Knowledge and Media 2012 Lecture 10: Research proposal QA

RESEARCH PROPOSAL QA
KM 2012 Lecture 10

Friday, November 30, 12

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

Logistics


RESEARCH PROPOSAL


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 2


INSPIRATION


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.


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.)


YOUR RESEARCH POSTER


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!


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 Amsterdam
Friday, November 30, 12 b.a.student@vu.nl

PEER REVIEW


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 reviewing


LIGHTNING TALK


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 idea


Try-on eyewear
Serious gaming for opticians


MusicWees
by 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.
http://en.wikipedia.org/wiki/ITunes_Store#Music, http://en.wikipedia.org/wiki/Spotify
http://dbtune.org/


Crowdsourcing for documentation and
revitalization of endangered languages
Language embeds knowledge…

documenting
sharing

in the hands of the crowd


LOGISTICS


SUBMITTING TO EASYCHAIR


REVIEWING


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
proportions


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 Easychair


QUESTIONS?


Knowledge and Media 2012 Lecture 10: Research proposal QA

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Knowledge and Media 2012 Lecture 10: Research proposal QA