SlideShare uma empresa Scribd logo
1 de 16
Concept Based Search SySearch vs. Traditional Search Simplified Version for External Yu Wang, Engineering Manager
Agenda ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
SySearch vs. Traditional Search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],A user receives an e-mail message that says: Following the incident close to Watford railway station in July, we need to assess the damage being done by tree branches tangling in overhead power lines or falling onto the tracks. The user then wants to locate documents matching the e-mail message.
Agenda ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Information Retrieval ,[object Object],[object Object],[object Object],[object Object]
IR Model, Key Metrics and Core Technologies User Query Query Rep. {Term} Document Rep. {Index, Term} Original Documents Relevance Ranking ,[object Object],[object Object]
Relevance Ranking: Traditional ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Relevance Ranking: SySearch ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example: Relevance Ranking (1) A user wants to find an article which introduces  both  apple and orange. He inputs query string {Apple, Orange} and the system found 2 match documents. Both documents contain both query terms, while in D1 Apple appears 3 times Orange and in D2 Apple appears equally to Orange in occurrence.  Is D1 or D2 more relevant to user need? Query Terms  {Apple, Orange} Apple  Orange Relevance Ranking Search Server Document Server Apple… Orange… Apple… Apple… Apple… Orange… Apple Orange… D1 D2 D1 Apple: Orange=3:1 D2 Apple: Orange=1:1 D1>D2? D2>D1?
Example: Relevance Ranking (2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Bayesian Probability (Simplified) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Boolean Model
Agenda ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
SySearch Functional Architecture SySearch Web GUI/API System Administration Security/CSI Event Framework Log Management Category Document Group Meta-Data Scheduler Query  Bayesian Engine Augmentation Expansion FS DB EM Document Import Document Filtering Content Add-on Meta-data Parser Text Processing Term Lexicon Index MPF Index Meta-data Index Term Index FS MPF Index Meta-data Index Term Index DB
SySearch Functionalities ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
SySearch Functionalities ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Distributed SySearch Deployment
Q&A

Mais conteúdo relacionado

Mais procurados

Probabilistic Information Retrieval
Probabilistic Information RetrievalProbabilistic Information Retrieval
Probabilistic Information RetrievalHarsh Thakkar
 
AndroidDescriptionsAndPermissions
AndroidDescriptionsAndPermissionsAndroidDescriptionsAndPermissions
AndroidDescriptionsAndPermissionsJoey Allen
 
Open Conceptual Data Models
Open Conceptual Data ModelsOpen Conceptual Data Models
Open Conceptual Data Modelsrumito
 
Placement oriented data structures
Placement oriented data structuresPlacement oriented data structures
Placement oriented data structuresLovelyn Rose
 
Interface for Finding Close Matches from Translation Memory
Interface for Finding Close Matches from Translation MemoryInterface for Finding Close Matches from Translation Memory
Interface for Finding Close Matches from Translation MemoryPriyatham Bollimpalli
 
professional fuzzy type-ahead rummage around in xml type-ahead search techni...
professional fuzzy type-ahead rummage around in xml  type-ahead search techni...professional fuzzy type-ahead rummage around in xml  type-ahead search techni...
professional fuzzy type-ahead rummage around in xml type-ahead search techni...Kumar Goud
 
Automatically Build Solr Synonyms List using Machine Learning - Chao Han, Luc...
Automatically Build Solr Synonyms List using Machine Learning - Chao Han, Luc...Automatically Build Solr Synonyms List using Machine Learning - Chao Han, Luc...
Automatically Build Solr Synonyms List using Machine Learning - Chao Han, Luc...Lucidworks
 
Dice.com Bay Area Search - Beyond Learning to Rank Talk
Dice.com Bay Area Search - Beyond Learning to Rank TalkDice.com Bay Area Search - Beyond Learning to Rank Talk
Dice.com Bay Area Search - Beyond Learning to Rank TalkSimon Hughes
 
Weblog Extraction With Fuzzy Classification Methods
Weblog Extraction With Fuzzy Classification MethodsWeblog Extraction With Fuzzy Classification Methods
Weblog Extraction With Fuzzy Classification MethodsEdy Portmann
 
Keyphrase Extraction using Neighborhood Knowledge
Keyphrase Extraction using Neighborhood KnowledgeKeyphrase Extraction using Neighborhood Knowledge
Keyphrase Extraction using Neighborhood KnowledgeIJMTST Journal
 
