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\begin{document}
%
% paper title
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\title{Using Semantic Web to Model Social Computing}


% author names and affiliations
% use a multiple column layout for up to three different
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\author{\IEEEauthorblockN{Kanghao Lu and WHO ELSE??? }
\IEEEauthorblockA{Decentralized Information Group\\
Computer Science and Artifical Intelligence Laboratory\\
Massachusetts Institute of Technology\\
Cambridge, MA 02139\\
Email: [kennyluck oshani ???]@csail.mit.edu}}

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%Homer Simpson\IEEEauthorrefmark{2},
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%Montgomery Scott\IEEEauthorrefmark{3} and
%Eldon Tyrell\IEEEauthorrefmark{4}}
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%Georgia Institute of Technology,
%Atlanta, Georgia 30332--0250\\ Email: see http://www.michaelshell.org/contact.html}
%\IEEEauthorblockA{\IEEEauthorrefmark{2}Twentieth Century Fox, Springfield, USA\\
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%Telephone: (800) 555--1212, Fax: (888) 555--1212}
%\IEEEauthorblockA{\IEEEauthorrefmark{4}Tyrell Inc., 123 Replicant Street, Los Angeles, California 90210--4321}}




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% make the title area
\maketitle


\begin{abstract}
%\boldmath
The Web connects people not just machines or documents. Interactions of people on the Web are just as complex as their interactions in the real life, which leads to a complex eco-system on the Web. Therfore, understanding and accounting for the social side on the Web is a vital aspect when it comes to encouraging the growth of the Web. Semantic Web provides a platform to connect people using ontologies like FOAF (Friend Of A Friend), model their interactions on web forums through ontologies such as SIOC (Semantically Interlinked Online Communities). These ontologies, together with many others, lay the groundwork to observe the social interactions of people to make useful observations and linkages. Although putting out the data out there may seem like a threat to an individual's privacy, the social good that can come out of this is immense. In this paper we provide several use cases expressed in a Semantic Web rule language called Notation 3 (N3) and a software agent implement based on a propagator network which can make useful deductions based on these rules and an input graph.

\end{abstract}

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\section{Introduction}

We introduce the idea of formally descibing a social computing service or an agent using rules grounded in Semantic Web technologies. These rules provide the basis for finding a resource, making an assertion, or even predict events that may happen in the future. It is needless to say about the benefits of having such outcomes based off of publicly available social data.

On the Semantic Web, data happens to be a first class citizen. Such data on the Semantic Web comes from different sources and are thus bound to be distributed. Therefore, services and agents based on the Semantic Web are guaranteed to be data-oriented and distributed.

In Section \ref{semantic-web} we describe some useful properties of the Semantic Web, in particular the Resource Description Framework (RDF)\cite{rdf}, and show why it is a good way of representing knowledge. In Section \ref{social-rules} we give a social computing mechanism using a formal Semantic Web language, namely Notation 3 (N3)\cite{n3}. In Section \ref{impl}, we describe an implementation of an agent which can make deductions based on the rules defined according to the some social interactions or situations. In Section \ref{related-work}, we compare our work with the related work in this area, and finally conclude the paper in a discussion of our results and our future work in section \ref{conclusion}.

%\hfill mds

%\hfill January 11, 2007

\section{Introduction to the Semantic Web} 
\label{semantic-web}

%Imagine you are on Facebook. You can make friends with people whom you may (or may not) know in the real world, you can wish a friend on her birthday, send a gift, post links to mulimedia content that you think would interest your friends, and do a myriad of activities which closely model things that you do in the real world. However, all that data are trapped within Facebook because it is not semantically annoatated nor it is not linking out.

Semantic Web is a Web of machine processable data, preferably RDF, linked together. The fundamental assumption of Semantic Web is that every important thing has a unique global name in the form of a Universal Resource Identifier (URI). This URI can be dereferenced to obtain a file. From this file, it may be possible to extract some RDF triples. A nonempty subset of these triples compose information about the thing, namely, the subject or the object of the thing.

The URI of a 'thing' could be thought of as a magical name with which you can get information about the thing, while other exsisting names or ids such as a name of a person or even the Social Security Number (SSN) does not incorporate any canonical method to retrieve information. The URI of a person, also called a Web ID \cite{webID}, could be dereferenced to get basic information such as name, gender, or email of a person. The concepts of 'name', 'gender', or 'email' also have URIs and they form a vocabulary known as FOAF\cite{foaf}. The files from which RDF triples are extracted could be pure RDF documents, where the triples are expressed in serializations such as RDF/XML or RDF/N3, or it could also be documents with embedded RDF in XHTML such as RDFa\cite{rdfa} or XML with GRDDL\cite{grddl}.

%In terms of what does it mean to be useful information, there are two opposing views. {@@@ Kenny, so, what are the opposing views?} The following is explanned with URIs of people, the Web IDs.

