SlideShare a Scribd company logo

Introduction of Semantic Web using NLP techniques.

To provide relevant data to users form massive data available on web the Semantic Web technique is used. This presentation gives introduction of semantic web and how NLP can be used in it.

1 of 12
Download to read offline
“HANDLING UNSTRUCTURED
DATA FOR SEMANTIC WEB”
Subject : Introduction of Semantic Web using NLP
- Sandeep Wakchaure
CONTENTS
 Introduction
 Data Forms
 Architecture
 Example
 Conclusion
 Reference
 Problem Statement : To get knowledge or
information on web search engine which does not
contain any kind of irrelevant data.
 Reason : In day to day life there large amount of
unstructured data is going to stored on web, data
warehouses , repository or on cloud.
 Purpose of the System: To get the relevant
data there should be technique which process the
entered keywords, find the context and provide
the relevant knowledge.
INTRODUCTION
 When a user search on web i.e. retrieving data through search engine, the
results contains the large amount of data which are not user’s required
information.
 In this case keywords search techniques is fail here, to get required
information with the huge amount of information.
 Hence there is need to move from original Web to the Semantic Web for fast
related and precise information access.
 Many fields of computer world such as Data Mining, Information
Retrieval, Database Management System and NLP have been introduced
with Semantic Web for machine supported data interpretation and
process integration.
DATA IN THE FORM OF :
Structured Data-
This data has structure in terms of grammar pattern and
contextual relations.
Unstructured Data-
This data has not a specific structure but it may have grammar.
Posted queries and answers on the page, advertisements,
graphics, text, emails, presentations and so they are included in
the unstructured data.
TECHNIQUE USED
Ontology : Ontology is a description of things that exist and how they
relate to each other. It is a study of categories of things and their
relation among them.
The core part of Semantic Web is ontologies, which defines the
relationship between related entities, which achieved using
 RDF(S) (Resource Description Framework/Schema) and
 OWL (Web Ontology Languages)
Ontologies and reasoning rules are applied to reason about data and infer
new information. Rules are nothing but some condition or
restriction to be applied on data to draw some facts. In fact,
Semantic Web is like a collection of related and clustered facts.

Recommended

Technical Whitepaper: A Knowledge Correlation Search Engine
Technical Whitepaper: A Knowledge Correlation Search EngineTechnical Whitepaper: A Knowledge Correlation Search Engine
Technical Whitepaper: A Knowledge Correlation Search Engines0P5a41b
 
Auto Mapping Texts for Human-Machine Analysis and Sensemaking
Auto Mapping Texts for Human-Machine Analysis and SensemakingAuto Mapping Texts for Human-Machine Analysis and Sensemaking
Auto Mapping Texts for Human-Machine Analysis and SensemakingShalin Hai-Jew
 
SemWeb Fundamentals - Info Linking & Layering in Practice
SemWeb Fundamentals - Info Linking & Layering in PracticeSemWeb Fundamentals - Info Linking & Layering in Practice
SemWeb Fundamentals - Info Linking & Layering in PracticeDan Brickley
 
Self adaptive based natural language interface for disambiguation of
Self adaptive based natural language interface for disambiguation ofSelf adaptive based natural language interface for disambiguation of
Self adaptive based natural language interface for disambiguation ofNurfadhlina Mohd Sharef
 
Keyword Query Routing
Keyword Query RoutingKeyword Query Routing
Keyword Query RoutingSWAMI06
 

More Related Content

What's hot

Nlp and semantic_web_for_competitive_int
Nlp and semantic_web_for_competitive_intNlp and semantic_web_for_competitive_int
Nlp and semantic_web_for_competitive_intKarenVacca
 
Exploration of a Data Landscape using a Collaborative Linked Data Framework.
Exploration of a Data Landscape using a Collaborative Linked Data Framework.Exploration of a Data Landscape using a Collaborative Linked Data Framework.
Exploration of a Data Landscape using a Collaborative Linked Data Framework.Laurent Alquier
 
Ontology For Data Integration
Ontology For Data IntegrationOntology For Data Integration
Ontology For Data Integrationjuanesteva
 
User behaviour modeling for data prefetching in web applications
User behaviour modeling for data prefetching in web applicationsUser behaviour modeling for data prefetching in web applications
User behaviour modeling for data prefetching in web applicationsKacper Łukawski
 