TensorFlow London 12: Oliver Gindele 'Recommender systems in Tensorflow'
TensorFlow London 12: Oliver Gindele 'Recommender systems in Tensorflow'TensorFlow London 12: Oliver Gindele 'Recommender systems in Tensorflow'
TensorFlow London 12: Oliver Gindele 'Recommender systems in Tensorflow'Seldon
 
Exploiting web search engines to search structured
Exploiting web search engines to search structuredExploiting web search engines to search structured
Exploiting web search engines to search structuredNita Pawar
 
Probabilistic Models of Novel Document Rankings for Faceted Topic Retrieval
Probabilistic Models of Novel Document Rankings for Faceted Topic RetrievalProbabilistic Models of Novel Document Rankings for Faceted Topic Retrieval
Probabilistic Models of Novel Document Rankings for Faceted Topic RetrievalYI-JHEN LIN
 
Data Mining and the Web_Past_Present and Future
Data Mining and the Web_Past_Present and FutureData Mining and the Web_Past_Present and Future
Data Mining and the Web_Past_Present and Futurefeiwin
 
IRJET- A Novel Approch Automatically Categorizing Software Technologies
IRJET- A Novel Approch Automatically Categorizing Software TechnologiesIRJET- A Novel Approch Automatically Categorizing Software Technologies
IRJET- A Novel Approch Automatically Categorizing Software TechnologiesIRJET Journal
 
Context-Based Diversification for Keyword Queries over XML Data
Context-Based Diversification for Keyword Queries over XML DataContext-Based Diversification for Keyword Queries over XML Data
Context-Based Diversification for Keyword Queries over XML Data1crore projects
 
Swap For Dummies Rsp 2007 11 29
Swap For Dummies Rsp 2007 11 29Swap For Dummies Rsp 2007 11 29
Swap For Dummies Rsp 2007 11 29Julie Allinson
 
Information retrieval 7 boolean model
Information retrieval 7 boolean modelInformation retrieval 7 boolean model
Information retrieval 7 boolean modelVaibhav Khanna
 

Mais procurados (20)

Probabilistic Information Retrieval
Probabilistic Information RetrievalProbabilistic Information Retrieval
Probabilistic Information Retrieval
 
AndroidDescriptionsAndPermissions
AndroidDescriptionsAndPermissionsAndroidDescriptionsAndPermissions
AndroidDescriptionsAndPermissions
 
Open Conceptual Data Models
Open Conceptual Data ModelsOpen Conceptual Data Models
Open Conceptual Data Models
 
Placement oriented data structures
Placement oriented data structuresPlacement oriented data structures
Placement oriented data structures
 
Interface for Finding Close Matches from Translation Memory
Interface for Finding Close Matches from Translation MemoryInterface for Finding Close Matches from Translation Memory
Interface for Finding Close Matches from Translation Memory
 
professional fuzzy type-ahead rummage around in xml type-ahead search techni...
professional fuzzy type-ahead rummage around in xml  type-ahead search techni...professional fuzzy type-ahead rummage around in xml  type-ahead search techni...
professional fuzzy type-ahead rummage around in xml type-ahead search techni...
 
Automatically Build Solr Synonyms List using Machine Learning - Chao Han, Luc...
Automatically Build Solr Synonyms List using Machine Learning - Chao Han, Luc...Automatically Build Solr Synonyms List using Machine Learning - Chao Han, Luc...
Automatically Build Solr Synonyms List using Machine Learning - Chao Han, Luc...
 
Dice.com Bay Area Search - Beyond Learning to Rank Talk
Dice.com Bay Area Search - Beyond Learning to Rank TalkDice.com Bay Area Search - Beyond Learning to Rank Talk
Dice.com Bay Area Search - Beyond Learning to Rank Talk
 
Weblog Extraction With Fuzzy Classification Methods
Weblog Extraction With Fuzzy Classification MethodsWeblog Extraction With Fuzzy Classification Methods
Weblog Extraction With Fuzzy Classification Methods
 
FINAL REVIEW
FINAL REVIEWFINAL REVIEW
FINAL REVIEW
 
BoTLRet: A Template-based Linked Data Information Retrieval
 BoTLRet: A Template-based Linked Data Information Retrieval BoTLRet: A Template-based Linked Data Information Retrieval
BoTLRet: A Template-based Linked Data Information Retrieval
 
