Mercurial > hg > musicweb-iswc2016
changeset 31:eb58bf95743b
after merge
| author | mariano |
|---|---|
| date | Sat, 30 Apr 2016 22:04:41 +0100 |
| parents | b65b3d984066 (diff) 8d1d5829dc2a (current diff) |
| children | 871ecf98047f |
| files | musicweb.tex |
| diffstat | 2 files changed, 314 insertions(+), 117 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/musicweb.bib Sat Apr 30 22:04:41 2016 +0100 @@ -0,0 +1,166 @@ + + +@inproceedings{song2012, + title={A survey of music recommendation systems and future perspectives}, + author={Song, Yading and Dixon, Simon and Pearce, Marcus}, + booktitle={9th International Symposium on Computer Music Modeling and Retrieval}, + year={2012} +} +@article{su2009, + title={A survey of collaborative filtering techniques}, + author={Su, Xiaoyuan and Khoshgoftaar, Taghi M}, + journal={Advances in artificial intelligence}, + volume={2009}, + pages={4}, + year={2009}, + publisher={Hindawi Publishing Corp.} +} + +@INPROCEEDINGS{Aucoutourier2002, + author = {J. J. Aucouturier and F. Pachet}, + title = {Music Similarity Measures: What is the Use.}, + booktitle = {ISMIR}, + year = {2002} +} + + + +@inproceedings{Kim2010, + title={Music emotion recognition: A state of the art review}, + author={Kim, Youngmoo E and Schmidt, Erik M and Migneco, Raymond and Morton, Brandon G and Richardson, Patrick and Scott, Jeffrey and Speck, Jacquelin A and Turnbull, Douglas}, + booktitle={Proc. ISMIR}, + pages={255--266}, + year={2010}, + organization={Citeseer} +} + + +@book{Celma2010, + author = {\`O.~Celma}, + title = {Music Recommendation and Discovery:The Long Tail, Long Fail, and Long Play in the Digital Music Space.}, + publisher = {Springer Verlag}, + city = {Heidelberg}, + year = 2010 +} + +@article{Zhou2004, + author = {S. Zhou and R. J. Mondrag\'on}, + title = {The rich-club phenomenon in the Internet topology.}, + journal = {Communications Letters, IEEE}, + year = 2004 +} + +@INPROCEEDINGS{Lee2015, + author = {D. Jennings}, + title = {Understanding users of commercial music services through personas: design implications.}, + booktitle = {Proceedings of the 16th ISMIR Conference}, + year = {2015} +} + + +@book{Jennings2007, + author = "D. Jennings", + title = "Net, Blogs and Rock ’n’ Rolls: How Digital Discovery Works and What It Means for Consumers.", + publisher = "Nicholas Brealey Pub.", + year = 2007 +} + + +@article{Pachet2005, + author = "F. Pachet", + title = "Knowledge management and musical metadata.", + journal = "Encyclopedia of Knowledge Management", + publisher = "Idea Group", + editor = "Schwartz, D.", + year = 2005 +} + + +@article{Marchioni2006, + author = "Gary Marchionini", + title = "Exploratory Search: from Finding to Understanding", + journal = "COMMUNICATIONS OF THE ACM", + volume = "49", + number = "9", + year = 2006 +} + + \bibitem{FazekasRJS10_OMRAS2} + G.~Fazekas, Y.~Raimond, K.~Jakobson, and M.~Sandler. + \newblock An overview of semantic web activities in the {OMRAS2} project. + \newblock {\em Journal of New Music Research (JNMR)}, 39(4), 2010. + +@inproceedings{Porter:ISMIR:15, + title={Acousticbrainz: a community platform for gathering music information obtained from audio}, + author={Porter, Alastair and Bogdanov, Dmitry and Kaye, Robert and Tsukanov, Roman and Serra, Xavier}, + booktitle={International Society for Music Information Retrieval (ISMIR’15) Conference}, + year={2015} +} + +@inproceedings{DBLP:conf/ismir/RaimondASG07, + title={The Music Ontology.}, + author={Raimond, Yves and Abdallah, Samer A and Sandler, Mark B and Giasson, Frederick}, + booktitle={ISMIR}, + pages={417--422}, + year={2007}, + organization={Citeseer} +} + +@inproceedings{Suchanek:WWW:2007, + title={Yago: A core of semantic knowledge unifying wordnet and wikipedia}, + author={Fabian, MS and Gjergji, K and Gerhard, W}, + booktitle={16th International World Wide Web Conference, WWW}, + pages={697--706}, + year={2007} +} + \bibitem{Suchanek:WWW:2007} + F.