AU2021100441A4 - A method of text mining in ranking of web pages using machine learning - Google Patents

A method of text mining in ranking of web pages using machine learning Download PDF

Info

Publication number
AU2021100441A4
AU2021100441A4 AU2021100441A AU2021100441A AU2021100441A4 AU 2021100441 A4 AU2021100441 A4 AU 2021100441A4 AU 2021100441 A AU2021100441 A AU 2021100441A AU 2021100441 A AU2021100441 A AU 2021100441A AU 2021100441 A4 AU2021100441 A4 AU 2021100441A4
Authority
AU
Australia
Prior art keywords
ranking
web pages
text
web
machine learning
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
AU2021100441A
Inventor
Piali Biswas
G. HimaBindu
Ch.Lakshmi Kumari
V. Revathi
J.Aruna Santhi
D. Shanthi
S. Silas
M. Sitha Ram
M. Srivenkatesh
A.Phani sheetal
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Biswas Piali Dr
Himabindu G Ms
Kumari ChLakshmi Mrs
Revathi V Ms
Santhi JAruna Mrs
Shanthi D Dr
Sheetal APhani Ms
Silas S Dr
Sitha Ram M Dr
Srivenkatesh M Dr
Original Assignee
Biswas Piali Dr
Himabindu G Ms
Kumari Ch Lakshmi Mrs
Revathi V Ms
Santhi J Aruna Mrs
Shanthi D Dr
Sheetal A Phani Ms
Silas S Dr
Sitha Ram M Dr
Srivenkatesh M Dr
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Biswas Piali Dr, Himabindu G Ms, Kumari Ch Lakshmi Mrs, Revathi V Ms, Santhi J Aruna Mrs, Shanthi D Dr, Sheetal A Phani Ms, Silas S Dr, Sitha Ram M Dr, Srivenkatesh M Dr filed Critical Biswas Piali Dr
Priority to AU2021100441A priority Critical patent/AU2021100441A4/en
Application granted granted Critical
Publication of AU2021100441A4 publication Critical patent/AU2021100441A4/en
Ceased legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/334—Query execution
    • G06F16/3347—Query execution using vector based model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/35—Clustering; Classification
    • G06F16/355—Class or cluster creation or modification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/3332—Query translation
    • G06F16/3335—Syntactic pre-processing, e.g. stopword elimination, stemming
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/951—Indexing; Web crawling techniques

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Computational Linguistics (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

A METHOD OF TEXT MINING IN RANKING OF WEB PAGES USING MACHINE LEARNING ABSTRACT A Web browser is a Software application for accessing the information from World Wide Web, displays the Web Pages based on the text keyword inputs from the Search Engine. The ranking of the Web Pages will vary from none Search Engine to another Search Engine. When user entered a query in the Search Engine, it should return relevant Web Pages related to the query by ranking the Web Pages. There are several factors affecting the ranking of the Web Pages such as content of pages, Title tags, URL structures, Meta Description Tags, XML Sitemap, Social Network Contents, Blog Forum Contents, and Videos. The Text Mining is an Artificial Intelligence technique for Text analytics to transform the unstructured Text in the documents and databases into normalized structured data. The existing Search Engines such as Google, Yahoo, Ask, and Bing produces numerous links for web pages which may or may not relevant for the search query of the user. The Text mining facilitates the clustering of the Web pages into top by ranking. The Text mining with the Machine Learning overcomes the difficulties in ranking the web pages in existing technologies of search engines by classification. The present invention proposed and disclosed herein is a Method of Text Mining in Ranking of Web Pages using Machine Learning comprising of: Keyword Query (201), Search Engine (202), Web Crawler (203), Web Page Retrieval (204), Preprocessing (205), Feature Extraction (206), Clustering (207), Classification (208), and Ranking Web Pages (209). The invention disclosed herein facilitates the Text Mining based web page ranking with the help of machine learning. The proposed present invention disclosed herein shows better ranking of the web pages with precision value of 0.9, and Recall value of 0.66 when compared with the other existing search engines and is validated on the Java platform. 1/1 A METHOD OF TEXT MINING IN RANKING OF WEB PAGES USING MACHINE LEARNING DRAWINGS 101 L2 OD Dataset Text Preprocessing Text transform Classification Clustering r- Feature Extraction Text Summary for Ranking 107 Figure 1: Text Mining using Machine Learning. 201 202 203 Keyword Query Search Engine Web Crawler 206 204 Feature Extraction Preprocessing Web Page Retrieval 207 208 209 Clustering Classification Ranking Web Pages Figure 2: A Method of Text Mining in Ranking of Web Pages using Machine Learning.