Keyphrase Extraction using Neighborhood Knowledge
Keyphrase Extraction using Neighborhood KnowledgeKeyphrase Extraction using Neighborhood Knowledge
Keyphrase Extraction using Neighborhood Knowledge
 
TensorFlow London 12: Oliver Gindele 'Recommender systems in Tensorflow'
TensorFlow London 12: Oliver Gindele 'Recommender systems in Tensorflow'TensorFlow London 12: Oliver Gindele 'Recommender systems in Tensorflow'
TensorFlow London 12: Oliver Gindele 'Recommender systems in Tensorflow'
 
Exploiting web search engines to search structured
Exploiting web search engines to search structuredExploiting web search engines to search structured
Exploiting web search engines to search structured
 
Probabilistic Models of Novel Document Rankings for Faceted Topic Retrieval
Probabilistic Models of Novel Document Rankings for Faceted Topic RetrievalProbabilistic Models of Novel Document Rankings for Faceted Topic Retrieval
Probabilistic Models of Novel Document Rankings for Faceted Topic Retrieval
 
Data Mining and the Web_Past_Present and Future
Data Mining and the Web_Past_Present and FutureData Mining and the Web_Past_Present and Future
Data Mining and the Web_Past_Present and Future
 
IRJET- A Novel Approch Automatically Categorizing Software Technologies
IRJET- A Novel Approch Automatically Categorizing Software TechnologiesIRJET- A Novel Approch Automatically Categorizing Software Technologies
IRJET- A Novel Approch Automatically Categorizing Software Technologies
 
Context-Based Diversification for Keyword Queries over XML Data
Context-Based Diversification for Keyword Queries over XML DataContext-Based Diversification for Keyword Queries over XML Data
Context-Based Diversification for Keyword Queries over XML Data
 
Swap For Dummies Rsp 2007 11 29
Swap For Dummies Rsp 2007 11 29Swap For Dummies Rsp 2007 11 29
Swap For Dummies Rsp 2007 11 29
 
Information retrieval 7 boolean model
Information retrieval 7 boolean modelInformation retrieval 7 boolean model
Information retrieval 7 boolean model
 

Semelhante a Concept Based Search

Search for Clarify/Dovetail
Search for Clarify/DovetailSearch for Clarify/Dovetail
Search for Clarify/DovetailGary Sherman
 
Tdm information retrieval
Tdm information retrievalTdm information retrieval
Tdm information retrievalKU Leuven
 
Information_Retrieval_Models_Nfaoui_El_Habib
Information_Retrieval_Models_Nfaoui_El_HabibInformation_Retrieval_Models_Nfaoui_El_Habib
Information_Retrieval_Models_Nfaoui_El_HabibEl Habib NFAOUI
 
User friendly pattern search paradigm
User friendly pattern search paradigmUser friendly pattern search paradigm
User friendly pattern search paradigmMigrant Systems
 
Search Quality Evaluation to Help Reproducibility: An Open-source Approach
Search Quality Evaluation to Help Reproducibility: An Open-source ApproachSearch Quality Evaluation to Help Reproducibility: An Open-source Approach
Search Quality Evaluation to Help Reproducibility: An Open-source ApproachAlessandro Benedetti
 
Resume Parser with Natural Language Processing
Resume Parser with Natural Language ProcessingResume Parser with Natural Language Processing
Resume Parser with Natural Language ProcessingIRJET Journal
 
The Art Of Searching
The Art Of SearchingThe Art Of Searching
The Art Of SearchingPaul Neal
 
Search domain basics
Search domain basicsSearch domain basics
Search domain basicspmanvi
 
Search Quality Evaluation to Help Reproducibility : an Open Source Approach
Search Quality Evaluation to Help Reproducibility : an Open Source ApproachSearch Quality Evaluation to Help Reproducibility : an Open Source Approach
Search Quality Evaluation to Help Reproducibility : an Open Source ApproachAlessandro Benedetti
 
Rated Ranking Evaluator: An Open Source Approach for Search Quality Evaluation
Rated Ranking Evaluator: An Open Source Approach for Search Quality EvaluationRated Ranking Evaluator: An Open Source Approach for Search Quality Evaluation
Rated Ranking Evaluator: An Open Source Approach for Search Quality EvaluationAlessandro Benedetti
 