~Suchanek, G.~Kasneci, and G.~Weikum + \newblock YAGO: A Core of Semantic Knowledge Unifying WordNet and Wikipedia. + \newblock In {\em Proceedings of the 16th international World Wide Web conference, May 8–12, 2007, Banff, Alberta, Canada.}, 2007. + +@INPROCEEDINGS{Oren2006, + author = {E. Oren and R. Delbru and S. Decker}, + title = {Extending faceted navigation for rdf data.}, + booktitle = {ISWC}, + pages = {559–572}, + year = 2006 +} + +@article{Lamb2013, + author = {S. Lamb, K. Graling and E. E. Wheeler}, + title = {‘Pole-arized’ discourse: An analysis of responses to Miley Cyrus’s Teen Choice Awards pole dance.}, + journal = {Feminism Psychology}, + volume = "23", + year = 2013 +} + +@article{moliterno2012, + title={What Riot? Punk Rock Politics, Fascism, and Rock Against Racism}, + author={Moliterno, Alessandro G}, + journal={Student Pulse}, + volume={4}, + number={01}, + year={2012} +} + + +@book{manning1999, + title={Foundations of statistical natural language processing}, + author={Manning, Christopher D and Sch{\"u}tze, Hinrich}, + volume={999}, + year={1999}, + publisher={MIT Press} +} + +@article{wong2012, + title={Ontology learning from text: A look back and into the future}, + author={Wong, Wilson and Liu, Wei and Bennamoun, Mohammed}, + journal={ACM Computing Surveys (CSUR)}, + volume={44}, + number={4}, + pages={20}, + year={2012}, + publisher={ACM} +} + +
--- a/musicweb.tex Sat Apr 30 21:45:56 2016 +0100 +++ b/musicweb.tex Sat Apr 30 22:04:41 2016 +0100 @@ -179,6 +179,13 @@ \section{Background}\label{sec:background} \begin{itemize} \item related work + \begin{itemize} + \item http://musikipedia.org/ MUSIKIPEDIA, some paper by the guy who made it, Mohamed Sordo + \item Pachet's thing on metadata? + \item Kurt's thesis on the similarity ontology + \item Phuong Nguyen, Paolo Tomeo, Tommaso Di Noia and Eugenio Di Sciascio: Content-based recommendations via DBpedia and Freebase + \item music recommendation dbpedia + \end{itemize} \item very brief intro to the role of music related data sources on the web and what they are \end{itemize} @@ -186,9 +193,10 @@ 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 keys. It does this by pulling data from several different web knowledge content resources and presenting them for the user to navigate in a faceted manner\cite{Marchioni2006}. The listener can begin his journey by choosing or searching for an artist (fig. \ref{fig:front_page}). The application offers youtube videos, audio streams, photographs and album covers, as well as the artist's biography (fig. \ref{fig:ella_page}) The page also includes many box widgets with links to artists who are related to the current artist in different, and sometimes unexpected and surprising ways\ref{fig:ella_links}). The user can then click on any of these artists and the search commences again, exploring a web of artists further and further. -vspace{-10pt} -\begin{figure} - \centering + + +%\begin{figure*} +% \centering %% \begin{minipage}[b]{.48\textwidth} %% \includegraphics[height=8cm]{graphics/front_page.png} %% \caption{Front page} @@ -201,27 +209,51 @@ %% \label{fig:ella_page} %% \end{minipage} %% \end{figure} - \begin{subfigure}[t]{0.45\textwidth} - \includegraphics[height=6cm]{graphics/front_page.png} + + %% \begin{subfigure}[t]{0.45\textwidth} + %% \centering + %% \hspace{-1.5em}\includegraphics[height=6cm]{graphics/front_page.png} + %% \caption{Front page} + %% \label{fig:front_page} + %% \end{subfigure} + %% \hspace{0.4cm} + %% \begin{subfigure}[t]{0.45\textwidth} + %% \centering + %% \hspace{1.5em}\includegraphics[height=6cm]{graphics/ella_page.png} + %% \caption{Ella} + %% \label{fig:ella_page} + %% \end{subfigure} + + %% \vspace{1.5cm} + %% \begin{subfigure}{\textwidth} + %% \hspace{0.5em}\includegraphics[width=\textwidth]{graphics/ella_links.png} + %% \caption{Discovered artists} + %% \end{subfigure} + %% \caption{MusicWeb