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% Oxygen Student Workshop, 24-Apr-2001.  Flames can go to /dev/null.
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% The final rip off by Yuan K. Shen for use in 2007 CSAIL Student workshop.
% The final rip off by Harshad Kasture for use in 2008 CSAIL Student workshop.

\documentclass[8pt]{article} 
\usepackage{csw2008} 
\usepackage{epsfig}
\usepackage{cswapa} 
\usepackage{times}
\usepackage{fancyvrb,relsize,url,graphicx}
\usepackage{fixltx2e} %For wide figures

% If you're using the archaic, outmoded, ancient, grungy, deprecated,
% and generally looked-down-upon LaTeX 2.09 version, then you'll want
% something more like this:
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\DefineVerbatimEnvironment%
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   {fontsize=\scriptsize}

\long\def\comment#1{\relax}

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\begin{document} 

\cswtitleblock{
\cswtitle{The Point of View Axis: Varying the Levels of Explanation Within a Generic RDF Data Browsing Environment}
\cswauthor{Oshani Seneviratne}{oshani@csail.mit.edu}
\cswauthor{Tim Berners-Lee}{timbl@w3.org}
\cswaddress{Decentralized Information Group,

MIT Computer Science and Artificial Intelligence Laboratory}
}

\section{Introduction}

RDF is at the heart of the Semantic Web as it is the primary means by which applications can share data and interoperate.  
Tabulator is a generic data browser and editor for linked RDF data on the web. It was developed with the motivation 
of providing a natural and a seamless experience for browsing and editing data \cite{berners08tabulator}. 
This paper describes how Tabulator can be used to develop custom applications which consume RDF data, in addition to providing a generic data browsing and editing environment. 
The goal is to make sure that the end-user has the ability to view the RDF data in a visualization that is most suitable given the nature of the data.

The paper is structured as follows.  We begin by describing some related work in Section \ref{related-work}. Section \ref{pane-system} gives an overview of the Pane System in Tabulator, and then in Section \ref{just-ui}, we give 
an example where Tabulator can be used to provide varying levels of explanations through \emph{ The Justification User Interface}. We then give a short overview of our future work in Section \ref{future-work}, and conclude the paper with a discussion of our results in Section \ref{conclusion}.

\comment{
In the Tabulator, documents and the 'things' on the web that are fetched are displayed in a table, and then the links in that table are followed 
to load more data about other 'things' in other documents.
The Tabulator project has gone through several iterations of development, and it now supports two different versions: the Online Version and the 
Firefox Extension. In the both of these versions, the documents and the things that are fetched are displayed in a table, and then the links in that table can be followed 
to load more data about other things in other documents \cite{tabonline}. The Tabulator Firefox Extension was built on top of the same javascript libraries used in the Online Version.
However, this provides an interface for linked data on the Firefox browser itself. Using Tabulator's outline mode, query views,
 and back-end code, it gives a more natural and seamless experience when browsing linked data on the Web \cite{tabext}.
}

%This short paper describes how Tabulator can be used to develop custom applications which consumes RDF data by making use of this robust Pane System. The paper is structured as follows: 
%we start by discussing details of
%the Pane System in section \ref{pane-system}
%and then describe the case study we are exploring -\emph{ The Justification User Interface} in section \ref{just-ui}.
%We then give a brief overview of related work in  section \ref{related-work} and conclude the
%paper with a discussion of our results and our future work in section \ref{future-work}.


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

Tabulator builds on a long tradition of data presentation applications. There are many popular domain-specific semantic web browsers. Most of them are front-ends 
tailored to particular triple stores. MSpace \cite{mspace} provides a front-end for a database of classical music detailing music pieces and composers.
CS AKtive \cite{csaktive} provides an interface to query from a database of people with information about their research fields and projects. 
Disco Hyperdata Browser \cite{disco} and OpenLink \cite{openlink} can navigate the Semantic Web as an unbound set of data sources. 
The Inference Web ~\cite{mcguinness04iw} is a comparable system to the \emph{Justification User Interface}, which provides a Semantic Web based knowledge provenance infrastructure that supports interoperable explanations of sources, assumptions, learned information, and answers.