Haystack 2019 - Rated Ranking Evaluator: an Open Source Approach for Search Q...
Haystack 2019 - Rated Ranking Evaluator: an Open Source Approach for Search Q...Haystack 2019 - Rated Ranking Evaluator: an Open Source Approach for Search Q...
Haystack 2019 - Rated Ranking Evaluator: an Open Source Approach for Search Q...OpenSource Connections
 
Tovek Presentation by Livio Costantini
Tovek Presentation by Livio CostantiniTovek Presentation by Livio Costantini
Tovek Presentation by Livio Costantinimaxfalc
 
Rated Ranking Evaluator: an Open Source Approach for Search Quality Evaluation
Rated Ranking Evaluator: an Open Source Approach for Search Quality EvaluationRated Ranking Evaluator: an Open Source Approach for Search Quality Evaluation
Rated Ranking Evaluator: an Open Source Approach for Search Quality EvaluationSease
 
TECHNIQUES FOR COMPONENT REUSABLE APPROACH
TECHNIQUES FOR COMPONENT REUSABLE APPROACHTECHNIQUES FOR COMPONENT REUSABLE APPROACH
TECHNIQUES FOR COMPONENT REUSABLE APPROACHcscpconf
 
Using metadata repositories with search
Using metadata repositories with searchUsing metadata repositories with search
Using metadata repositories with searchJean Graef
 
Data Science - Part XI - Text Analytics
Data Science - Part XI - Text AnalyticsData Science - Part XI - Text Analytics
Data Science - Part XI - Text AnalyticsDerek Kane
 
Faceted search using Solr and Ontopia
Faceted search using Solr and OntopiaFaceted search using Solr and Ontopia
Faceted search using Solr and OntopiaGeir Ove Grønmo
 
Structured Document Search and Retrieval
Structured Document Search and RetrievalStructured Document Search and Retrieval
Structured Document Search and RetrievalOptum
 

Semelhante a Concept Based Search (20)

Search for Clarify/Dovetail
Search for Clarify/DovetailSearch for Clarify/Dovetail
Search for Clarify/Dovetail
 
Tdm information retrieval
Tdm information retrievalTdm information retrieval
Tdm information retrieval
 
Information_Retrieval_Models_Nfaoui_El_Habib
Information_Retrieval_Models_Nfaoui_El_HabibInformation_Retrieval_Models_Nfaoui_El_Habib
Information_Retrieval_Models_Nfaoui_El_Habib
 
Cl4201593597
Cl4201593597Cl4201593597
Cl4201593597
 
User friendly pattern search paradigm
User friendly pattern search paradigmUser friendly pattern search paradigm
User friendly pattern search paradigm
 
Search Quality Evaluation to Help Reproducibility: An Open-source Approach
Search Quality Evaluation to Help Reproducibility: An Open-source ApproachSearch Quality Evaluation to Help Reproducibility: An Open-source Approach
Search Quality Evaluation to Help Reproducibility: An Open-source Approach
 
I explore
I exploreI explore
I explore
 
Resume Parser with Natural Language Processing
Resume Parser with Natural Language ProcessingResume Parser with Natural Language Processing
Resume Parser with Natural Language Processing
 
The Art Of Searching
The Art Of SearchingThe Art Of Searching
The Art Of Searching
 
Search domain basics
Search domain basicsSearch domain basics
Search domain basics
 
Search Quality Evaluation to Help Reproducibility : an Open Source Approach
Search Quality Evaluation to Help Reproducibility : an Open Source ApproachSearch Quality Evaluation to Help Reproducibility : an Open Source Approach
Search Quality Evaluation to Help Reproducibility : an Open Source Approach
 
Rated Ranking Evaluator: An Open Source Approach for Search Quality Evaluation
Rated Ranking Evaluator: An Open Source Approach for Search Quality EvaluationRated Ranking Evaluator: An Open Source Approach for Search Quality Evaluation
Rated Ranking Evaluator: An Open Source Approach for Search Quality Evaluation
 
Haystack 2019 - Rated Ranking Evaluator: an Open Source Approach for Search Q...
Haystack 2019 - Rated Ranking Evaluator: an Open Source Approach for Search Q...Haystack 2019 - Rated Ranking Evaluator: an Open Source Approach for Search Q...
Haystack 2019 - Rated Ranking Evaluator: an Open Source Approach for Search Q...
 
