annotate musicweb.tex @ 6:1b88c2531512

intro to intro

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\begin{document}
	
\mainmatter

\title{MusicWeb: an open linked semantic platform for music metadata}

\author{Mariano Mora-Mcginity \and Alo Allik \and Gy\"orgy Fazekas \and Mark Sandler }
%

\institute{Queen Mary University of London, \\
\email{\{m.mora-mcginity, a.allik, g.fazekas, mark.sandler\}@qmul.ac.uk}}

\maketitle
	
\begin{abstract} 

% MusicWeb is a web site that provides users a browsing, searching and linking platform of music artist and group information by integrating open linked semantic metadata from various Semantic Web, music recommendation and social media data sources, including DBpedia.org, sameas.org, MusicBrainz, the Music Ontology, Last.FM, Youtube, and Echonest. The front portal includes suggested links to selected artists and a search functionality from where users can navigate to individual artists pages. Each artist page contains a biography, links to online audio and a video player with a side menu displaying a selection of Youtube videos. Further it provides lists of YAGO categories linking each artist to other artists by various commonalities such as style, geographical location, instrumentation, record label as well as more obscure categories, for example, artists who have received the same award, have shared the same fate, or belonged to the same organisation or religion. The artist connections are further enhanced by thematic analysis of journal articles and blog posts as well as content-based music information retrieval similarity measures.

This paper presents MusicWeb, a novel platform for linking music artists within a web-based application for discovering connections between them. MusicWeb provides a browsing experience using connections that are either extra-musical or tangential to music, such as the artists' political affiliation or social influence, or intra-musical, such as the artists' main instrument or most favoured musical key. The platform integrates open linked semantic metadata from various Semantic Web, music recommendation and social media data sources including DBpedia.org, sameas.org, MusicBrainz, the Music Ontology, Last.FM and Youtube as well as content-derived information. The front portal includes suggested links to selected artists and a search functionality from where users can navigate to individual artists pages. Each artist page contains a biography and links to online audio and a video resources. Connections are made using YAGO categories linking artist by various commonalities such as style, geographical location, instrumentation, record label as well as more obscure categories, for instance, artists who have received the same award, have shared the same fate, or belonged to the same organisation or religion. These connections are further enhanced by thematic analysis of journal articles and blog posts as well as content-based similarity measures focussing on high level musical categories.
	
\keywords{Semantic Web, Linked Open Data, music metadata, semantic audio analysis, music information retrieval }
\end{abstract}

\section{Introduction}\label{sec:introduction}
In recent years we have witnessed an explosion of information, a consequence of millions of users producing and consuming web resources. Researchers and industry have recognised the potential of this data, and have endeavoured to develop methods to handle such a vast amount of information: to understand and manage it, to transform into knowledge. Multimedia content providers have devoted a lot of energy to analysing consumer preference, in an effort to offer customised user experiences. Music stream services, for instance, carry out extensive analysis trying to identify patterns in user's listening habits, and researchers are striving to refine multimedia recommendation algorithms\cite{Song2012}. There are, however, limitations in user-preference based approaches: recommendations based solely on user preference can very easily lead to a ''rich-club phenomenon''\cite{Zhou2004}, in which the short-tail popular music is heavily reinforced whereas most of the music available online is ignored and remains unknown\cite{Celma2010}. Music recommendation systems such as 
\begin{itemize}
\item Why are we doing this?
  \begin{itemize}
  \item What does the application do?
  \item Why is this a good thing?
  \item Who is it good for?: User experience.
  \item Who is it potentially good for?
    
  \end{itemize}
\end{itemize}

\section{Background}\label{sec:background}
\begin{itemize}
\item Information management
\item Music data collection: Spotify has acquired Echonest for \$100 million.
\item Music recommendation systems: many recommendation systems are based on identifying trends in user listening patterns: it likely that a user who likes a particular artist will also like another artist because other users have shown this tendency. 
\item Some references to semantic web audio
\item Linked musicians: echonest, musicbrainz
\item Smart music

\end{itemize}
 
\section{MusicWeb: Yago linking}\label{sec:yago}
	
\section{MUSIC: linking by topic}\label{sec:music}
\begin{itemize}
\item Semantic analysis\cite{Landauer1998}
\item Topic modeling\cite{Blei2012}
\item Entity recognition
\item Hierarchical bayesian modeling
\item Authors, journals, keywords, tags
  
\end{itemize}
\section{Content-based information retrieval}\label{sec:mir}
	
\section{Discussion}\label{sec:discussion}
	
\section{Conclusions}\label{sec:conclusions}
	
	%
	% ---- Bibliography ----
	%
	\vspace{-1em}\begin{thebibliography}{5}
		%

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	\end{thebibliography}
	
\end{document}