Question Answering over Linked Data - Reasoning Issues
Question Answering over Linked Data - Reasoning IssuesQuestion Answering over Linked Data - Reasoning Issues
Question Answering over Linked Data - Reasoning IssuesMichael Petychakis
 
A NEAR-DUPLICATE DETECTION ALGORITHM TO FACILITATE DOCUMENT CLUSTERING
A NEAR-DUPLICATE DETECTION ALGORITHM TO FACILITATE DOCUMENT CLUSTERINGA NEAR-DUPLICATE DETECTION ALGORITHM TO FACILITATE DOCUMENT CLUSTERING
A NEAR-DUPLICATE DETECTION ALGORITHM TO FACILITATE DOCUMENT CLUSTERINGIJDKP
 
Web Mining & Text Mining
Web Mining & Text MiningWeb Mining & Text Mining
Web Mining & Text MiningHemant Sharma
 
WEB SEARCH ENGINE BASED SEMANTIC SIMILARITY MEASURE BETWEEN WORDS USING PATTE...
WEB SEARCH ENGINE BASED SEMANTIC SIMILARITY MEASURE BETWEEN WORDS USING PATTE...WEB SEARCH ENGINE BASED SEMANTIC SIMILARITY MEASURE BETWEEN WORDS USING PATTE...
WEB SEARCH ENGINE BASED SEMANTIC SIMILARITY MEASURE BETWEEN WORDS USING PATTE...cscpconf
 
Algorithm for calculating relevance of documents in information retrieval sys...
Algorithm for calculating relevance of documents in information retrieval sys...Algorithm for calculating relevance of documents in information retrieval sys...
Algorithm for calculating relevance of documents in information retrieval sys...IRJET Journal
 
Volume 2-issue-6-2016-2020
Volume 2-issue-6-2016-2020Volume 2-issue-6-2016-2020
Volume 2-issue-6-2016-2020Editor IJARCET
 
Data, Text and Web Mining
Data, Text and Web Mining Data, Text and Web Mining
Data, Text and Web Mining Jeremiah Fadugba
 
Semantic Annotation: The Mainstay of Semantic Web
Semantic Annotation: The Mainstay of Semantic WebSemantic Annotation: The Mainstay of Semantic Web
Semantic Annotation: The Mainstay of Semantic WebEditor IJCATR
 
Semantic Integration for Heterogeneous Domain-specific Information: The NIF Case
Semantic Integration for Heterogeneous Domain-specific Information: The NIF CaseSemantic Integration for Heterogeneous Domain-specific Information: The NIF Case
Semantic Integration for Heterogeneous Domain-specific Information: The NIF CaseNeuroscience Information Framework
 
IEEE 2014 JAVA DATA MINING PROJECTS Keyword query routing
IEEE 2014 JAVA DATA MINING PROJECTS Keyword query routingIEEE 2014 JAVA DATA MINING PROJECTS Keyword query routing
IEEE 2014 JAVA DATA MINING PROJECTS Keyword query routingIEEEFINALYEARSTUDENTPROJECTS
 

What's hot (17)

Nlp and semantic_web_for_competitive_int
Nlp and semantic_web_for_competitive_intNlp and semantic_web_for_competitive_int
Nlp and semantic_web_for_competitive_int
 
Exploration of a Data Landscape using a Collaborative Linked Data Framework.
Exploration of a Data Landscape using a Collaborative Linked Data Framework.Exploration of a Data Landscape using a Collaborative Linked Data Framework.
Exploration of a Data Landscape using a Collaborative Linked Data Framework.
 
Ontology For Data Integration
Ontology For Data IntegrationOntology For Data Integration
Ontology For Data Integration
 
User behaviour modeling for data prefetching in web applications
User behaviour modeling for data prefetching in web applicationsUser behaviour modeling for data prefetching in web applications
User behaviour modeling for data prefetching in web applications
 
Question Answering over Linked Data - Reasoning Issues
Question Answering over Linked Data - Reasoning IssuesQuestion Answering over Linked Data - Reasoning Issues
Question Answering over Linked Data - Reasoning Issues
 