\comment{
The explanation framework in \cite{bonatti06advanced} provides natural
language explanations for questions about policy decisions including
explanations for failed results. In their framework, the explanation
generation process is separate from the regular query process and uses
abductive reasoning, a method of inferring which hypothesis best
explains the facts, to obtain a proof. Parts of their proof language
in the proof are then substituted with natural language
structures. One problem with this approach is that abductive reasoning
is known to produce logically invalid results that can be confirmed
inductively and as the proof generation
process is different from the query process, it could infer an
explanation that is different from that was inferred by the query
process. Another problem is that the explanation for failed policy
results (e.g. why-not) ends up being the list of all rules that the
user did not match. This means that the user has to sort through a lot
of potentially irrelevant information and the disclosure of these
additional policy rules could also be a privacy risk. In our approach,
however, the proof generation happens along with the inference and is
not a separate process so our reasoner tracks the rules and statements
that were actually used to infer the statement (policy
decision). Also, by explicitly handling failed results or unmatched
cases using the \airname{alt} construct, our reasoner is able to
provide explanations for failed (or incompliant) policy decisions
without revealing all possible unmatched rules.

Both WhyNot~\cite{chalupsky02whynot} and the Know
system~\cite{kapadia04know} focus explicitly on failed queries and try
to suggest changes to the knowledge base that will cause these queries
to succeed.  Our justification approach is more general and allows
failures to be captured in policies so explanations can be provided
for both successful and failed policy decisions.

Our proofs could be easily converted to generic proof representation
formats such as PML for display and analysis. PML is a general proof
language or "proof interlingua" that is used to describe proof steps
generated by different kinds of reasoning engines. Once our proofs are
converted to PML a user could manipulate them in Inference Web (IW), a
framework for displaying and manipulating proofs defined in Proof
Markup Language (PML)~\cite{mcguinness04iw}.  IW concentrates on
displaying proofs whereas our approach is mainly about generating
these proofs.

 %IW

Provenance - if users (humans and agents) are to use and integrate data from unknown, uncertain, or multiple sources, they need provenance metadata for evaluation
Interoperability - more systems are using varied sources and multiple information manipulation engines, thus increasing interoperability requirements
Explanation/Justification - if information has been manipulated (i.e., by sound deduction or by heuristic processes), information manipulation trace information should be available
Trust - if some sources are more trustworthy than others, trust ratings are desired
The Inference Web consists of two important components:

Proof Markup Language (PML) Ontology - Semantic Web based representation for exchanging explanations including
provenance information - annotating the sources of knowledge
justification information - annotating the steps for deriving the conclusions or executing workflows
trust information - annotating trustworthiness assertions about knowledge and sources
IW Toolkit - Web-based and standalone tools that facilitate human users to browse, debug, explain, and abstract the knowledge encoded in PM

there is a legal justification has been profuced by an inference engine, and the challenge is to go over it with diffeet types of user, or users doing different tasks.

The Semantic Web lacks support for explaining answers from web applications. When applications return answers, many users do not know what information sources were used, when they were updated, how reliable the source was, or what information was looked up versus derived. Many users also do not know how implicit answers were derived. The Inference Web (IW) aims to take opaque query answers and make the answers more transparent by providing infrastructure for presenting and managing explanations. The explanations include information concerning where answers came from (knowledge provenance) and how they were derived (or retrieved). In this article we describe an infrastructure for IW explanations. The infrastructure includes: IWBase Ñ an extensible web-based registry containing details about information sources, reasoners, languages, and rewrite rules; PML Ñ the Proof Markup Language specification and API used for encoding portable proofs; IW browser Ñ a tool supporting navigation and presentations of proofs and their explanations; and a new explanation dialogue component. Source information in the IWBase is used to convey knowledge provenance. Representation and reasoning language axioms and rewrite rules in the IWBase are used to support proofs, proof combination, and Semantic Web agent interoperability. The Inference Web is in use by four Semantic Web agents, three of them using embedded reasoning engines fully registered in the IW. Inference Web also provides explanation infrastructure for a number of DARPA and ARDA projects.

