Knowledge and Media 2012 Lecture 10: Research proposal QA


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Tips on how to write your research proposal, review your fellow students' proposals, and prepare for your lightning talk.

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Knowledge and Media 2012 Lecture 10: Research proposal QA

  1. 1. RESEARCH PROPOSAL QA KM 2012 Lecture 10Friday, November 30, 12
  2. 2. OVERVIEW Research Proposal Finding your topic Defining your research question Writing it up Research Poster: Communicating your idea visually Peer Review: Providing positive feedback Lightning Talk: Condense your idea LogisticsFriday, November 30, 12
  3. 3. RESEARCH PROPOSALFriday, November 30, 12
  4. 4. FINDING YOUR TOPIC Which topics in the course did you like? Which problem should be solved? Think out of the box, what have you seen in the literature in other lectures that may be of use here? Sleep on it. Am I still excited about it? OK, go to step 2Friday, November 30, 12
  5. 5. INSPIRATIONFriday, November 30, 12
  6. 6. INSPIRATIONFriday, November 30, 12
  7. 7. INSPIRATIONFriday, November 30, 12
  8. 8. DEFINING YOUR RQ Dig into the literature, has my problem been researched before? If so, what techniques have been used to deal with it? Is my proposed solution novel and viable? No literature? Ask yourself if the problem you want to investigate is relevant.Friday, November 30, 12
  9. 9. WRITING IT UP Make sure the proposal is self-contained, i.e., any peer reviewer should understand your main problem and proposed solution by just reading your document Use examples, or figures to explain your proposal Don’t forget any parts (literature etc.)Friday, November 30, 12
  10. 10. YOUR RESEARCH POSTERFriday, November 30, 12
  11. 11. VISUALISING YOUR IDEA A picture says more than a thousand words Come up with a catchy example Don’t paste text from your proposal into your poster!Friday, November 30, 12
  12. 12. Knowledge & Media Conference 2011 December 12th VU University Amsterdam Juicing the LOD Cloud with WordNet Use WordNet to Though at first glance it may seem as if there are many connections between data sources Use a validation metric suggest new links in the LOD Cloud, a more detailed look will show that most data sources are connected to determine the in the LOD Cloud to only one or two other data sources. This also follows from the LOD Cloud statistics. relevance of new links More than 50% of the data sources in the LOD Cloud link to no more than two other sources, and more than 66% of them link to no more than three other sources. Derive identifying terms Use WordNet as a semantic and relational from existing RDF Triples knowledge base to analyze the subjects, predicates and objects of existing triples in ▼ the LOD Cloud and propose new links between data items based on the linguistic Match these terms The number of data sets that link to 1, 2, 3, 4, 5, 6 to 10 or more than 10 other data sets relations defined in WordNet. Nouns, verbs, adjectives and adverbs are grouped into sets against synsets in of cognitive synonyms called synsets, each expressing a distinct concept. Synsets are WordNet interlinked by means of conceptual-semantic and lexical relations. ▼ Use synonymy hyponymy WordNet contains 3 major relation types and meronymy relations that could be utilized: Synonymy relations; relations between words that have similar ▼ meaning,  e.g.  ‘forest’  is  synonymous  to   ‘wood’.  Hyponymy relations; relations Suggest links based on between words that are sub concepts or super  concepts  of  each  other,  e.g.  ‘taxi’  is  a   distance in the linguistic sub  concept  of  ‘car’,  which  in  turn  is  a  sub   concept  of  ‘vehicle’.  Meronymy relations; WordNet relation and relations that define if words are sub concepts,  e.g.  ‘bumper’  is  a  part  of  ‘car’. matching percentage ▼ Use a filter for domain specific applications Ben A. Student VU University AmsterdamFriday, November 30, 12
  13. 13. PEER REVIEWFriday, November 30, 12
  14. 14. PROVIDING POSITIVE FEEDBACK Meant to help each other in improving the proposal Read critically, but fairly Provide detailed as well as high level comments to aid the author whose work you are reviewingFriday, November 30, 12
  15. 15. LIGHTNING TALKFriday, November 30, 12
  16. 16. CONDENSING YOUR IDEA Explain the core of your idea in one minute Don’t try to summarise your entire proposal Create a single slide to communicate your ideaFriday, November 30, 12
  17. 17. Try-on eyewear Serious gaming for opticiansFriday, November 30, 12
  18. 18. MusicWeesby Justin van discovery and recommendations using the Semantic Web Problem statement Research question • Enormous collections of music are available Can we create a system that generates personalized music online recommendations by using Semantic Web technologies and • To find new, possibly interseting music, currently available Linked Open Data? users can: We wan to: - Read reviews • help users discover new music that - Listen to lots of tracks fits personal taste - ... or use colleborative filtering services 20+ • combine collaborative filtering data, like: million songs expert-based data and high-level content based features • provide meaningful feedback on Text why items are suggested (Cohen and Fan, 2000) • intergrate with a (popular) existing Colleborative filtering methods have service several disadvantages: • compares on (very few) high level Methods metadeta properties • collect music related linked data and map it to the • content-based properties are Music Ontology (Raimond et al., 2007) ignored • build and evaluate recommendation methods • prone to a popularity bias; makes • determine what information on recommendations is useful to it unlikely for artists located in the the end-user ‘Long Tail’ to be ever recommend References • recommendations are not Casey, M., Veltkamp, R., Goto, M., Leman, M., Rhodes, C., and Slaney, M. (2008). Content-based music information retrieval: current direc- tions and future challenges. Proceedings of the IEEE, 96(4):668–696. Celma, O. and Cano, P. (2008). From hits to niches?: or how popular artists can bias music recommendation and discovery. In Proceedings transparent of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition, page 5. ACM. Cohen, W. and Fan, W. (2000). Web-collaborative filtering: Recommending music by crawling the web. Computer Networks, 33(1):685– The Top–737 artists accumulate 50% of total 698. playcounts (Celma and Cano, 2008). Raimond, Y., Abdallah, S., Sandler, M., and Giasson, F. (2007). The music ontology. In Proceedings of the International Conference on Music Information Retrieval, pages 417– 422. Citeseer.,, November 30, 12
  19. 19. Crowdsourcing for documentation and revitalization of endangered languages Language  embeds  knowledge… documenting sharing in the hands of the crowdFriday, November 30, 12
  20. 20. LOGISTICSFriday, November 30, 12
  21. 21. SUBMITTING TO EASYCHAIRFriday, November 30, 12
  22. 22. REVIEWINGFriday, November 30, 12
  23. 23. LIGHTNING TALK SLIDE Submit a PDF file with one single slide to the dropbox, named <LASTNAME>_slide.pdf Deadline: Friday 7 December 23:59 CET. Make sure the slide is in landscape mode and has at dimensions 1024x768 or greater with same proportionsFriday, November 30, 12
  24. 24. FINAL VERSION Process reviewers’ comments and lightning talk comments Explain your improvements in a response letter Deadline: Sunday 23 December 23:59 CET Resubmit using EasychairFriday, November 30, 12
  25. 25. QUESTIONS?Friday, November 30, 12