A NEAR-DUPLICATE DETECTION ALGORITHM TO FACILITATE DOCUMENT CLUSTERING
A NEAR-DUPLICATE DETECTION ALGORITHM TO FACILITATE DOCUMENT CLUSTERINGA NEAR-DUPLICATE DETECTION ALGORITHM TO FACILITATE DOCUMENT CLUSTERING
A NEAR-DUPLICATE DETECTION ALGORITHM TO FACILITATE DOCUMENT CLUSTERING
 
Az31349353
Az31349353Az31349353
Az31349353
 
Web Mining & Text Mining
Web Mining & Text MiningWeb Mining & Text Mining
Web Mining & Text Mining
 
WEB SEARCH ENGINE BASED SEMANTIC SIMILARITY MEASURE BETWEEN WORDS USING PATTE...
WEB SEARCH ENGINE BASED SEMANTIC SIMILARITY MEASURE BETWEEN WORDS USING PATTE...WEB SEARCH ENGINE BASED SEMANTIC SIMILARITY MEASURE BETWEEN WORDS USING PATTE...
WEB SEARCH ENGINE BASED SEMANTIC SIMILARITY MEASURE BETWEEN WORDS USING PATTE...
 
Algorithm for calculating relevance of documents in information retrieval sys...
Algorithm for calculating relevance of documents in information retrieval sys...Algorithm for calculating relevance of documents in information retrieval sys...
Algorithm for calculating relevance of documents in information retrieval sys...
 
Price "KBART: improving the supply of data to link resolvers and knowledge ba...
Price "KBART: improving the supply of data to link resolvers and knowledge ba...Price "KBART: improving the supply of data to link resolvers and knowledge ba...
Price "KBART: improving the supply of data to link resolvers and knowledge ba...
 
Volume 2-issue-6-2016-2020
Volume 2-issue-6-2016-2020Volume 2-issue-6-2016-2020
Volume 2-issue-6-2016-2020
 
Web Mining
Web Mining Web Mining
Web Mining
 
Data, Text and Web Mining
Data, Text and Web Mining Data, Text and Web Mining
Data, Text and Web Mining
 
Semantic Annotation: The Mainstay of Semantic Web
Semantic Annotation: The Mainstay of Semantic WebSemantic Annotation: The Mainstay of Semantic Web
Semantic Annotation: The Mainstay of Semantic Web
 
Semantic Integration for Heterogeneous Domain-specific Information: The NIF Case
Semantic Integration for Heterogeneous Domain-specific Information: The NIF CaseSemantic Integration for Heterogeneous Domain-specific Information: The NIF Case
Semantic Integration for Heterogeneous Domain-specific Information: The NIF Case
 
IEEE 2014 JAVA DATA MINING PROJECTS Keyword query routing
IEEE 2014 JAVA DATA MINING PROJECTS Keyword query routingIEEE 2014 JAVA DATA MINING PROJECTS Keyword query routing
IEEE 2014 JAVA DATA MINING PROJECTS Keyword query routing
 

Similar to Introduction of Semantic Web using NLP techniques.

SEMANTIC INFORMATION EXTRACTION IN UNIVERSITY DOMAIN
SEMANTIC INFORMATION EXTRACTION IN UNIVERSITY DOMAINSEMANTIC INFORMATION EXTRACTION IN UNIVERSITY DOMAIN
SEMANTIC INFORMATION EXTRACTION IN UNIVERSITY DOMAINcscpconf
 
Ontology Based Approach for Semantic Information Retrieval System
Ontology Based Approach for Semantic Information Retrieval SystemOntology Based Approach for Semantic Information Retrieval System
Ontology Based Approach for Semantic Information Retrieval SystemIJTET Journal
 
Semantics in Financial Services -David Newman
Semantics in Financial Services -David NewmanSemantics in Financial Services -David Newman
Semantics in Financial Services -David NewmanPeter Berger
 
Semantic Information Retrieval Using Ontology in University Domain
Semantic Information Retrieval Using Ontology in University Domain Semantic Information Retrieval Using Ontology in University Domain
Semantic Information Retrieval Using Ontology in University Domain dannyijwest
 
SEMANTIC NETWORK BASED MECHANISMS FOR KNOWLEDGE ACQUISITION
SEMANTIC NETWORK BASED MECHANISMS FOR KNOWLEDGE ACQUISITIONSEMANTIC NETWORK BASED MECHANISMS FOR KNOWLEDGE ACQUISITION
SEMANTIC NETWORK BASED MECHANISMS FOR KNOWLEDGE ACQUISITIONcscpconf
 