}

\section{Panes in Tabulator}
\label{pane-system}

%Figure Panes in Tabulator

Tabulator is capable of generic data browsing, but goes one step further by allowing users to exploit the RDF data browsing and editing capabilities to build custom applications through a \emph{'Pane'}.

\begin{figure}[h]
 \centerline{\epsfig{file=images/expanded_panes.jpg, width=1\linewidth }}
  \caption{Some of the Expanded Panes in the Data View }
  \label{fig-expanded-panes}
\end{figure}

A Pane is basically a view for the data fetched in the \emph{Outline} of Tabulator. 
A system of Views and Panes allows the user to see the data in different perspectives. 
If the document fetched is an image, the image itself will be displayed in the default pane. If it is a web page, 
the web page itself will be displayed. However, if an RDF document is fetched, there are more options for visualizing the data. 
For example, suppose a server is serving a document with MIME type "application/rdf+xml". With a normal web browser which is capable of parsing RDF/XML, one would
only be able to see the document in the RDF/XML format only. However with Tabulator, one can see the metadata associated with the document,
serialization of the same data in RDF/N3 and the things described in the document in a class and property explorative manner.


As shown in the Figure~\ref{fig-expanded-panes}, Tabulator Outline has the \emph{Data View}, which could be expanded and collapsed to browse data at several different levels. All the expanded panes are shown highlighted red in the figure. At each level of expansion, Tabulator shows the available \emph{Panes} so that one could visualize the data in different views available. As shown in 
Figure~\ref{fig-panes}, these are some of the icons displayed which signal the user of the available Panes once a document is loaded. Given the nature of the data viewed in a particular Pane, some of these icons may be displayed whereas others may not. For example, if one is looking at FOAF data, the 'FOAF' icon will be displayed, but the 'Explanations View' or the 'Lawyer's View' icons may not be displayed . If a particular Pane is selected, the corresponding icon will be shown to be depressed as in icon 7. 

The functionalities of each of the different types of Panes available in Tabulator are as  follows: {\bf About the Document} gives metadata associated with the document such as the \emph{License}, \emph{Title}, \emph{Maker}, \emph{Primary Topic}, etc.  
{\bf Under the Hood} shows what happened when a thing was looked up on the web, such as the contents of the HTTP headers and the other URIs it referred to.
The {\bf Data View} gives the big picture of the things described in the document, i.e. ordered view of the classes and properties.
 {\bf RDF/N3 View } gives Notation 3 ~\cite{berners08n3} serialization, and the {\bf RDF/XML View} gives the XML serialization of the RDF data.
 The {\bf Explanations View} allows the user to explore the justification proof trace from the AIR reasoner in a step by step manner, whereas the {\bf Lawyer's View} gives a summary of the scenario and the justification behind the reasoning for the same data. The {\bf Friend-Of-A-Friend View} gives an embryonic open social network view based on a user's FOAF data.
 
 %Figure Panes in Tabulator
\begin{figure}
 \centerline{\epsfig{file=images/panes.jpg, width=1\linewidth }}
  \caption{Different Types of Panes/Views in Tabulator. These are {\bf 1:} About the Document View, {\bf 2:} Under the Hood View, {\bf 3:} Data View, {\bf 4:} RDF/N3 View, {\bf 5:} RDF/XML View, {\bf 6:} Explanations View, {\bf 7:} Lawyer's View, and {\bf 8:} Friend-Of-A-Friend View}
  \label{fig-panes}
\end{figure}