\subsection{Privacy and Trust Oriented View}

Web ID of a person is essentially owned or controlled by the person. The person has the right to determine what information is useful and what is available on the Web about him/her. The Web ID of this kind is analogous to the homepage of a person.

\subsection{Maximum Information Oriented View}

In this view, the Web ID of a person is owned by the public, in the same spirit that a Wikipedia entry about a particular topic can be edited by anybody. For example, while Tim Berners-Lee as a person is owned by himself, the term 'Tim Berners-Lee' is owned by the public, that is, people are almost free to use this term to decribe Tim Berners-Lee in their articles and blogs (??). In the same way, in this view, the Web ID of Tim Berners-Lee is owned by public. The difference between the Web ID and a person's name is that, in the case of a Web ID, all the information about the person can be gathered by intelligent agents with the SPARQL Update protocol\cite{sparul} to be the source from which the Web ID is dereferenced. The resulting information is mostly likely to be inconsistent and lacking in accountability, but from the perspective of this view, lots of social computing, such as justice enforcement could be made possible. For example, the information that somebody is a liar could be computed and updated, and following consequences can be deduced. See chapter ? for further information. Therefore, a Web ID could be thought of as wikipedia entry augmented with a lot more imformation.

\subsection{Properties of Semantic Web}

\subsubsection{Extensibility and Universality}
New concepts can be introduced.
Universality???

\subsubsection{Independance from human languages}
RDF is a formal language.


\subsection{Advantages of Semantic Web}

Raw Data and formal rules
Detecting duplicate services

\section{Modelling Social Computing with N3 Rules}
\label{social-rules}

We decribe the method of modeling social computing with machine readable rules in this section. Several examples are given. (N3 tutorial comes here?)

First of all, we give an example of social computing which is very pervasive.

\subsection{Birthday Greeting}
In social networking sites such as Facebook and MySpace, the user is notified of her friend's birthday so that the user could write a greeting on the friends wall. This computation, done by a person with either the help from the computer agent (in this case, Facebook), could be vaguely modeled with N3 rules as follows,

\begin{VerbatimScript}
{?personA knows ?personB.
 ?personB birthday ?day.
 now day ?day.} =>

{[ a BirthdayGreetingEvent;
   first_participant ?personA;
   second_participant ?personB].}
\end{VerbatimScript}

This reads as "If personA knows personB and personB's birthday is today, then there should be a birthday greeting event for which personA is the first participant (doer) and personB is the second participant (accepter).
Notice that the terms 'knows' and 'birthday' are already defined in the FOAF vocabulary

\subsection{Wish-lists and Trading}

\begin{VerbatimScript}
{?personA :wants ?thingA.
 ?personB :wants ?thingB.
 ?personA :can_provide ?thingB.
 ?personB :can_provide ?thingA.} =>

{[ a :NegotiationEvent;
   :participant ?personA, ?personB;
   :goods ?thingA, ?thingB].}.
\end{VerbatimScript}

This rule basically says if personB can provide the thing personA wants, then they should negotiate to see whether this trading is feasible and what personA should pay personB. Simply, we imagine that in the future people tell about the things they desire on the web, while provider provides the information of the goods. The computer could do various useful caclulations based on these information, if it is truly machine readable.

It is worth mentioning that Amazon has already implemented a system of wishlist. The user is able to put her wishlist on her website in the hope that someone can give her a gift from the wishlist on her birthday. This is one of the benefits of making wishlists transparent. Others include, for example, the fact that the wishlists of many people in Africa contain only 'food' would make people have a deeper understanding of poverty. 

\subsection{Rental Systems}

\begin{VerbatimScript}
{?personA :wants_to_borrow ?thing.
 ?personB :can_lend ?thing.} =>

{[ a :BorrowingEvent;
   :first_participant ?personA;
   :second_participant ?personB].}
\end{VerbatimScript}

\subsection{Confirming "Relationship Status" on Facebook}

\begin{VerbatimScript}
{?personA :in_love_with ?personB.
 ?personB :in_love_with ?personA.} =>

{[ a :ConfessionEvent;
   :participant ?personA, ?personB].}.
\end{VerbatimScript}

\subsection{Making Friends}

\begin{VerbatimScript}
{?personA :interest ?thing.
 ?personB :interest ?thing.} =>

{[ a :MakingFriendsEvent;
   :participant ?personA, ?personB].}.
\end{VerbatimScript} 

\subsection{Preparing a Tax Return}

Assume someone is preparing her tax return. If all the data in her receipts are available as RDF data, she can just run a rule against the graph of data and obtain the necessary information to prepare her tax return. The following is such a simple rule which will filter out the receipt based on the time period of the year that she is preparing her tax return for. We can envisage the benefits of having such a system implemented because it provides transparency for where the data (and thus the money!) is flowing if the IRS (Internal Revenue Service) wants to audit the tax return further. Of course the actual preparation of the tax return has many complex rules and many complex pieces of information come in to play rather than the one we have illustrated here.

\begin{VerbatimScript}
{?receipt :belongs_to ?person;
          :has_date ?date.
 ?date :less_than :last_day_of_the_tax_year;
       :greater_than :first_day_of_the_tax_year.} =>

{[ a :UseReceiptInTaxReturnEvent;
   :participant ?person;
   :evidence ?receipt].}.