Building a Semantic search Engine in a library
Building a Semantic search Engine in a libraryBuilding a Semantic search Engine in a library
Building a Semantic search Engine in a librarySEECS NUST
 
From Linked Data to Semantic Applications
From Linked Data to Semantic ApplicationsFrom Linked Data to Semantic Applications
From Linked Data to Semantic ApplicationsAndre Freitas
 
Toward The Semantic Deep Web
Toward The Semantic Deep WebToward The Semantic Deep Web
Toward The Semantic Deep WebSamiul Hoque
 
Semantic Information Retrieval Based on Domain Ontology
Semantic Information Retrieval Based on Domain OntologySemantic Information Retrieval Based on Domain Ontology
Semantic Information Retrieval Based on Domain Ontologyijbuiiir1
 
Disadvantages And Disadvantages Of A Relational Database...
Disadvantages And Disadvantages Of A Relational Database...Disadvantages And Disadvantages Of A Relational Database...
Disadvantages And Disadvantages Of A Relational Database...Carla Potier
 
Semantic Search of E-Learning Documents Using Ontology Based System
Semantic Search of E-Learning Documents Using Ontology Based SystemSemantic Search of E-Learning Documents Using Ontology Based System
Semantic Search of E-Learning Documents Using Ontology Based Systemijcnes
 
Extracting and Reducing the Semantic Information Content of Web Documents to ...
Extracting and Reducing the Semantic Information Content of Web Documents to ...Extracting and Reducing the Semantic Information Content of Web Documents to ...
Extracting and Reducing the Semantic Information Content of Web Documents to ...ijsrd.com
 
Enhancing Semantic Mining
Enhancing Semantic MiningEnhancing Semantic Mining
Enhancing Semantic MiningSanthosh Kumar
 
Riding The Semantic Wave
Riding The Semantic WaveRiding The Semantic Wave
Riding The Semantic WaveKaniska Mandal
 
Document Based Data Modeling Technique
Document Based Data Modeling TechniqueDocument Based Data Modeling Technique
Document Based Data Modeling TechniqueCarmen Sanborn
 
An imperative focus on semantic
An imperative focus on semanticAn imperative focus on semantic
An imperative focus on semanticijasa
 
2014 IEEE JAVA DATA MINING PROJECT Keyword query routing
2014 IEEE JAVA DATA MINING PROJECT Keyword query routing2014 IEEE JAVA DATA MINING PROJECT Keyword query routing
2014 IEEE JAVA DATA MINING PROJECT Keyword query routingIEEEMEMTECHSTUDENTSPROJECTS
 
INFORMATION RETRIEVAL TECHNIQUE FOR WEB USING NLP
INFORMATION RETRIEVAL TECHNIQUE FOR WEB USING NLP INFORMATION RETRIEVAL TECHNIQUE FOR WEB USING NLP
INFORMATION RETRIEVAL TECHNIQUE FOR WEB USING NLP ijnlc
 

Similar to Introduction of Semantic Web using NLP techniques. (20)

SEMANTIC INFORMATION EXTRACTION IN UNIVERSITY DOMAIN
SEMANTIC INFORMATION EXTRACTION IN UNIVERSITY DOMAINSEMANTIC INFORMATION EXTRACTION IN UNIVERSITY DOMAIN
SEMANTIC INFORMATION EXTRACTION IN UNIVERSITY DOMAIN
 
Ontology Based Approach for Semantic Information Retrieval System
Ontology Based Approach for Semantic Information Retrieval SystemOntology Based Approach for Semantic Information Retrieval System
Ontology Based Approach for Semantic Information Retrieval System
 
Semantics in Financial Services -David Newman
Semantics in Financial Services -David NewmanSemantics in Financial Services -David Newman
Semantics in Financial Services -David Newman
 
Semantic Information Retrieval Using Ontology in University Domain
Semantic Information Retrieval Using Ontology in University Domain Semantic Information Retrieval Using Ontology in University Domain
Semantic Information Retrieval Using Ontology in University Domain
 