Description

1/1
A METHOD OF TEXT MINING IN RANKING OF WEB PAGES USING MACHINE LEARNING DRAWINGS
101 OD L2
Dataset Text Preprocessing Text transform
Classification Clustering r- Feature Extraction Text Summary for Ranking 107
Figure 1: Text Mining using Machine Learning.
201 202 203
Keyword Query Search Engine Web Crawler
206 204
Feature Extraction Preprocessing Web Page Retrieval
207 208 209
Clustering Classification Ranking Web Pages
Figure 2: A Method of Text Mining in Ranking of Web Pages using Machine
Learning.
I A METHOD OF TEXT MINING IN RANKING OF WEB PAGES USING MACHINE LEARNING FIELD OF INVENTION
[0001] The present invention relates to the technical field of Computer Science Engineering.
[0002] Particularly, the present invention is related to a Method of Text Mining in Ranking of Web Pages using Machine Learning of the broader field of Artificial Intelligence in Computer Science Engineering.
[0003] More particularly, the present invention is relates to a Method of Text Mining in Ranking of Web Pages using Machine Learning, a new method of ranking the Web Pages with the Text mining by the Machine Learning.
BACKGROUND OF INVENTION
[0004] A Web browser is a Software application for accessing the information from World Wide Web, displays the Web Pages based on the text keyword inputs from the Search Engine. The ranking of the Web Pages will vary from none Search Engine to another Search Engine. When user entered a query in the Search Engine, it should return relevant Web Pages related to the query by ranking the Web Pages.
[0005] There are several factors affecting the ranking of the Web Pages such as content of pages, Title tags, URL structures, Meta Description Tags, XML Sitemap, Social Network Contents, Blog Forum Contents, and Videos.
[0006] The Text Mining is an Artificial Intelligence technique for Text analytics to transform the unstructured Text in the documents and databases into normalized structured data.
[0007] The existing Search Engines such as Google, Yahoo, Ask, and Bing produces numerous links for web pages which may or may not relevant for the search query of the user.
[0008] The Text mining facilitates the clustering of the Web pages into top by ranking. The Text mining with the Machine Learning overcomes the difficulties in ranking the web pages in existing technologies of search engines by classification.
[0009] The Machine Learning is the subset of an Artificial Intelligence capable of retrieving the web pages from the worldwide web automatically at increased accuracy.
[0010] The Web Page Ranking may be done using Machine Learning from the Text mining in which the unstructured Text in the documents and databases transformed into normalized structured data.
[0011] The drawback of the existing techniques of Web Page Ranking using Search engines such as Google, Yahoo, Ask, and Bing is Low Precision Values due to which more irrelevant information is retrieved in the form of Web Pages, Lower Recall Values due to which all possible web pages will not be retrieved based on the user query.
[0012] The existing Search Engines such as Google, Yahoo, Ask, and Bing are used herein to test the performance of the proposed invention with Machine Learning technique to obtain more Precision and Recall values. The drawbacks such as Low Precision and Recall Values will be overcome with the present invention disclosed herein.
[0013] The Machine Learning algorithm along with the Text Mining was applied to the available Search Engines such as Google, Yahoo, Ask, and Bing with multi keyword based user query for mining the web pages which are matching the user query, rank values for the Web Pages is generated with the Web Page Ranking Algorithm.
[0014] Referring to Figure 1, Text Mining using Machine Learning comprising of Dataset (101), Text Preprocessing (102), Text transform (103), Feature Extraction (104), Clustering (105), Classification (106), and Text Summary for Ranking (107). It shows the mechanism of Text Mining with the help of Machine Learning to Rank the Text Summary.
[0014a] The Dataset (101) is considered to test the model using machine learning, the Dataset (101) is Text Analysis Conference TAC2011. The Text Preprocessing (102) converts the unstructured text into structured text by Text transform (103), removes the unwanted data or redundant text by performing tokenization, stop word removal and stemming. The Feature Extraction (104) produces the text vectors form the preprocessed data before applying the text mining algorithm for ranking the Web Pages. The Clustering (105) is used to cluster the similar web pages using clustering algorithm. The Classification (106) is done by the discriminative Support Vector Machine (DSVM), classify the Text Summary for Ranking (107) based on the feature vectors and the rank.
[0015] The present invention disclosed herein is uses the above method of Text Mining for Ranking the web Pages with the help of Machine Learning. The improved precision and recall values were found compared to the existing techniques of different search engines.
[0016] The present invention disclosed herein is used in different areas such as Publishing House in production, Literature Review, sentiment analysis, Risk Management, Cybercrime Prevention, Fraud Detection, Contextual Advertising, Content Enrichment, Spam Filtering, and Social Media Data Analysis.
SUMMARY OF INVENTION
[0017] Referring to Figure 1, Text Mining using Machine Learning comprising of Dataset (101), Text Preprocessing (102), Text transform (103), Feature Extraction (104), Clustering (105), Classification (106), and Text Summary for Ranking (107). It shows the mechanism of Text Mining with the help of Machine Learning to Rank the Text Summary.
