Novel of Web Search Strategy Based on Web Page Block Granularity Analysis Algorithm and Correlation Calculation Model

2019 ◽  
pp. 521-527
Author(s):  
Ganglong Fan ◽  
Hongsheng Xu
Keyword(s):  
Web Search ◽  
Search Strategy ◽  
Calculation Model ◽  
Web Page ◽  
Analysis Algorithm

The Pose of Data Model and Research Based on Analysis of Intelligent Test Paper

2013 ◽  
Vol 303-306 ◽  
pp. 2203-2206
Author(s):  
Cai Li Fang ◽  
Lin Wu
Keyword(s):  
Data Model ◽  
Paper Analysis ◽  
Calculation Model ◽  
Test Paper ◽  
Fast Reaction ◽  
Analysis Algorithm ◽  
The Future ◽  
Education Measurement ◽  
Calculation Models

As a important part of teaching, Traditional paper analysis algorithm is complicated and not comprehensive enough. This paper according to the indicators of education measurement and improves the traditional calculation models, the system assesses the perfect calculation model, The teacher also can get the true and fast reaction to students' knowledge and the quality of the paper. This method thereby provides a convincing basis for the improvement on the future teaching and increase the effectiveness in teaching

scholarly journals Document representations for classification of short web-page descriptions

2008 ◽  
Vol 18 (1) ◽  
pp. 123-138 ◽  
Author(s):  
Milos Radovanovic ◽  
Mirjana Ivanovic
Keyword(s):  
Text Categorization ◽  
Web Search ◽  
Classification Performance ◽  
Document Representation ◽  
Web Page ◽  
Ve Bayes ◽  
Large Hierarchy ◽  
The Impact ◽  
Document Representations

Motivated by applying Text Categorization to classification of Web search results, this paper describes an extensive experimental study of the impact of bag-of- words document representations on the performance of five major classifiers - Na?ve Bayes, SVM, Voted Perceptron, kNN and C4.5. The texts, representing short Web-page descriptions sorted into a large hierarchy of topics, are taken from the dmoz Open Directory Web-page ontology, and classifiers are trained to automatically determine the topics which may be relevant to a previously unseen Web-page. Different transformations of input data: stemming, normalization, logtf and idf, together with dimensionality reduction, are found to have a statistically significant improving or degrading effect on classification performance measured by classical metrics - accuracy, precision, recall, F1 and F2. The emphasis of the study is not on determining the best document representation which corresponds to each classifier, but rather on describing the effects of every individual transformation on classification, together with their mutual relationships. .

scholarly journals Techniques for Improving Web Search by  Understanding Queries

2021 ◽  
Author(s):  
◽  
Daniel Wayne Crabtree
Keyword(s):  
Search Engines ◽  
Best Practice ◽  
Web Search ◽  
Special Focus ◽  
Clustering Methods ◽  
Web Page ◽  
Clustering Method ◽  
Evaluation Measures ◽  
Search Results ◽  
Web Page Clustering

<p>This thesis investigates the refinement of web search results with a special focus on the use of clustering and the role of queries. It presents a collection of new methods for evaluating clustering methods, performing clustering effectively, and for performing query refinement. The thesis identifies different types of query, the situations where refinement is necessary, and the factors affecting search difficulty. It then analyses hard searches and argues that many of them fail because users and search engines have different query models. The thesis identifies best practice for evaluating web search results and search refinement methods. It finds that none of the commonly used evaluation measures for clustering meet all of the properties of good evaluation measures. It then presents new quality and coverage measures that satisfy all the desired properties and that rank clusterings correctly in all web page clustering situations. The thesis argues that current web page clustering methods work well when different interpretations of the query have distinct vocabulary, but still have several limitations and often produce incomprehensible clusters. It then presents a new clustering method that uses the query to guide the construction of semantically meaningful clusters. The new clustering method significantly improves performance. Finally, the thesis explores how searches and queries are composed of different aspects and shows how to use aspects to reduce the distance between the query models of search engines and users. It then presents fully automatic methods that identify query aspects, identify underrepresented aspects, and predict query difficulty. Used in combination, these methods have many applications — the thesis describes methods for two of them. The first method improves the search results for hard queries with underrepresented aspects by automatically expanding the query using semantically orthogonal keywords related to the underrepresented aspects. The second method helps users refine hard ambiguous queries by identifying the different query interpretations using a clustering of a diverse set of refinements. Both methods significantly outperform existing methods.</p>

scholarly journals Techniques for Improving Web Search by  Understanding Queries

2021 ◽  
Author(s):  
◽  
Daniel Wayne Crabtree
Keyword(s):  
Search Engines ◽  
Best Practice ◽  
Web Search ◽  
Special Focus ◽  
Clustering Methods ◽  
Web Page ◽  
Clustering Method ◽  
Evaluation Measures ◽  
Search Results ◽  
Web Page Clustering