Semantic Web Nature
Semantic Web NatureSemantic Web Nature
Semantic Web Nature
 
SEMANTIC NETWORK BASED MECHANISMS FOR KNOWLEDGE ACQUISITION
SEMANTIC NETWORK BASED MECHANISMS FOR KNOWLEDGE ACQUISITIONSEMANTIC NETWORK BASED MECHANISMS FOR KNOWLEDGE ACQUISITION
SEMANTIC NETWORK BASED MECHANISMS FOR KNOWLEDGE ACQUISITION
 
Building a Semantic search Engine in a library
Building a Semantic search Engine in a libraryBuilding a Semantic search Engine in a library
Building a Semantic search Engine in a library
 
From Linked Data to Semantic Applications
From Linked Data to Semantic ApplicationsFrom Linked Data to Semantic Applications
From Linked Data to Semantic Applications
 
Introduction abstract
Introduction abstractIntroduction abstract
Introduction abstract
 
Toward The Semantic Deep Web
Toward The Semantic Deep WebToward The Semantic Deep Web
Toward The Semantic Deep Web
 
Semantic Information Retrieval Based on Domain Ontology
Semantic Information Retrieval Based on Domain OntologySemantic Information Retrieval Based on Domain Ontology
Semantic Information Retrieval Based on Domain Ontology
 
Disadvantages And Disadvantages Of A Relational Database...
Disadvantages And Disadvantages Of A Relational Database...Disadvantages And Disadvantages Of A Relational Database...
Disadvantages And Disadvantages Of A Relational Database...
 
Semantic Search of E-Learning Documents Using Ontology Based System
Semantic Search of E-Learning Documents Using Ontology Based SystemSemantic Search of E-Learning Documents Using Ontology Based System
Semantic Search of E-Learning Documents Using Ontology Based System
 
Extracting and Reducing the Semantic Information Content of Web Documents to ...
Extracting and Reducing the Semantic Information Content of Web Documents to ...Extracting and Reducing the Semantic Information Content of Web Documents to ...
Extracting and Reducing the Semantic Information Content of Web Documents to ...
 
Enhancing Semantic Mining
Enhancing Semantic MiningEnhancing Semantic Mining
Enhancing Semantic Mining
 
Riding The Semantic Wave
Riding The Semantic WaveRiding The Semantic Wave
Riding The Semantic Wave
 
Document Based Data Modeling Technique
Document Based Data Modeling TechniqueDocument Based Data Modeling Technique
Document Based Data Modeling Technique
 
An imperative focus on semantic
An imperative focus on semanticAn imperative focus on semantic
An imperative focus on semantic
 
2014 IEEE JAVA DATA MINING PROJECT Keyword query routing
2014 IEEE JAVA DATA MINING PROJECT Keyword query routing2014 IEEE JAVA DATA MINING PROJECT Keyword query routing
2014 IEEE JAVA DATA MINING PROJECT Keyword query routing
 
INFORMATION RETRIEVAL TECHNIQUE FOR WEB USING NLP
INFORMATION RETRIEVAL TECHNIQUE FOR WEB USING NLP INFORMATION RETRIEVAL TECHNIQUE FOR WEB USING NLP
INFORMATION RETRIEVAL TECHNIQUE FOR WEB USING NLP
 

Recently uploaded

killingcamp 광고삽입문제 풀이, killingcamp 광고삽입문제 풀이
killingcamp 광고삽입문제 풀이, killingcamp 광고삽입문제 풀이killingcamp 광고삽입문제 풀이, killingcamp 광고삽입문제 풀이
killingcamp 광고삽입문제 풀이, killingcamp 광고삽입문제 풀이ssuser82c38d
 
AUTOKEYUNLOCKER-BRANDS-SUPPORT-STANDARD-VERSION.pdf
AUTOKEYUNLOCKER-BRANDS-SUPPORT-STANDARD-VERSION.pdfAUTOKEYUNLOCKER-BRANDS-SUPPORT-STANDARD-VERSION.pdf
AUTOKEYUNLOCKER-BRANDS-SUPPORT-STANDARD-VERSION.pdfAutokey
 
Implementing Docker Containers with Windows Server 2019
Implementing Docker Containers with Windows Server 2019Implementing Docker Containers with Windows Server 2019
Implementing Docker Containers with Windows Server 2019VICTOR MAESTRE RAMIREZ
 
The Top Outages of 2023: Analyses and Takeaways
The Top Outages of 2023: Analyses and TakeawaysThe Top Outages of 2023: Analyses and Takeaways
The Top Outages of 2023: Analyses and TakeawaysThousandEyes
 