[0017a] The Dataset (101) is considered to test the model using machine learning, the Dataset (101) is Text Analysis Conference TAC2011. The Text Preprocessing (102) converts the unstructured text into structured text by Text transform (103), removes the unwanted data or redundant text by performing tokenization, stop word removal and
'It
stemming. The Feature Extraction (104) produces the text vectors form the preprocessed data before applying the text mining algorithm for ranking the Web Pages. The Clustering (105) is used to cluster the similar web pages using clustering algorithm. The Classification (106) is done by the discriminative Support Vector Machine (DSVM), classify the Text Summary for Ranking (107) based on the feature vectors and the rank.
[0018] The present invention, Referring to Figure 2, A Method of Text Mining in Ranking of Web Pages using Machine Learning comprising of: Keyword Query (201), Search Engine (202), Web Crawler (203), Web Page Retrieval (204), Preprocessing (205), Feature Extraction (206), Clustering (207), Classification (208), and Ranking Web Pages (209).
[0018a] The Keyword Query (201) is the multiple keyword based query used by the user for text mining for the Web Pages ranking. The user requests the Search Engine (202) by the Keyword Query (201) with string of words, result Page generates multiple web pages based on this query. The proposed method compared with other Search Engines (202) such as Google, Yahoo, Ask, and Bing for its validation.
[0018b] The Web Crawler (203) collects the web links relevant to the Keyword search Query (201), finds the publicly available pages from the World Wide Web. The Web Page Retrieval (204) is for retrieval of Web Pages.
[0018c] The Preprocessing (205) performs tokenization, word removal and stemming for retrieved Search Engine Result Page.
[0018d] The Feature Extraction (206) produces the text vectors form the preprocessed data before applying the text mining algorithm for ranking the Web Pages. The multivariate samples are considered as features, can be selected by the Feature Selection algorithm known as Term Frequency Inverse Document Frequency. The Clustering (207) is performed by the LBG (Linde, Buzo, Gray) algorithm to create the clusters of the web pages which are similar. This algorithm will work same as k-means clustering algorithm for forming the similar web pages as clusters.
[0018e] The Classification (208) is done by a Discriminative Support Vector Machine (DSVM). The one-against-all (OAA) and one-against-one (OAO) strategy is followed by the DSVM classifier to Classify the Web Pages before ranking them. The Ranking Web Pages (209) uses page rank algorithm, measure frequency of the user reaching specific web page during one or two clicks of links in other websites.
[0019] The proposed present invention disclosed herein shows better ranking of the web pages with precision value of 0.9, and Recall value of 0.66 when compared with the other existing search engines and is validated on the Java platform.
BRIEF DESCRIPTION OF DRAWINGS
[0020] The Accompanying Drawings are included to provide further understanding of the invention disclosed here, and are incorporated in and constitute a part this specification. The drawing illustrates exemplary embodiments of the present disclosure and, together with the description, serves to explain the principles of the present disclosure. The Drawings are for illustration only, which thus not a limitation of the present disclosure.
[0021] Referring to Figure 1, Text Mining using Machine Learning comprising of Dataset (101), Text Preprocessing (102), Text transform (103), Feature Extraction (104), Clustering (105), Classification (106), and Text Summary for Ranking (107) provides the mechanism of Text Mining with the help of Machine Learning to Rank the Text Summary.
[0022] The present invention, Referring to Figure 2, A Method of Text Mining in Ranking of Web Pages using Machine Learning comprising of: Keyword Query (201), Search Engine (202), Web Crawler (203), Web Page Retrieval (204), Preprocessing (205), Feature Extraction (206), Clustering (207), Classification (208), and Ranking Web Pages (209). The invention disclosed herein facilitates the Text Mining based web page ranking with the help of machine leading.
[0023] Referring to Figure 1, illustrates Text Mining using Machine Learning, in accordance with an exemplary embodiment of the present disclosure.
[0024] Referring to Figure 2, illustrates a Method of Text Mining in Ranking of Web
U
Pages using Machine Learning, in accordance with another exemplary embodiment of the present disclosure.
DETAIL DESCRIPTION OF INVENTION
[0025] Referring to Figure 1, Text Mining using Machine Learning comprising of Dataset (101), Text Preprocessing (102), Text transform (103), Feature Extraction (104), Clustering (105), Classification (106), and Text Summary for Ranking (107) provides the mechanism of Text Mining with the help of Machine Learning to Rank the Text Summary.
[0025a] The Dataset (101) is considered to test the model using machine learning, the Dataset (101) is Text Analysis Conference TAC2011. The Text Preprocessing (102) converts the unstructured text into structured text by Text transform (103), removes the unwanted data or redundant text by performing tokenization, stop word removal and stemming. The Feature Extraction (104) produces the text vectors form the preprocessed data before applying the text mining algorithm for ranking the Web Pages. The Clustering (105) is used to cluster the similar web pages using clustering algorithm. The Classification (106) is done by the discriminative Support Vector Machine (DSVM), classify the Text Summary for Ranking (107) based on the feature vectors and the rank.