Tovek Presentation by Livio Costantini
Tovek Presentation by Livio CostantiniTovek Presentation by Livio Costantini
Tovek Presentation by Livio Costantini
 
Rated Ranking Evaluator: an Open Source Approach for Search Quality Evaluation
Rated Ranking Evaluator: an Open Source Approach for Search Quality EvaluationRated Ranking Evaluator: an Open Source Approach for Search Quality Evaluation
Rated Ranking Evaluator: an Open Source Approach for Search Quality Evaluation
 
TECHNIQUES FOR COMPONENT REUSABLE APPROACH
TECHNIQUES FOR COMPONENT REUSABLE APPROACHTECHNIQUES FOR COMPONENT REUSABLE APPROACH
TECHNIQUES FOR COMPONENT REUSABLE APPROACH
 
Using metadata repositories with search
Using metadata repositories with searchUsing metadata repositories with search
Using metadata repositories with search
 
Data Science - Part XI - Text Analytics
Data Science - Part XI - Text AnalyticsData Science - Part XI - Text Analytics
Data Science - Part XI - Text Analytics
 
Faceted search using Solr and Ontopia
Faceted search using Solr and OntopiaFaceted search using Solr and Ontopia
Faceted search using Solr and Ontopia
 
Structured Document Search and Retrieval
Structured Document Search and RetrievalStructured Document Search and Retrieval
Structured Document Search and Retrieval
 

Último

Organizational Structure Running A Successful Business
Organizational Structure Running A Successful BusinessOrganizational Structure Running A Successful Business
Organizational Structure Running A Successful BusinessSeta Wicaksana
 
(Best) ENJOY Call Girls in Faridabad Ex | 8377087607
(Best) ENJOY Call Girls in Faridabad Ex | 8377087607(Best) ENJOY Call Girls in Faridabad Ex | 8377087607
(Best) ENJOY Call Girls in Faridabad Ex | 8377087607dollysharma2066
 
8447779800, Low rate Call girls in Rohini Delhi NCR
8447779800, Low rate Call girls in Rohini Delhi NCR8447779800, Low rate Call girls in Rohini Delhi NCR
8447779800, Low rate Call girls in Rohini Delhi NCRashishs7044
 
Annual General Meeting Presentation Slides
Annual General Meeting Presentation SlidesAnnual General Meeting Presentation Slides
Annual General Meeting Presentation SlidesKeppelCorporation
 
FULL ENJOY Call girls in Paharganj Delhi | 8377087607
FULL ENJOY Call girls in Paharganj Delhi | 8377087607FULL ENJOY Call girls in Paharganj Delhi | 8377087607
FULL ENJOY Call girls in Paharganj Delhi | 8377087607dollysharma2066
 
Pitch Deck Teardown: Geodesic.Life's $500k Pre-seed deck
Pitch Deck Teardown: Geodesic.Life's $500k Pre-seed deckPitch Deck Teardown: Geodesic.Life's $500k Pre-seed deck
Pitch Deck Teardown: Geodesic.Life's $500k Pre-seed deckHajeJanKamps
 
Call Us 📲8800102216📞 Call Girls In DLF City Gurgaon
Call Us 📲8800102216📞 Call Girls In DLF City GurgaonCall Us 📲8800102216📞 Call Girls In DLF City Gurgaon
Call Us 📲8800102216📞 Call Girls In DLF City Gurgaoncallgirls2057
 
8447779800, Low rate Call girls in Uttam Nagar Delhi NCR
8447779800, Low rate Call girls in Uttam Nagar Delhi NCR8447779800, Low rate Call girls in Uttam Nagar Delhi NCR
8447779800, Low rate Call girls in Uttam Nagar Delhi NCRashishs7044
 
Flow Your Strategy at Flight Levels Day 2024
Flow Your Strategy at Flight Levels Day 2024Flow Your Strategy at Flight Levels Day 2024
Flow Your Strategy at Flight Levels Day 2024Kirill Klimov
 
Future Of Sample Report 2024 | Redacted Version
Future Of Sample Report 2024 | Redacted VersionFuture Of Sample Report 2024 | Redacted Version
Future Of Sample Report 2024 | Redacted VersionMintel Group
 