Joseph Yoder : Being Agile about Architecture
Joseph Yoder : Being Agile about ArchitectureJoseph Yoder : Being Agile about Architecture
Joseph Yoder : Being Agile about ArchitectureHironori Washizaki
 
killingcamp longest common subsequence.pdf
killingcamp longest common subsequence.pdfkillingcamp longest common subsequence.pdf
killingcamp longest common subsequence.pdfssuser82c38d
 
OpenChain AI Study Group - North America and Europe - 2024-02-20
OpenChain AI Study Group - North America and Europe - 2024-02-20OpenChain AI Study Group - North America and Europe - 2024-02-20
OpenChain AI Study Group - North America and Europe - 2024-02-20Shane Coughlan
 
Essence of Requirements Engineering: Pragmatic Insights for 2024
Essence of Requirements Engineering: Pragmatic Insights for 2024Essence of Requirements Engineering: Pragmatic Insights for 2024
Essence of Requirements Engineering: Pragmatic Insights for 2024Asher Sterkin
 
Machine Learning Basics for Dummies (no math!)
Machine Learning Basics for Dummies (no math!)Machine Learning Basics for Dummies (no math!)
Machine Learning Basics for Dummies (no math!)Dmitry Zinoviev
 
Automation for Bonterra Impact Management (fka Apricot)
Automation for Bonterra Impact Management (fka Apricot)Automation for Bonterra Impact Management (fka Apricot)
Automation for Bonterra Impact Management (fka Apricot)Jeffrey Haguewood
 
AI Product Management by Abhijit Bendigiri
AI Product Management by Abhijit BendigiriAI Product Management by Abhijit Bendigiri
AI Product Management by Abhijit BendigiriISPMAIndia
 
No more Dockerfiles? Buildpacks to help you ship your image!
No more Dockerfiles? Buildpacks to help you ship your image!No more Dockerfiles? Buildpacks to help you ship your image!
No more Dockerfiles? Buildpacks to help you ship your image!Anthony Dahanne
 
"Discovery and Delivery through Product IntelliGenAI framework" by Ramkumar A...
"Discovery and Delivery through Product IntelliGenAI framework" by Ramkumar A..."Discovery and Delivery through Product IntelliGenAI framework" by Ramkumar A...
"Discovery and Delivery through Product IntelliGenAI framework" by Ramkumar A...ISPMAIndia
 
The Age of AI: Elevating Experiences & Delivering Customer Value!
The Age of AI: Elevating Experiences & Delivering Customer Value!The Age of AI: Elevating Experiences & Delivering Customer Value!
The Age of AI: Elevating Experiences & Delivering Customer Value!ISPMAIndia
 
Welcome to AltTask - the nexus where innovation converges with empowerment!
Welcome to AltTask - the nexus where innovation converges with empowerment!Welcome to AltTask - the nexus where innovation converges with empowerment!
Welcome to AltTask - the nexus where innovation converges with empowerment!alttaskcom
 
The Game-Changer_ How Software Development Outsource Can Catapult Your Growth...
The Game-Changer_ How Software Development Outsource Can Catapult Your Growth...The Game-Changer_ How Software Development Outsource Can Catapult Your Growth...
The Game-Changer_ How Software Development Outsource Can Catapult Your Growth...emili denli
 
killing camp week 6 problem - maximal matrix.pdf
killing camp week 6 problem - maximal matrix.pdfkilling camp week 6 problem - maximal matrix.pdf
killing camp week 6 problem - maximal matrix.pdfssuser82c38d
 
Agile & Scrum, Certified Scrum Master! Crash Course
Agile & Scrum,  Certified Scrum Master! Crash CourseAgile & Scrum,  Certified Scrum Master! Crash Course
Agile & Scrum, Certified Scrum Master! Crash CourseRohan Chandane
 
Role of DevOps in SaaS product Development.pdf.pptx
Role of DevOps in SaaS product Development.pdf.pptxRole of DevOps in SaaS product Development.pdf.pptx
Role of DevOps in SaaS product Development.pdf.pptxMindInventory
 
P1 Inspection Types in Municity 5 Smartsheet
P1 Inspection Types in Municity 5 SmartsheetP1 Inspection Types in Municity 5 Smartsheet
P1 Inspection Types in Municity 5 SmartsheetMatthewTHawley
 