[0026] The present invention, Referring to Figure 2, A Method of Text Mining in Ranking of Web Pages using Machine Learning comprising of: Keyword Query (201), Search Engine (202), Web Crawler (203), Web Page Retrieval (204), Preprocessing (205), Feature Extraction (206), Clustering (207), Classification (208), and Ranking Web Pages (209). The invention disclosed herein facilitates the Text Mining based web page ranking with the help of machine learning.
[0026a] The Keyword Query (201) is the multiple keyword based query used by the user for text mining for the Web Pages ranking. The phrases that are used in the Search Engine (202) by the user are the Keywords. The multiple keyword searching facilitates the web search engine to generate page ranks. The user requests the Search Engine (202) by the Keyword Query (201) with string of words, Search Engine (202) result
Page generates multiple web pages based on this query. The proposed method compared with other Search Engines (202) such as Google, Yahoo, Ask, and Bing for its validation.
[0026b] The Web Crawler (203) collects the web links relevant to the Keyword search Query (201), finds the publicly available pages from the World Wide Web. The Web Crawler (203) gathers the list of addresses of the web pages based on the query from the web master and also provides how often the web pages are visited.
[0026c] The Web Page Retrieval (204) is for retrieval of Web Pages, Search Engine Result Page contains the web pages which are retrieved based on the user query. The title and the Web links are displayed in the Search Engine Result Page for Web Page Retrieval.
[0026d] The Preprocessing (205) performs tokenization, word removal and stemming for retrieved Search Engine Result Page. The tokenization generates tokens based on the splitting of the line of words into words, phrases, symbols, and other structured Data. The lexical analysis is made with the tokens generated. The word removal removes the stop words to facilitate the fast web page retrieval; it removes the unwanted words to reduce the dimensionality of the retrieval information. The stemming decreases the term index and performs the lossy compression, remove of various suffixes.
[0026e] The Feature Extraction (206) produces the text vectors form the preprocessed data before applying the text mining algorithm for ranking the Web Pages. The multivariate samples are considered as features, Features can be selected by the Feature Selection algorithm known as Term Frequency Inverse Document Frequency for computing weighting factor for text mining and web pages information retrieval process.
[0026f] The Clustering (207) is performed by the LBG (Linde, Buzo, Gray) algorithm to create the clusters of the web pages which are similar. This algorithm will work same as k-means clustering algorithm for forming the similar web pages as clusters. The Page rank algorithm methodology used by the Google is used here for ranking the web pages. But the same methodology is used here with the Machine Learning classifier for better ranking of the web pages.
[0026g] The Classification (208) is done by a Discriminative Support Vector Machine (DSVM). The one-against-all (OAA) and one-against-one (OAO) strategy is followed by the DSVM classifier to Classify the Web Pages before ranking them. The feature vectors and the current ranks are used by the classifier for further ranking and classifying.
[0026h] The Ranking Web Pages (209) uses page rank algorithm, measure frequency of the user reaching specific web page during one or two clicks of links in other websites, or by searching the web page based on the text phrases by giving one or two keywords.
[0027] The precision of the proposed system can be calculated using the following equation (1).
Precision Value = Relevant Documentn Retrieved Document Equation (1) Retrieved Document
The Low Precision Values creates more irrelevant information is retrieved in the form of Web Pages. The present invention disclosed herein is showing Precision Value of 0.9 compared to the other search engines. The following table-i provides the precision values of the other existing methods with our proposed method.
TABLE 1 Precision Values Comparison of disclosure disclosed herein with other Search Engines.
Search Engines Precision Value
Google 0.66
Yahoo 0.58
Ask 0.44
Bing 0.38
Proposed Invention 0.9
[0028] The Recall Value of the proposed system can be calculated using the following equation (2).
y
Recall Value = Relevant DocumentnRetrieved Document Equation(2) Relevant Document
The Low Recall Values will not be retrieved all web pages related to the user query. The present invention disclosed herein is showing Recall Value of 0.66 compared to the other search engines. The following table-2 provides the precision values of the other existing methods with our proposed method. TABLE2 Recall Values Comparison of disclosure disclosed herein with other Search Engines.
Search Engines Precision Value
Google 0.45
Yahoo 0.39
Ask 0.26
Bing 0.24
Proposed Invention 0.66
[00291 The Rank based comparison of the proposed system with other search engines for the word "Text Mining" are given in table 3. TABLE3 Recall Values Comparison of disclosure disclosed herein with other Search Engines.
Search Engine links Rank
Google: https://en.wikipedia.org/wiki/Text mining 3
Yahoo: https://www.ibm.com/cloud/learn/text-mining 5
Ask: https://monkeylearn.com/text-mini/ 4
Bing: https://www.ibm.com/cloud/learn/text-mining 6
Proposed Invention: https://www.javatpoint.com/text-data-mining 2