interface} + %% \label{fig:ella_links} +%\end{figure*} + + \begin{figure}[!ht] + \centering + \hspace{-1.5em}\includegraphics[width=\textwidth, height=6cm]{graphics/front_page.png} \caption{Front page} \label{fig:front_page} - \end{subfigure} - \hspace{1cm} - \begin{subfigure}[t]{0.45\textwidth} - \includegraphics[height=6cm]{graphics/ella_page.png} + \end{figure} + \hspace{0.4cm} + \begin{figure}[!ht] + \centering + \includegraphics[width=\textwidth, height=6cm]{graphics/ella_page.png} \caption{Ella} \label{fig:ella_page} - \end{subfigure} + \end{figure} + - \vspace{0.5cm} - \begin{subfigure}{\textwidth} - \includegraphics[width=\textwidth]{graphics/ella_links.png} + \begin{figure} + \hspace{0.5em}\includegraphics[width=\textwidth, height=6cm]{graphics/ella_links.png} \caption{Discovered artists} - \end{subfigure} - \caption{MusicWeb interface} \label{fig:ella_links} -\end{figure} - + \end{figure} + MusicWeb was originally conceived as a platform for collating metadata about music artists using already available online linked data resources. The core functionality of the platform relies on available SPARQL endpoints sa well as various commercial and community-run APIs. More recently, novel services complement the platform to provide alternative ways to forge connections using natural language processing and machine learning methods. %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, a playlist of online audio and a selection of Youtube videos. Further it provides lists of categories linking each artist to other similar 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, content-based music information retrieval similarity metrics and proximity measures in a 2-dimensional mood space. \begin{figure}[!ht] @@ -264,14 +296,14 @@ %% 4.1 Socio-cultiral linkage (using linked data) %% 4.2 Artist similarity by NLP [needs a better subtitle] %% 4.3 Artist similarity by features [i can write this part] -Music does not lend itself easily to categorisation. There are many ways in which artist can be, and in fact are, considered to be related. Similarity may refer to whether artists' songs sound similar, or are considered to be in the same style or genre. But it may also mean that they are followed by people from similar social backgrounds or political inclinations, or similar ages; or perhaps they are similar because they have played together, or participated in the same event, or their songs touch on similar themes. Linked data facilitates faceted searching and displaying of information\cite{Oren2006}: an artist may be similar to many other artists in one of the ways just mentioned, and to a completely different plethora of artists in other senses, all of which might contribute to music discovery. Semantic web technologies can help us gather different facets of data and shape them into representations of knowledge. MusicWeb does this by searching similarities in three different domains: socio-cultural, research and journalistic literature and content-based linkage. +Music does not lend itself easily to categorisation. There are many ways in which artist can be, and in fact are, considered to be related. Similarity may refer to whether artists' songs sound similar, or are perceived to be in the same style or genre. But it may also mean that they are followed by people from similar social backgrounds or political inclinations, or similar ages; or perhaps they are similar because they have played together, or participated in the same event, or their songs touch on similar themes. Linked data facilitates faceted searching and displaying of information\cite{Oren2006}: an artist may be similar to many other artists in one of the ways just mentioned, and to a completely different plethora of artists in other senses, all of which might contribute to music discovery. Semantic web technologies can help us gather different facets of data and