Recently uploaded (20)

killingcamp 광고삽입문제 풀이, killingcamp 광고삽입문제 풀이
killingcamp 광고삽입문제 풀이, killingcamp 광고삽입문제 풀이killingcamp 광고삽입문제 풀이, killingcamp 광고삽입문제 풀이
killingcamp 광고삽입문제 풀이, killingcamp 광고삽입문제 풀이
 
AUTOKEYUNLOCKER-BRANDS-SUPPORT-STANDARD-VERSION.pdf
AUTOKEYUNLOCKER-BRANDS-SUPPORT-STANDARD-VERSION.pdfAUTOKEYUNLOCKER-BRANDS-SUPPORT-STANDARD-VERSION.pdf
AUTOKEYUNLOCKER-BRANDS-SUPPORT-STANDARD-VERSION.pdf
 
Implementing Docker Containers with Windows Server 2019
Implementing Docker Containers with Windows Server 2019Implementing Docker Containers with Windows Server 2019
Implementing Docker Containers with Windows Server 2019
 
The Top Outages of 2023: Analyses and Takeaways
The Top Outages of 2023: Analyses and TakeawaysThe Top Outages of 2023: Analyses and Takeaways
The Top Outages of 2023: Analyses and Takeaways
 
Joseph Yoder : Being Agile about Architecture
Joseph Yoder : Being Agile about ArchitectureJoseph Yoder : Being Agile about Architecture
Joseph Yoder : Being Agile about Architecture
 
killingcamp longest common subsequence.pdf
killingcamp longest common subsequence.pdfkillingcamp longest common subsequence.pdf
killingcamp longest common subsequence.pdf
 
OpenChain AI Study Group - North America and Europe - 2024-02-20
OpenChain AI Study Group - North America and Europe - 2024-02-20OpenChain AI Study Group - North America and Europe - 2024-02-20
OpenChain AI Study Group - North America and Europe - 2024-02-20
 
Essence of Requirements Engineering: Pragmatic Insights for 2024
Essence of Requirements Engineering: Pragmatic Insights for 2024Essence of Requirements Engineering: Pragmatic Insights for 2024
Essence of Requirements Engineering: Pragmatic Insights for 2024
 
Machine Learning Basics for Dummies (no math!)
Machine Learning Basics for Dummies (no math!)Machine Learning Basics for Dummies (no math!)
Machine Learning Basics for Dummies (no math!)
 
Automation for Bonterra Impact Management (fka Apricot)
Automation for Bonterra Impact Management (fka Apricot)Automation for Bonterra Impact Management (fka Apricot)
Automation for Bonterra Impact Management (fka Apricot)
 
AI Product Management by Abhijit Bendigiri
AI Product Management by Abhijit BendigiriAI Product Management by Abhijit Bendigiri
AI Product Management by Abhijit Bendigiri
 
No more Dockerfiles? Buildpacks to help you ship your image!
No more Dockerfiles? Buildpacks to help you ship your image!No more Dockerfiles? Buildpacks to help you ship your image!
No more Dockerfiles? Buildpacks to help you ship your image!
 
"Discovery and Delivery through Product IntelliGenAI framework" by Ramkumar A...
"Discovery and Delivery through Product IntelliGenAI framework" by Ramkumar A..."Discovery and Delivery through Product IntelliGenAI framework" by Ramkumar A...
"Discovery and Delivery through Product IntelliGenAI framework" by Ramkumar A...
 
The Age of AI: Elevating Experiences & Delivering Customer Value!
The Age of AI: Elevating Experiences & Delivering Customer Value!The Age of AI: Elevating Experiences & Delivering Customer Value!
The Age of AI: Elevating Experiences & Delivering Customer Value!
 
Welcome to AltTask - the nexus where innovation converges with empowerment!
Welcome to AltTask - the nexus where innovation converges with empowerment!Welcome to AltTask - the nexus where innovation converges with empowerment!
Welcome to AltTask - the nexus where innovation converges with empowerment!
 