Digital Transformation in the PLM domain - distrib.pdf
Digital Transformation in the PLM domain - distrib.pdfDigital Transformation in the PLM domain - distrib.pdf
Digital Transformation in the PLM domain - distrib.pdfJos Voskuil
 
2024 Numerator Consumer Study of Cannabis Usage
2024 Numerator Consumer Study of Cannabis Usage2024 Numerator Consumer Study of Cannabis Usage
2024 Numerator Consumer Study of Cannabis UsageNeil Kimberley
 
Marketplace and Quality Assurance Presentation - Vincent Chirchir
Marketplace and Quality Assurance Presentation - Vincent ChirchirMarketplace and Quality Assurance Presentation - Vincent Chirchir
Marketplace and Quality Assurance Presentation - Vincent Chirchirictsugar
 
8447779800, Low rate Call girls in New Ashok Nagar Delhi NCR
8447779800, Low rate Call girls in New Ashok Nagar Delhi NCR8447779800, Low rate Call girls in New Ashok Nagar Delhi NCR
8447779800, Low rate Call girls in New Ashok Nagar Delhi NCRashishs7044
 
India Consumer 2024 Redacted Sample Report
India Consumer 2024 Redacted Sample ReportIndia Consumer 2024 Redacted Sample Report
India Consumer 2024 Redacted Sample ReportMintel Group
 
Buy gmail accounts.pdf Buy Old Gmail Accounts
Buy gmail accounts.pdf Buy Old Gmail AccountsBuy gmail accounts.pdf Buy Old Gmail Accounts
Buy gmail accounts.pdf Buy Old Gmail AccountsBuy Verified Accounts
 
Contemporary Economic Issues Facing the Filipino Entrepreneur (1).pptx
Contemporary Economic Issues Facing the Filipino Entrepreneur (1).pptxContemporary Economic Issues Facing the Filipino Entrepreneur (1).pptx
Contemporary Economic Issues Facing the Filipino Entrepreneur (1).pptxMarkAnthonyAurellano
 

Último (20)

Call Us ➥9319373153▻Call Girls In North Goa
Call Us ➥9319373153▻Call Girls In North GoaCall Us ➥9319373153▻Call Girls In North Goa
Call Us ➥9319373153▻Call Girls In North Goa
 
Organizational Structure Running A Successful Business
Organizational Structure Running A Successful BusinessOrganizational Structure Running A Successful Business
Organizational Structure Running A Successful Business
 
(Best) ENJOY Call Girls in Faridabad Ex | 8377087607
(Best) ENJOY Call Girls in Faridabad Ex | 8377087607(Best) ENJOY Call Girls in Faridabad Ex | 8377087607
(Best) ENJOY Call Girls in Faridabad Ex | 8377087607
 
8447779800, Low rate Call girls in Rohini Delhi NCR
8447779800, Low rate Call girls in Rohini Delhi NCR8447779800, Low rate Call girls in Rohini Delhi NCR
8447779800, Low rate Call girls in Rohini Delhi NCR
 
Japan IT Week 2024 Brochure by 47Billion (English)
Japan IT Week 2024 Brochure by 47Billion (English)Japan IT Week 2024 Brochure by 47Billion (English)
Japan IT Week 2024 Brochure by 47Billion (English)
 
Annual General Meeting Presentation Slides
Annual General Meeting Presentation SlidesAnnual General Meeting Presentation Slides
Annual General Meeting Presentation Slides
 
FULL ENJOY Call girls in Paharganj Delhi | 8377087607
FULL ENJOY Call girls in Paharganj Delhi | 8377087607FULL ENJOY Call girls in Paharganj Delhi | 8377087607
FULL ENJOY Call girls in Paharganj Delhi | 8377087607
 
Pitch Deck Teardown: Geodesic.Life's $500k Pre-seed deck
Pitch Deck Teardown: Geodesic.Life's $500k Pre-seed deckPitch Deck Teardown: Geodesic.Life's $500k Pre-seed deck
Pitch Deck Teardown: Geodesic.Life's $500k Pre-seed deck
 
Call Us 📲8800102216📞 Call Girls In DLF City Gurgaon
Call Us 📲8800102216📞 Call Girls In DLF City GurgaonCall Us 📲8800102216📞 Call Girls In DLF City Gurgaon
Call Us 📲8800102216📞 Call Girls In DLF City Gurgaon
 