shape them into representations of knowledge. MusicWeb does this by searching similarities in three different domains: socio-cultural, research and journalistic literature and content-based linkage. \subsection{Socio-cultural linkage} -NOTE: not sure about this. Do we consider the dbpedia queries to be socio-cultural? or the collaborates-with in musicbrainz? +NOTE: not sure about this. Do we consider the dbpedia queries to be socio-cultural? or the collaborates-with in musicbrainz? Or do you (George) mean something like the introduction just above? \subsection{Similarity in the literature} -Artists tend to be regarded as similar when writing about certain topics. For example: a psychologist interested in self-image during adolescence might want to research the impact of artists like Miley Cyrus or Rihanna on young teenagers\cite{Lamb2013}. Or a historian researching class politics might write about The Sex Pistols and John Lennon\cite{Moliterno2012}. The starting point is a large database of 100,000 artists. MusicWeb searches and collects texts which mention each artist from several sources and carries out semantic analysis to identify such connections between artists and higher-level topics. There are two main sources of texts: +Often artist share a connection through literature topics. For example: a psychologist interested in self-image during adolescence might want to research the impact of artists like Miley Cyrus or Rihanna on young teenagers\cite{Lamb2013}. Or a historian researching class politics in the UK might write about The Sex Pistols and John Lennon\cite{Moliterno2012}. In order to extract these relations one must mine the data from texts using natural language processing. Our starting point is a large database of 100,000 artists. MusicWeb searches several sources and collects texts that mention each artist. It then carries out semantic analysis to identify connections between artists and higher-level topics. There are two main sources of texts: \begin{enumerate} -\item Research articles. There are various web resources that allow querying their research literature databases. MusicWeb uses mendeley\footnote{http://dev.mendeley.com/} and elsevier\footnote{http://dev.elsevier.com/}. Both resources offer managed and largely curated data and search possibilities include keywords, authors and disciplines. Data comprehension varies, but most often it features an array of keywords, an abstract, readership categorised according to discipline and sometimes the article itself. +\item Research articles. There are various web resources that allow querying their research literature databases. MusicWeb uses mendeley\footnote{\url{http://dev.mendeley.com/}} and elsevier\footnote{\url{http://dev.elsevier.com/}}. Both resources offer managed and largely curated data and search possibilities include keywords, authors and disciplines. Data comprehension varies, but most often it features an array of keywords, an abstract, readership categorised according to discipline and sometimes the article itself. \item Online publications, such as newspapers, music magazines and blogs focused on music. This is non-managed, non-curated data, it must be extracted from the body of the text. The data is accessed after having crawled websites searching for keywords or tags in the title, and then scraped. External links contained in the page are also followed. \end{enumerate} Many texts contain references to an artist name without actually being relevant to MusicWeb. A search for Madonna, for example, can yield many results from the fields of sculpture, art history or religion studies. The first step is to model the relevance of the text, and discard texts which are of no interest to music discovery. This is done through a two stage process: @@ -283,7 +315,7 @@ \begin{figure}[!ht] \centering - \includegraphics[scale=0.5]{graphics/article_graph.pdf} + \includegraphics[scale=0.4]{graphics/article_graph.pdf} \caption{Article graph} \label{fig:article_graph} @@ -291,7 +323,7 @@ Texts (or abstracts, in the case of research publications where the body is not available) are subjected to semantic analysis. It is first tokenised and a bag of words is extracted from it. This bag of words is used to query the alchemy\footnote{AlchemyAPI is used under license from IBM Watson.