The Game-Changer_ How Software Development Outsource Can Catapult Your Growth...
The Game-Changer_ How Software Development Outsource Can Catapult Your Growth...The Game-Changer_ How Software Development Outsource Can Catapult Your Growth...
The Game-Changer_ How Software Development Outsource Can Catapult Your Growth...
 
killing camp week 6 problem - maximal matrix.pdf
killing camp week 6 problem - maximal matrix.pdfkilling camp week 6 problem - maximal matrix.pdf
killing camp week 6 problem - maximal matrix.pdf
 
Agile & Scrum, Certified Scrum Master! Crash Course
Agile & Scrum,  Certified Scrum Master! Crash CourseAgile & Scrum,  Certified Scrum Master! Crash Course
Agile & Scrum, Certified Scrum Master! Crash Course
 
Role of DevOps in SaaS product Development.pdf.pptx
Role of DevOps in SaaS product Development.pdf.pptxRole of DevOps in SaaS product Development.pdf.pptx
Role of DevOps in SaaS product Development.pdf.pptx
 
P1 Inspection Types in Municity 5 Smartsheet
P1 Inspection Types in Municity 5 SmartsheetP1 Inspection Types in Municity 5 Smartsheet
P1 Inspection Types in Municity 5 Smartsheet
 

Introduction of Semantic Web using NLP techniques.

  • 1. “HANDLING UNSTRUCTURED DATA FOR SEMANTIC WEB” Subject : Introduction of Semantic Web using NLP - Sandeep Wakchaure
  • 2. CONTENTS  Introduction  Data Forms  Architecture  Example  Conclusion  Reference
  • 3.  Problem Statement : To get knowledge or information on web search engine which does not contain any kind of irrelevant data.  Reason : In day to day life there large amount of unstructured data is going to stored on web, data warehouses , repository or on cloud.  Purpose of the System: To get the relevant data there should be technique which process the entered keywords, find the context and provide the relevant knowledge.
  • 4. INTRODUCTION  When a user search on web i.e. retrieving data through search engine, the results contains the large amount of data which are not user’s required information.  In this case keywords search techniques is fail here, to get required information with the huge amount of information.  Hence there is need to move from original Web to the Semantic Web for fast related and precise information access.  Many fields of computer world such as Data Mining, Information Retrieval, Database Management System and NLP have been introduced with Semantic Web for machine supported data interpretation and process integration.
  • 5. DATA IN THE FORM OF : Structured Data- This data has structure in terms of grammar pattern and contextual relations. Unstructured Data- This data has not a specific structure but it may have grammar. Posted queries and answers on the page, advertisements, graphics, text, emails, presentations and so they are included in the unstructured data.
  • 6. TECHNIQUE USED Ontology : Ontology is a description of things that exist and how they relate to each other. It is a study of categories of things and their relation among them. The core part of Semantic Web is ontologies, which defines the relationship between related entities, which achieved using  RDF(S) (Resource Description Framework/Schema) and  OWL (Web Ontology Languages) Ontologies and reasoning rules are applied to reason about data and infer new information. Rules are nothing but some condition or restriction to be applied on data to draw some facts. In fact, Semantic Web is like a collection of related and clustered facts.
  • 7. For finding pattern from these sources, pre-processing of the source documents required which is supported by the NLP techniques. The techniques are like, 1.Stemming (finding stem) 2.Removing suffixes and prefixes 3.Lemmatization for replacing inflected word with its base form, 4. Part of Speech (POS) tagging for finding grammar category of language - such as Noun, pronoun, adverb, adjective, proposition Using ontologies with NLP, understanding of natural language through systems become smarter enough to make inference and respond with defined and relevant result what a user requests. CONT…
  • 10. EXAMPLE : Dependency graph for sentence : “on-screen keyboard displays a virtual keyboard on computer-screen”
  • 11. CONCLUSION The goal of the system is to automate the software agents for the retrieving relevant and required information or data rather than providing the massive unrelated data. NLP techniques with the Semantic web provide the capability to turning the original web to Semantic Web while dealing with a combination of structured and unstructured data.
  • 12. REFERENCES [1] Gharehchopogh FS, Khalifelu ZA ; Analysis and Evaluation of Unstructured Data: Text Mining versus Natural Language Processing. Application of Information and Communication Technologies (AICT), 5th International Conference, 2011 [2] https://en.wikipedia.org/ [3] Fortuna B, Grobelnik M, Mladenić D; OntoGen: Semi-automatic Ontology Editor. Proceedings of HCI, 2007;309-318 [4] https://www.quora.com/