\comment{
\begin{enumerate}
	\item  {\bf About the Document}: Metadata associated with the document such as the \emph{License}, \emph{Title}, \emph{Maker}, \emph{Primary Topic}, etc.  
 	\item {\bf Under the Hood}: Shows what happened when a thing was looked up on the web, such as the contents of the HTTP headers and the other URIs it referred to.
  	\item {\bf Data View}: Gives the big picture of the things described in the document, i.e. ordered view of the classes and properties.
 	\item {\bf RDF/N3 View }: The Notation 3 serialization of the RDF data.
 	\item {\bf RDF/XML View}: The RDF/XML serialization of the RDF data.
	\item {\bf Explanations View}: Allows the user to explore the justification proof trace from the AIR reasoner in a step by step manner.
	\item {\bf Lawyer's View}: Gives a summary of the scenario and the justification behind the reasoning.
	\item {\bf Friend-Of-A-Friend View}: An open social network based on a user's FOAF data.
\end{enumerate}
}

 \comment{
The structure of a pane is simple, and loosely coupled from the rest of the Tabulator code. In order to add a new pane, all that has to be done is to add a new
 javascript module which defines the new pane in the \emph{panes} folder. A pane will usually have an \emph{icon}, and a \emph{label} function which will display this icon when the criterion
 for displaying this pane on Tabulator is satisfied. This criterion would usually be some filter based on the RDF data the application is expecting.
  The \emph{render} function defines what the pane should be showing to the user and handlers for any user interactions that are anticipated, etc.
   Finally the new pane should be registered with the \emph{outline} to notify Tabulator of it's existence. 
 
 The basic structure of a pane is as follows:
 \begin{VerbatimScript}
// instance of the pane
var newPane = {};  

// give the pane an icon to display
newPane.icon = icon_source;   
    
newPane.label = function() {
   // define under what conditions this pane 
   // should be visible in Tabulator
}

newPane.render = function(subject) {
   // define what should be rendered in the pane
}
\end{VerbatimScript}
}

\section{An Example Application}
\label{just-ui}

This application was built on Tabulator using the modular \emph{'Pane system'}, and was intended to be used in accountable data systems. We have named it the \emph{'Justification User Interface'}, because it provides the 'justifications' for a legal reasoning given the facts which are based on a log of a person's actions, and a set of policies or rules. A reasoner built independent from the Tabulator infers whether or not the actions were appropriate, and derives a proof tree based on the facts and the rules ~\cite{lkagal08ieee}. The challenge we describe here is that of explaining the inference and presenting the proof in essence, but in a way understandable by people with completely different backgrounds and needs.

There is a Firefox sidebar extension which will invoke the reasoner given the URIs of the relevant log file and the policy file. 
One could also invoke the reasoner by appending the proper parameters to the URI of the reasoner.
The explanations are produced in the form of proof trees in RDF/N3, and indicates whether the given facts
comply with the policy specified. It also gives the premises on which this decision was based. A part of a sample proof tree in the raw format is shown in Figure~\ref{fig-ex}. 
The data visualization in this default outline view could be a bit overwhelming to read and 
understand, as one might only be interested in the final outcome of the policy reasoning.  
The Justification User Interface solves this problem by providing two alternative views as described below.

\comment{ 
\begin{itemize}
	\item  ~{\bf Explanations View}: {This is a graphical layout that highlights the result of the reasoning  and allows the explanation to be explored step-by-step.  }
	\item  ~{\bf Lawyer's View}: {This is summarized format of the reasoning }.
\end{itemize}
}

\comment{
%Figure Everything together
\begin{figure}
 \centerline{\epsfig{file=images/all_panes.jpg, width=1\linewidth }}
  \caption{Different Views for Data Explanation stacked together: On the bottom is the Default Outline View, on top of that is the Explanations View, and then on top of that is the Lawyer's View }
  \label{fig-allpanes}
\end{figure}
}