} language analysis service for: \begin{itemize} \item Named entity recognition. The entity recogniser provides a list of names that appear mentioned in the text together with a measure of relevance. They can include toponyms, institutions, publications and persons. MusicWeb is interested in identifying artists, so every person mentioned is checked against the database. If the person is not included in MusicWeb's database then three resources are checked: dbpedia, musicbrainz and freebase. All three resources identify musicians using the yago ontology. It is important to align the artist properly, since the modeling process is largely unsupervised, and wrong identifications can skew the model. Musicians identified in texts are stored and linked to the artist that originated the query. MusicWeb then offers a link to either of them as ``appearing together in article''. -\item Keyword extraction. Non-managed texts and research that don't include tags or keywords. Keywords are checked against wordnet for hypernyms and stored\cite{Agirre2004}. Artists that share keywords or hypernyms are considered to be relevant to the same topic in the literature. +\item Keyword extraction. Non-managed texts and research that don't include tags or keywords. Keywords are checked against wordnet for hypernyms and stored. Artists that share keywords or hypernyms are considered to be relevant to the same topic in the literature. \end{itemize} MusicWeb also offers links between artists who appear in different articles by the same author, as well as in the same journal. @@ -314,127 +346,126 @@ % % ---- Bibliography ---- % - \vspace{-1em}\begin{thebibliography}{5} - % + %% \vspace{-1em}\begin{thebibliography}{5} + %% % - \bibitem{Song2012} - Y.~Song, S.~Dixon and M.~Pearce. - \newblock A survey of music recommendation systems and future perspectives - \newblock In {\em Proceedings of the 9th International Symposium on Computer Music Modelling and Retrieval}, 2012. + %% \bibitem{Song2012} + %% Y.~Song, S.~Dixon and M.~Pearce. + %% \newblock A survey of music recommendation systems and future perspectives + %% \newblock In {\em Proceedings of the 9th International Symposium on Computer Music Modelling and Retrieval}, 2012. - \bibitem{Su2009} - X.~Su and T. M. ~Khoshgoftaar. - \newblock A Survey of Collaborative Filtering Techniques. - \newblock In {\em Advances in Artificial Intelligence,(Section 3):1–19}, 2009. + %% \bibitem{Su2009} + %% X.~Su and T. M. ~Khoshgoftaar. + %% \newblock A Survey of Collaborative Filtering Techniques. + %% \newblock In {\em Advances in Artificial Intelligence,(Section 3):1–19}, 2009. - \bibitem{Aucoutourier2002} - J. J.~Aucouturier and F~Pachet. - \newblock Music Similarity Measures: What is the Use. - \newblock In {\em Proceedings of the ISMIR, pages 157–163}, 2002. + %% \bibitem{Aucoutourier2002} + %% J. J.~Aucouturier and F~Pachet. + %% \newblock Music Similarity Measures: What is the Use. + %% \newblock In {\em Proceedings of the ISMIR, pages 157–163}, 2002. - \bibitem{Kim2010} - Y.E.~Kim, E.M.~Schmidt, R.~Migneco, B.G.~Morton, P.~Richardson, J.~Scott, J.A.~Speck and D.~Turnbull. - \newblock Music Emotion Recognition: A State of the Art Review. - \newblock In {\em Proc. of the 11th Intl. Society for Music Information Retrieval (ISMIR) Conf}, 2010. + %% \bibitem{Kim2010} + %% Y.E.~Kim, E.M.~Schmidt, R.~Migneco, B.G.~Morton, P.~Richardson, J.~Scott, J.A.~Speck and D.