Claims (5)

A METHOD OF TEXT MINING IN RANKING OF WEB PAGES USING MACHINE LEARNING CLAIMS We claim:
1. A Method of Text Mining in Ranking of Web Pages using Machine Learning comprising of: Keyword Query (201), Search Engine (202), Web Crawler (203), Web Page Retrieval (204), Preprocessing (205), Feature Extraction (206), Clustering (207), Classification (208), and Ranking Web Pages (209) herein facilitates the Text Mining based web page ranking with the help of machine learning.
2. A Method of Text Mining in Ranking of Web Pages using Machine Learning as claimed in claim 1, wherein it is uses multiple keyword based query text mining for Web Pages ranking, Web Crawler for collecting the web links.
3. A Method of Text Mining in Ranking of Web Pages using Machine Learning as claimed in claim 1, wherein it uses Preprocessing (205) performs tokenization, word removal and stemming for retrieved Search Engine Result Page.
4. A Method of Text Mining in Ranking of Web Pages using Machine Learning as claimed in claim 1, wherein it uses Feature Extraction (206) produces the text vectors, the multivariate samples are considered as features, Features can be selected by the Feature Selection algorithm known as Term Frequency Inverse Document Frequency for computing weighting factor for text mining and web pages information retrieval process. The Clustering (207) is performed by the LBG (Linde, Buzo, Gray) algorithm to create the clusters of the web pages which are similar.
5. A Method of Text Mining in Ranking of Web Pages using Machine Learning as claimed in claim 1, wherein it uses The Classification (208) is done by a Discriminative Support Vector Machine (DSVM). The one-against-all (OAA) and one-against-one (OAO) strategy is followed by the DSVM classifier to Classify the Web Pages before ranking them. The feature vectors and the current ranks are used by the classifier for further ranking and classifying. It shows better ranking of the web pages with precision value of 0.9, and Recall value of 0.66 when compared with the other existing search engines and is validated on the Java platform.
AU2021100441A 2021-01-24 2021-01-24 A method of text mining in ranking of web pages using machine learning Ceased AU2021100441A4 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU2021100441A AU2021100441A4 (en) 2021-01-24 2021-01-24 A method of text mining in ranking of web pages using machine learning

