresponding documents. Moreover, when the intended information need specified in a search query is ambiguous, it is very difficult, if not impossible, for a search engine to identify precisely the set of documents that satisfy the user’s intended request without requiring additional information. Furthermore, a document title is not always a good indicator of the content of the corresponding document either. Design/methodology/approach The authors propose to develop a query-based summarizer, called QSum, in solving the existing problems of Web search engines which use titles and abstracts in capturing the contents of retrieved documents. QSum generates a concise/comprehensive summary for each cluster of documents retrieved in response to a user query, which saves the user’s time and effort in searching for specific information of interest by skipping the step to browse through the retrieved documents one by one. Findings Experimental results show that QSum is effective and efficient in creating a high-quality summary for each cluster to enhance Web search. Originality/value The proposed query-based summarizer, QSum, is unique based on its searching approach. QSum is also a significant contribution to the Web search community, as it handles the ambiguous problem of a search query by creating summaries in response to different interpretations of the search which offer a “road map” to assist users to quickly identify information of interest.

scholarly journals Location based Web Object Search using Probabilistic Classification Model.

2019 ◽  
Vol 9 (2S) ◽  
pp. 145-151
Keyword(s):  
Search Engines ◽  
Web Search ◽  
Classification Model ◽  
Web Documents ◽  
Web Document ◽  
Web Objects ◽  
User Query ◽  
Object Search ◽  
Search Facility ◽  
The Web

The classical Web search engines focus on satisfying the information need of the users by retrieving relevant Web documents corresponding to the user query. The Web document contains the information on different Web objects such as authors, automobiles, political parties e.t.c. The user might be accessing the Web document to procure information about a specific Web object, the remaining information in the Web object [2-6] becomes redundant specific to the user. If the size of Web documents is significantly large and the user information requirement is small fraction of the document, the user has to invest effort in locating the required information inside the document. It would be much more convenient if the user is provided with only the required Web object information located inside the Web documents. Web object search engines provide Web search facility through vertical search on Web objects. In this paper the main goal we considered is the objective information present in different documents is extracted and integrated into an object repository over which the Web object search facility is built.

scholarly journals Personalized Mobile Web Search Techniques

2014 ◽  
Vol 4 (11) ◽  
pp. 1193-1198
Author(s):  
Disha Gupta ◽  
Nekita Chavhan
Keyword(s):  
Web Search ◽  
Mobile Web ◽  
Search Techniques
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