~Turnbull. + %% \newblock Music Emotion Recognition: A State of the Art Review. + %% \newblock In {\em Proc. of the 11th Intl. Society for Music Information Retrieval (ISMIR) Conf}, 2010. - \bibitem{Celma2010} - \`O.~Celma - \newblock Music Recommendation and Discovery:The Long Tail, Long Fail, and Long Play in the Digital Music Space. - \newblock Springer Verlag, Heidelberg, 2010. + %% \bibitem{Celma2010} + %% \`O.~Celma + %% \newblock Music Recommendation and Discovery:The Long Tail, Long Fail, and Long Play in the Digital Music Space. + %% \newblock Springer Verlag, Heidelberg, 2010. - \bibitem{Zhou2004} - S.~Zhou and R. J.~Mondrag\'on - \newblock The rich-club phenomenon in the Internet topology - \newblock In {\em Communications Letters, IEEE}, 2004 + %% \bibitem{Zhou2004} + %% S.~Zhou and R. J.~Mondrag\'on + %% \newblock The rich-club phenomenon in the Internet topology + %% \newblock In {\em Communications Letters, IEEE}, 2004 - \bibitem{Lee2015} - J. H.~Lee and R.~Price - \newblock Understanding users of commercial music services through personas: design implications. - \newblock In {\em Proceedings of the 16th ISMIR Conference}, M\'alaga, Spain, 2015 + %% \bibitem{Lee2015} + %% J. H.~Lee and R.~Price + %% \newblock Understanding users of commercial music services through personas: design implications. + %% \newblock In {\em Proceedings of the 16th ISMIR Conference}, M\'alaga, Spain, 2015 - \bibitem{Jennings2007} - D.~Jennings. - \newblock Net, Blogs and Rock ’n’ Rolls: How Digital Discovery Works and What It Means for Consumers. - \newblock Nicholas Brealey Pub., 2007 + %% \bibitem{Jennings2007} + %% D.~Jennings. + %% \newblock Net, Blogs and Rock ’n’ Rolls: How Digital Discovery Works and What It Means for Consumers. + %% \newblock Nicholas Brealey Pub., 2007 - \bibitem{Pachet2005} - F.~Pachet - \newblock Knowledge management and musical metadata. - \newblock In {\em Encyclopedia of Knowledge Management}, Schwartz, D. Ed. Idea Group, 2005 + %% \bibitem{Pachet2005} + %% F.~Pachet + %% \newblock Knowledge management and musical metadata. + %% \newblock In {\em Encyclopedia of Knowledge Management}, Schwartz, D. Ed. Idea Group, 2005 - \bibitem{Marchioni2006} - G.~Marchionini - \newblock Exploratory search: from finding to understanding. - \newblock In {\em COMMUNICATIONS OF THE ACM}, 49(9), 2006 + %% \bibitem{Marchioni2006} + %% G.~Marchionini + %% \newblock Exploratory search: from finding to understanding. + %% \newblock In {\em COMMUNICATIONS OF THE ACM}, 49(9), 2006 - \bibitem{FazekasRJS10_OMRAS2} - G.~Fazekas, Y.~Raimond, K.~Jakobson, and M.~Sandler. - \newblock An overview of semantic web activities in the {OMRAS2} project. - \newblock {\em Journal of New Music Research (JNMR)}, 39(4), 2010. + %% \bibitem{FazekasRJS10_OMRAS2} + %% G.~Fazekas, Y.~Raimond, K.~Jakobson, and M.~Sandler. + %% \newblock An overview of semantic web activities in the {OMRAS2} project. + %% \newblock {\em Journal of New Music Research (JNMR)}, 39(4), 2010. - \bibitem{Porter:ISMIR:15} - A.~Porter, D.~Bogdanov, R.~Kaye, R.~Tsukanov, and X.~Serra. - \newblock Acousticbrainz: a community platform for gathering music information - obtained from audio. - \newblock In {\em 16th International Society for Music Information Retrieval - (ISMIR) Conference}, 2015. + %% \bibitem{Porter:ISMIR:15} + %% A.~Porter, D.~Bogdanov, R.~Kaye, R.~Tsukanov, and X.~Serra. + %% \newblock Acousticbrainz: a community platform for gathering music information + %% obtained from audio. + %% \newblock In {\em 16th International Society for Music Information Retrieval + %% (ISMIR) Conference}, 2015. - \bibitem{DBLP:conf/ismir/RaimondASG07} - Y~Raimond, S.~Abdallah, M.~Sandler, and F.~Giasson. - \newblock The music ontology. - \newblock In {\em Proceedings of the 8th International Conference on Music - Information Retrieval, ISMIR 2007, Vienna, Austria, September 23-27}, 2007. + %% \bibitem{DBLP:conf/ismir/RaimondASG07} + %% Y~Raimond, S.~Abdallah, M.~Sandler, and F.