view musicweb.tex @ 8:d4d732b8b9cb

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. There are two main approaches to music recommendation\cite{Song2012}: the first is known as \emph{collaborative filtering}\cite{Su2009}, which recommends music items based on the choices of similar users. There are, however, limitations in user-preference based systems: recommendations based solely on user preference can easily lead to a ''rich-club phenomenon''\cite{Zhou2004}, in which short-tail popular music is heavily reinforced whereas most of the music available online is ignored and remains unknown\cite{Celma2010}. The second most significant model of music recommendation is based on audio content analysis, or \emph{music information retrieval}. This analysis extracts low to high-level audio features such as tempo, key, metric structure, melodic and harmonic sequences, instrument recognition and song segmentation, which are then used to measure music similarity\cite{Aucoutourier2002}, to carry out genre classification or to identify the mood of the song\cite{Kim2010}. Music discovery websites such as Last.fm\footnote{http://www.last.fm}, Allmusic\footnote{http://www.allmusic.com} or Pandora\footnote{http://www.pandora.com} have successfully developed hybrid systems which combine both approaches. 

\begin{itemize}
\item NOTE: The problem with this is that it does not offer music \emph{discovery}!
\item Music metadata can improve this
\item Pachet's patper on metadata identifies three different kinds of music metadata
  \begin{itemize}
  \item Editorial metadata
  \item Cultural metadata
  \item Acoustic metadata
  \end{itemize}
\end{itemize}



\section{Background}\label{sec:background}
\begin{itemize}
\item Information management
\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 ----
	%
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		%

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\end{document}