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CMPE58H Project Progress Presentation QAPoint

CMPE58H Project Progress Presentation QAPoint. H.Tuğçe Özkaptan Gözde Kaymaz Serkan Kırbaş. http://code.google.com/p/qapoint/. Agenda. What is QAPoint? What are the main characteristics? Which technologies are used? QAPoint Architecture What have we done so far?. Let's remember.

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CMPE58H Project Progress Presentation QAPoint

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  1. CMPE58H Project Progress PresentationQAPoint H.Tuğçe Özkaptan Gözde Kaymaz Serkan Kırbaş http://code.google.com/p/qapoint/

  2. Agenda • What is QAPoint? • What are the main characteristics? • Which technologies are used? • QAPoint Architecture • What have we done so far?

  3. Let's remember...

  4. What is QAPoint? QAPoint is a location-based Q&A social network by which you can: • ask and drop your question marks on Google map • get answers for your previously asked questions • answer others' questions or recommended questions fitting with your interests • get cold answers from QAPoint semantic backend • rate the comments and thus the others • gain reputation according to your ratings 

  5. What are the main characteristics? Location: • Location information of a question will be gathered via map by using GPS on a mobile phone. • Location information of a question will represent at least district, user will be able to expand it to city or country. • Location information of a question will be added to the interests of a user. • User can provide his/her current location information when s/he gets online.

  6. What are the main characteristics? Interest: • Interests are related with the keywords in the questions and answers. • Interests are also related with the locations. • An interest will be added when it exceeds a predefined threshold value. • Questions will be recommended to the users according to these interest values.

  7. What are the main characteristics? Semantic web: • QAPoint will have own RDF store which also contains different RDF's colected from different semantic web sources. • When a question is asked, the main keywords in the question will be extracted and these keywords will be queried in semantic web sources by SPARQL. • By using semantic web, relations between different keywords will be reached. • Relations between the users and the keywords (interests) will be used in SNA. 

  8. What are the main characteristics? • Questions will be recommended according to: •  Current location (if exists) •  Interest •  Status (allowed/not allowed, online/offline) • Load balancing: Questions will not be asked to the same set of users, an algorithm for asking mixed set of users will be designed.

  9. What are the main characteristics? Answers: • Cold (previously given related answers) and hot (after the question is asked) answer • Like/dislike • When a user is satisfied, s/he will close the question and no more answers will be accepted. • Answers of a questions will be ranked according to the like/dislike amount. Reputation: • Users will gain reputation according to their answers. • Users with high reputation will gain more trust in the community and the probability to being recommended a question will increase.

  10. TRIPLESTORE There are two available large RDF dataset storage environment which are component of Jena: • SDB • TDB   While SDB works on relational databases, TDB uses a pure Java engine. After installation phase, we've decided to use TDB for storing and quering our RDF data.

  11. Turtle-Terse RDF Triple Language Since there are some methods available in TDB.Factory class to create models and datasets from ttl files, we needed the information about the ttl file format. The useful tutorial about ttl can be found in here

  12. TDB • TDB is a component of Jena for RDF storage and query, as well as the full range of Jena APIs. • TDB can be used as a high performance, non-transactional, RDF store on a single machine. • TDB is distributed from here •  It could also be found from wiki page that how to create models and/or RDF-datasets

  13. The Protégé-OWL  The Protégé editor is used for creating OWL files. You can define classes, class hierarchy, relations between classes, restriction on them ...etc! This editor can be downloaded from here The useful tutorial is  here

  14. The Stanford Parser • The parser of Standford NLP Group is used for processing the sentences entered by the users. • The subject of the sentence, the qualifiers of the subject and the prepositions are detected as a result of sentence parsing. • The Stanford Parser is distributed from here

  15. The Android SDK • The software which will wolk on mobile phones is developed by using Android SDK and Eclipse • aimed Android OS

  16. QAPoint Architecture

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