<p>This thesis investigates the refinement of web search results with a special focus on the use of clustering and the role of queries. It presents a collection of new methods for evaluating clustering methods, performing clustering effectively, and for performing query refinement. The thesis identifies different types of query, the situations where refinement is necessary, and the factors affecting search difficulty. It then analyses hard searches and argues that many of them fail because users and search engines have different query models. The thesis identifies best practice for evaluating web search results and search refinement methods. It finds that none of the commonly used evaluation measures for clustering meet all of the properties of good evaluation measures. It then presents new quality and coverage measures that satisfy all the desired properties and that rank clusterings correctly in all web page clustering situations. The thesis argues that current web page clustering methods work well when different interpretations of the query have distinct vocabulary, but still have several limitations and often produce incomprehensible clusters. It then presents a new clustering method that uses the query to guide the construction of semantically meaningful clusters. The new clustering method significantly improves performance. Finally, the thesis explores how searches and queries are composed of different aspects and shows how to use aspects to reduce the distance between the query models of search engines and users. It then presents fully automatic methods that identify query aspects, identify underrepresented aspects, and predict query difficulty. Used in combination, these methods have many applications — the thesis describes methods for two of them. The first method improves the search results for hard queries with underrepresented aspects by automatically expanding the query using semantically orthogonal keywords related to the underrepresented aspects. The second method helps users refine hard ambiguous queries by identifying the different query interpretations using a clustering of a diverse set of refinements. Both methods significantly outperform existing methods.</p>

A Novel Heuristic PageRank Algorithm in Web Search

2011 ◽  
Vol 216 ◽  
pp. 747-751
Author(s):  
Yan Li He
Keyword(s):  
Probability Distribution ◽  
Web Search ◽  
The Internet ◽  
Power Method ◽  
Web Page ◽  
Principal Eigenvector ◽  
Pagerank Algorithm ◽  
Empirical Distributions ◽  
The Matrix ◽  
The Web

With the booming development of the Internet, web search engines have become the most important Internet tools for retrieving information. PageRank computes the principal eigenvector of the matrix describing the hyperlinks in the web using the famous power method. Based on empirical distributions of Web page degrees, we derived analytically the probability distribution for the PageRank metric. We found out that it follows the familiar inverse polynomial law reported for Web page degrees.

scholarly journals WEB GRAPH BASED SEARCH BY USING DENSITY OF KEYWORD AND AGE FACTOR

2013 ◽  
pp. 89-93
Author(s):  
GAURAV AGARWAL ◽  
SACHI GUPTA ◽  
SAURABH MUKHERJEE
Keyword(s):  
Search Engine ◽  
Web Search ◽  
Web Pages ◽  
Main Role ◽  
Ranking Algorithm ◽  
Web Page ◽  
Web Crawler ◽  
User Requirement ◽  
Priority Assignment ◽  
The Web

Today, web servers, are the key repositories of the information & internet is the source of getting this information. There is a mammoth data on the Internet. It becomes a difficult job to search out the accordant data. Search Engine plays a vital role in searching the accordant data. A search engine follows these steps: Web crawling by crawler, Indexing by Indexer and Searching by Searcher. Web crawler retrieves information of the web pages by following every link on the site. Which is stored by web search engine then the content of the web page is indexed by the indexer. The main role of indexer is how data can be catch soon as per user requirements. As the client gives a query, Search Engine searches the results corresponding to this query to provide excellent output. Here ambition is to enroot an algorithm for search engine which may response most desirable result as per user requirement. In this a ranking method is used by the search engine to rank the web pages. Various ranking approaches are discussed in literature but in this paper, ranking algorithm is proposed which is based on parent-child relationship. Proposed ranking algorithm is based on priority assignment phase of Heterogeneous Earliest Finish Time (HEFT) Algorithm which is designed for multiprocessor task scheduling. Proposed algorithm works on three on range variable its means the density of keywords, number of successors to the nodes and the age of the web page. Density shows the occurrence of the keyword on the particular web page. Numbers of successors represent the outgoing link to a single web page. Age is the freshness value of the web page. The page which is modified recently is the freshest page and having the smallest age or largest freshness value. Proposed Technique requires that the priorities of each page to be set with the downward rank values & pages are arranged in ascending/ Descending order of their rank values. Experiments show that our algorithm is valuable. After the comparison with Google we find that our Algorithm is performing better. For 70% problems our algorithm is working better than Google.

TemporalClassifier

2018 ◽  
pp. 1143-1165
Author(s):  
Rahul Pradhan ◽  
Dilip Kumar Sharma
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Web Search ◽  
Web Page ◽  
Temporal Expression ◽  
Course Of Action ◽  
Temporal Boundary ◽  
Web Search Engines ◽  
The Web

Users issuing query on search engine, expect results to more relevant to query topic rather than just the textual match with text in query. Studies conducted by few researchers shows that user want the search engine to understand the implicit intent of query rather than looking the textual match in hypertext structure of document or web page. In this paper the authors will be addressing queries that have any temporal intent and help the web search engines to classify them in certain categories. These classes or categories will help search engine to understand and cater the need of query. The authors will consider temporal expression (e.g. 1943) in document and categories them on the basis of temporal boundary of that query. Their experiment classifies the query and tries to suggest further course of action for search engines. Results shows that classifying the query to these classes will help user to reach his/her seeking information faster.

Sign in / Sign up

Export Citation Format

Share Document