~Giasson. + %% \newblock The music ontology. + %% \newblock In {\em Proceedings of the 8th International Conference on Music + %% Information Retrieval, ISMIR 2007, Vienna, Austria, September 23-27}, 2007. - \bibitem{Suchanek:WWW:2007} - F.~Suchanek, G.~Kasneci, and G.~Weikum - \newblock YAGO: A Core of Semantic Knowledge Unifying WordNet and Wikipedia. - \newblock In {\em Proceedings of the 16th international World Wide Web conference, May 8–12, 2007, Banff, Alberta, Canada.}, 2007. + %% \bibitem{Suchanek:WWW:2007} + %% F.~Suchanek, G.~Kasneci, and G.~Weikum + %% \newblock YAGO: A Core of Semantic Knowledge Unifying WordNet and Wikipedia. + %% \newblock In {\em Proceedings of the 16th international World Wide Web conference, May 8–12, 2007, Banff, Alberta, Canada.}, 2007. - \bibitem{Oren2006} - E.~Oren, R.~ Delbru, and S.~Decker - \newblock Extending faceted navigation for rdf data. - \newblock In {\em ISWC, 559–572}, 2006 + %% \bibitem{Oren2006} + %% E.~Oren, R.~ Delbru, and S.~Decker + %% \newblock Extending faceted navigation for rdf data. + %% \newblock In {\em ISWC, 559–572}, 2006 - \bibitem{Lamb2013} - S.~Lamb, K.~Graling and E. E. Wheeler - \newblock ‘Pole-arized’ discourse: An analysis of responses to Miley Cyrus’s Teen Choice Awards pole dance. - \newblock In {\em Feminism Psychology vol. 23}, May 2013 + %% \bibitem{Lamb2013} + %% S.~Lamb, K.~Graling and E. E. Wheeler + %% \newblock ‘Pole-arized’ discourse: An analysis of responses to Miley Cyrus’s Teen Choice Awards pole dance. + %% \newblock In {\em Feminism Psychology vol. 23}, May 2013 - \bibitem{Moliterno2012} - A. G.~Moliterno - \newblock What Riot? Punk Rock Politics, Fascism, and Rock Against Racism. - \newblock Published online: \url{http://www.studentpulse.com/articles/612/what-riot-punk-rock-politics-fascism-and-rock-against-racism}, 2012 + %% \bibitem{Moliterno2012} + %% A. G.~Moliterno + %% \newblock What Riot? Punk Rock Politics, Fascism, and Rock Against Racism. + %% \newblock Published online: \url{http://www.studentpulse.com/articles/612/what-riot-punk-rock-politics-fascism-and-rock-against-racism}, 2012 - \bibitem{Manning1999} - C.~Manning and H.~Sch\"utze - \newblock Foundations of Statistical Natural Language Processing. - \newblock MIT Press, Cambridge, MA., 1999 + %% \bibitem{Manning1999} + %% C.~Manning and H.~Sch\"utze + %% \newblock Foundations of Statistical Natural Language Processing. + %% \newblock MIT Press, Cambridge, MA., 1999 - \bibitem{Agirre2004} - E.~Agirre, E.~Alfonseca and O.~Lopez de Lacalle - \newblock Approximating Hierarchy-Based Similarity for WordNet Nominal Synsets using Topic Signatures. - \newblock In {\em Proceedings of the Second Global WordNet Conference, pp. 15-22}, 2004. - \bibitem{Wong2012} - W.~Wong, W.~Liu and M.~Bennamoun - \newblock Ontology Learning from Text: A Look Back and into the Future - \newblock In {\em ACM Comput. Surv. 44, 4}, 2012 + %% \bibitem{Wong2012} + %% W.~Wong, W.~Liu and M.~Bennamoun + %% \newblock Ontology Learning from Text: A Look Back and into the Future + %% \newblock In {\em ACM Comput. Surv. 44, 4}, 2012 - \bibitem{Landauer1998} - T.~Landauer, P.~Folt, and D.~Laham. - \newblock An introduction to latent semantic analysis - \newblock In {\em Discourse processes, 25}, 1998 + %% \bibitem{Landauer1998} + %% T.~Landauer, P.~Folt, and D.~Laham. + %% \newblock An introduction to latent semantic analysis + %% \newblock In {\em Discourse processes, 25}, 1998 - \bibitem{Blei2012} - D.~Blei, A.~ Ng, and M.I.~Jordan. - \newblock Latent Dirichlet Allocation. - \newblock In {\em Journal of Machine Learning Research, 3(4-5), 993–1022}, 2012 + %% \bibitem{Blei2012} + %% D.~Blei, A.~ Ng, and M.I.~Jordan. + %% \newblock Latent Dirichlet Allocation. + %% \newblock In {\em Journal of Machine Learning Research, 3(4-5), 993–1022}, 2012 - \end{thebibliography} + %% \end{thebibliography} + + \bibliographystyle{plain} + \bibliography{musicweb} \end{document}