Timilar ppt

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Computing Semantic Similarity between two twitter users based on tweets -ppt

Computing Semantic Similarity between two twitter users based on tweets -ppt

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  • 1. TimilarFinding similarity between twitter users Semantic Web Project Amit Krishna Joshi 2011
  • 2. Key concepts Tweet Similarity Twitter Keywords Semantic Meta-data Normalized Google Distance
  • 3. PhasesExtract tweets and Extract Semantics Integrate and Designuser meta • Consume Alchemy web user Interface• Retrieve 100 most recent service to receive • Integrate all modules tweets semantic metadata of • Desing user initerface• Retrieve list of friends tweets and the links in tweets • Perform testing and friends• Extract links from each • Use cosine algorithm to tweet determine the similarity • Implement google similarity and computer aggregate similarity
  • 4. API and Web Services Twittter Rest API Get tweets Get friends and followers of a user Alchemy REST API: Get semantic meta-data for the tweets and contents of links in tweets Readablility API Clean up messy HTML before sending requests to Alchemy
  • 5. Semantic Meta-data html, internet, web, social network, semantic web FieldTerminology
  • 6. Workflow 1. Get tweets and user metadata 2. Extracts urls and get url contents Two twitter usernames 3. Clean up html contentsUser Timilar 4. Get Semantic Metadata 1. Tag Cloud 2. Similarity Chart 3. Similarity Score 5. Compute Keyword and Semantic Similarity using Cosine Algorithm 6. Compute Google Similarity for each pair of terms
  • 7. Algorithm Implementation Cosine Similarity Google Similarity:
  • 8. Google Similarity
  • 9. Project Timeline
  • 10. Enhancements Dynamic Similarity Automatic score generation for all friends Use Streaming API
  • 11. Demo – Disaster !!! Need of recorded Video Demos ☺
  • 12. DemoWeb Application Input – two twitter usernames Output – Tag cloud Keyword Similarity Semantic Similarity based on Alchemy API Aggregated Semantic Similarity using NGD
  • 13. Accomplishments Finding similarity between any two entities instance matching semantic associations through schemas/vocabularies/ontologies/classifications Metadata and ontologies for connecting disparate data Latent Semantics Similarity Algorithms Jaccard’s Coefficient Cosine Similarity Google Similarity
  • 14. Thanks!