\end{VerbatimScript}

\subsection{Predicting Ecosystem Collapse}

Scientists are already combining the text and numbers available on the Web in the hopes of extracting otherwise unavailable or expensive information, essentially using human beings as sensors by mining their communications \cite{eco}. Such methods of "crowd mining" has led to interesting results, such as the Google Flu Trends \cite{flu}. This uses a cloud of keywords to determine episdemoligical data from the Centers   .
As a very extreme example, 
May be incorporate things from here:


\section{Decentralized Social Computing Service}
\label{impl}
 
We have prototyped an 'agent' which use the existing linked social data on the web to make useful inferences. Based on these inferences, the agent can make suggestions to the concerned parties.


\section{Related Work}
\label{related-work}

DBpedia from Wikipedia \cite{dbpedia}

Social Computing stuff \cite{socialcomp_informatics}

\section{Conclusion}
\label{conclusion}

The ideas presented in the paper is at a very conceptual level. Although we provide an implementation, it is still very far from being complete.
Our contributions\\
Future Work\\

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% where an .eps filename suffix will be assumed under latex, 
% and a .pdf suffix will be assumed for pdflatex; or what has been declared
% via \DeclareGraphicsExtensions.
%\caption{Simulation Results}
%\label{fig_sim}
%\end{figure}

% Note that IEEE typically puts floats only at the top, even when this
% results in a large percentage of a column being occupied by floats.


% An example of a double column floating figure using two subfigures.
% (The subfig.sty package must be loaded for this to work.)
% The subfigure \label commands are set within each subfloat command, the
% \label for the overall figure must come after \caption.
% \hfil must be used as a separator to get equal spacing.
% The subfigure.sty package works much the same way, except \subfigure is
% used instead of \subfloat.
%
%\begin{figure*}[!t]
%\centerline{\subfloat[Case I]\includegraphics[width=2.5in]{subfigcase1}%
%\label{fig_first_case}}
%\hfil
%\subfloat[Case II]{\includegraphics[width=2.5in]{subfigcase2}%
%\label{fig_second_case}}}
%\caption{Simulation results}
%\label{fig_sim}
%\end{figure*}
%
% Note that often IEEE papers with subfigures do not employ subfigure
% captions (using the optional argument to \subfloat), but instead will
% reference/describe all of them (a), (b), etc., within the main caption.


% An example of a floating table. Note that, for IEEE style tables, the 
% \caption command should come BEFORE the table. Table text will default to
% \footnotesize as IEEE normally uses this smaller font for tables.
% The \label must come after \caption as always.
%
%\begin{table}[!t]
%% increase table row spacing, adjust to taste
%\renewcommand{\arraystretch}{1.3}
% if using array.sty, it might be a good idea to tweak the value of
% \extrarowheight as needed to properly center the text within the cells
%\caption{An Example of a Table}
%\label{table_example}
%\centering
%% Some packages, such as MDW tools, offer better commands for making tables
%% than the plain LaTeX2e tabular which is used here.
%\begin{tabular}{|c||c|}
%\hline
%One & Two\\
%\hline
%Three & Four\\
%\hline
%\end{tabular}
%\end{table}


% Note that IEEE does not put floats in the very first column - or typically
% anywhere on the first page for that matter. Also, in-text middle ("here")
% positioning is not used. Most IEEE journals/conferences use top floats
% exclusively. Note that, LaTeX2e, unlike IEEE journals/conferences, places
% footnotes above bottom floats. This can be corrected via the \fnbelowfloat
% command of the stfloats package.



\section{Conclusion}
The conclusion goes here.




% conference papers do not normally have an appendix


% use section* for acknowledgement
\section*{Acknowledgment}


The authors would like to thank...



% trigger a \newpage just before the given reference
% number - used to balance the columns on the last page
% adjust value as needed - may need to be readjusted if
% the document is modified later
%\IEEEtriggeratref{8}
% The "triggered" command can be changed if desired:
%\IEEEtriggercmd{\enlargethispage{-5in}}

% references section

% can use a bibliography generated by BibTeX as a .bbl file
% BibTeX documentation can be easily obtained at:
% http://www.ctan.org/tex-archive/biblio/bibtex/contrib/doc/
% The IEEEtran BibTeX style support page is at:
% http://www.michaelshell.org/tex/ieeetran/bibtex/
%\bibliographystyle{IEEEtran}
% argument is your BibTeX string definitions and bibliography database(s)
%\bibliography{IEEEabrv,../bib/paper}
%
% <OR> manually copy in the resultant .bbl file
% set second argument of \begin to the number of references
% (used to reserve space for the reference number labels box)

\bibliographystyle{IEEEtran}
\bibliography{paper}


\end{document}