8447779800, Low rate Call girls in Uttam Nagar Delhi NCR
8447779800, Low rate Call girls in Uttam Nagar Delhi NCR8447779800, Low rate Call girls in Uttam Nagar Delhi NCR
8447779800, Low rate Call girls in Uttam Nagar Delhi NCR
 
Flow Your Strategy at Flight Levels Day 2024
Flow Your Strategy at Flight Levels Day 2024Flow Your Strategy at Flight Levels Day 2024
Flow Your Strategy at Flight Levels Day 2024
 
Future Of Sample Report 2024 | Redacted Version
Future Of Sample Report 2024 | Redacted VersionFuture Of Sample Report 2024 | Redacted Version
Future Of Sample Report 2024 | Redacted Version
 
Digital Transformation in the PLM domain - distrib.pdf
Digital Transformation in the PLM domain - distrib.pdfDigital Transformation in the PLM domain - distrib.pdf
Digital Transformation in the PLM domain - distrib.pdf
 
Enjoy ➥8448380779▻ Call Girls In Sector 18 Noida Escorts Delhi NCR
Enjoy ➥8448380779▻ Call Girls In Sector 18 Noida Escorts Delhi NCREnjoy ➥8448380779▻ Call Girls In Sector 18 Noida Escorts Delhi NCR
Enjoy ➥8448380779▻ Call Girls In Sector 18 Noida Escorts Delhi NCR
 
2024 Numerator Consumer Study of Cannabis Usage
2024 Numerator Consumer Study of Cannabis Usage2024 Numerator Consumer Study of Cannabis Usage
2024 Numerator Consumer Study of Cannabis Usage
 
Marketplace and Quality Assurance Presentation - Vincent Chirchir
Marketplace and Quality Assurance Presentation - Vincent ChirchirMarketplace and Quality Assurance Presentation - Vincent Chirchir
Marketplace and Quality Assurance Presentation - Vincent Chirchir
 
8447779800, Low rate Call girls in New Ashok Nagar Delhi NCR
8447779800, Low rate Call girls in New Ashok Nagar Delhi NCR8447779800, Low rate Call girls in New Ashok Nagar Delhi NCR
8447779800, Low rate Call girls in New Ashok Nagar Delhi NCR
 
India Consumer 2024 Redacted Sample Report
India Consumer 2024 Redacted Sample ReportIndia Consumer 2024 Redacted Sample Report
India Consumer 2024 Redacted Sample Report
 
Buy gmail accounts.pdf Buy Old Gmail Accounts
Buy gmail accounts.pdf Buy Old Gmail AccountsBuy gmail accounts.pdf Buy Old Gmail Accounts
Buy gmail accounts.pdf Buy Old Gmail Accounts
 
Contemporary Economic Issues Facing the Filipino Entrepreneur (1).pptx
Contemporary Economic Issues Facing the Filipino Entrepreneur (1).pptxContemporary Economic Issues Facing the Filipino Entrepreneur (1).pptx
Contemporary Economic Issues Facing the Filipino Entrepreneur (1).pptx
 

Concept Based Search

  • 1. Concept Based Search SySearch vs. Traditional Search Simplified Version for External Yu Wang, Engineering Manager
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9. Example: Relevance Ranking (1) A user wants to find an article which introduces both apple and orange. He inputs query string {Apple, Orange} and the system found 2 match documents. Both documents contain both query terms, while in D1 Apple appears 3 times Orange and in D2 Apple appears equally to Orange in occurrence. Is D1 or D2 more relevant to user need? Query Terms {Apple, Orange} Apple Orange Relevance Ranking Search Server Document Server Apple… Orange… Apple… Apple… Apple… Orange… Apple Orange… D1 D2 D1 Apple: Orange=3:1 D2 Apple: Orange=1:1 D1>D2? D2>D1?
  • 10.
  • 11.
  • 12. SySearch Functional Architecture SySearch Web GUI/API System Administration Security/CSI Event Framework Log Management Category Document Group Meta-Data Scheduler Query Bayesian Engine Augmentation Expansion FS DB EM Document Import Document Filtering Content Add-on Meta-data Parser Text Processing Term Lexicon Index MPF Index Meta-data Index Term Index FS MPF Index Meta-data Index Term Index DB
  • 13.
  • 14.
  • 16. Q&A