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
AU2021100441A AU2021100441A4 (en) 2021-01-24 2021-01-24 A method of text mining in ranking of web pages using machine learning

Publications (1)

Publication Number Publication Date
AU2021100441A4 true AU2021100441A4 (en) 2021-06-03

Family

ID=76132780

Family Applications (1)

Application Number Title Priority Date Filing Date
AU2021100441A Ceased AU2021100441A4 (en) 2021-01-24 2021-01-24 A method of text mining in ranking of web pages using machine learning

Country Status (1)

Country Link
AU (1) AU2021100441A4 (en)

Similar Documents

Publication Publication Date Title
US9864808B2 (en) Knowledge-based entity detection and disambiguation
US10146862B2 (en) Context-based metadata generation and automatic annotation of electronic media in a computer network
CN108280114B (en) Deep learning-based user literature reading interest analysis method
US10002330B2 (en) Context based co-operative learning system and method for representing thematic relationships
CN110637316B (en) System and method for prospective object identification
US20090327266A1 (en) Index Optimization for Ranking Using a Linear Model
US20110307432A1 (en) Relevance for name segment searches
WO2006108069A2 (en) Searching through content which is accessible through web-based forms
JP7451747B2 (en) Methods, devices, equipment and computer readable storage media for searching content
US20100145922A1 (en) Personalized search apparatus and method
CN105912662A (en) Coreseek-based vertical search engine research and optimization method
Sivakumar Effectual web content mining using noise removal from web pages
CN111651675B (en) UCL-based user interest topic mining method and device
CN105279231A (en) Music resource aggregation search method
Ramya et al. DRDLC: discovering relevant documents using latent dirichlet allocation and cosine similarity
AU2021100441A4 (en) A method of text mining in ranking of web pages using machine learning
Rajkumar et al. Users’ click and bookmark based personalization using modified agglomerative clustering for web search engine
Singhal et al. Automating document annotation using open source knowledge
CN113516202A (en) Webpage accurate classification method for CBL feature extraction and denoising
Gavankar et al. Explicit query interpretation and diversification for context-driven concept search across ontologies
JP2011159100A (en) Successive similar document retrieval apparatus, successive similar document retrieval method and program
Potey et al. Personalization approaches for ranking: A review and research experiments
Arora et al. Survey on different ranking algorithms along with their approaches
Kundur et al. Recommendation System Based on Content Filtering for Specific Commodity
Sun et al. A method for discovering and obtaining company hot events from Internet news

Legal Events

Date Code Title Description
FGI Letters patent sealed or granted (innovation patent)
MK22 Patent ceased section 143a(d), or expired - non payment of renewal fee or expiry