%Figure Raw Explanations
\begin{figure*}
 \centerline{\epsfig{file=images/raw_explanation.jpg, width=1\linewidth }}
  \caption{Explanation in the Default Outline View}
  \label{fig-ex}
\end{figure*}

 %Figure Why Begininng the Explanation
\begin{figure*}
 \centerline{\epsfig{file=images/why.jpg, width=1\linewidth }}
  \caption{Final Conclusion in the Explanations View}
  \label{fig-why-beg}
\end{figure*}

%Figure Why Explanations
\begin{figure*}
 \centerline{\epsfig{file=images/explanation.jpg, width=1\linewidth }}
  \caption{Explanations after clicking the "why" button}
  \label{fig-why}
\end{figure*}

  
%Figure Explanations for Lawyers
\begin{figure*}
 \centerline{\epsfig{file=images/lawyer.jpg, width=1\linewidth}}
  \caption{Lawyer's View}
  \label{fig-law}
\end{figure*}


\comment{

%Subsection Scenario
\subsection{Scenario}
\label{scenario}

To explain what a user might be seeing in this Justification User Interface, let's look at a hypothetical scenario which was developed by the TAMI project~\cite{tami}. 
The full details of the scenario along with the rules that are applicable are described comprehensively at ~\cite{s9}.  

\begin{itemize}
	\item A hospital in Massachusetts (MA) discovers that a patient called Alfred Newman has a rare form of Tuberculosis (TB) that is resistant to known treatment. The Center for Disease Control (CDC) is notified in order to prevent a TB epidemic.
	\item Unfortunately Alfred is unconscious and cannot be interviewed by the CDC to determine who might have been in contact with him for the past few months. 
	Therefore the CDC decides to follow a traditional forensic investigation. As part of the investigation, the CDC decides to determine the contacts of Alfred through data mining which involves requesting his telephone records from the XPhone Company.
	\item  In Xphone company's phone records, there is a record of Alfred calling Bob a month ago. However it turns out that the two have never met physically, and Bob was cleared by the CDC for TB.
	\item Some time later Bob has some phone trouble, so he calls the XPhone company to schedule a service repair person. Betty, the Customer Service representative at XPhone company, seeing that the CDC has requested Bob's details infers that he must have a contagious disease. 
	Therefore she refuses to send Bob a service-person.
	\item According to the MA Disability Discrimination Law, it is unlawful to deny a service request based on health information. Therefore Betty's actions are not compliant with the law.
\end{itemize}

The AIR reasoner is invoked with a ~\emph{log file} which contains the facts described above in RDF, and a ~\emph{policy file} which describes the MA Disability Discrimination Law in the AIR Policy language.
Once the reasoner has reasoned over the log based on the rules in the policy file, it sends a response with the proof tree which could be explored on Tabulator using the panes described in the next section in addition to the default panes used to view RDF data.
Both these panes are displayed only when the response from the server can be filtered on the predicates \airname{compliant-with} or \airname{non-compliant-with} as defined in the AIR ontology. 
}

	
%Subsection The Explanations View
\subsection{The Explanations View}
\label{ex-view}

In this Pane, the final policy decision from reasoning is filtered, and displayed first as in Figure~\ref{fig-why-beg}. A non-compliant event will be highlighted in red, whereas a compliant event will be highlighted in green. 
Clicking on the "Why?" button which is located underneath this conclusion will initiate the traversal of the proof tree. Figure~\ref{fig-why} shows a sample proof several steps down. The algorithm used here extracts the most appropriate reason for the final outcome based on the \emph{air:descriptions} given. 
The top box in this view gives the description of the rule with the necessary variable bindings that generated the assertion, whereas the box on the bottom gives the premises or the matched graph of that rule. The "More Information" button provides a way for the user to step through the proof starting from the most relevant information working back to the top of the proof tree. The goal is to select the most appropriate high-level description from the proof tree.


%Subsection The Lawyer's View
\subsection{The Lawyer's View}
\label{law-view}

From the perspective of a lawyer, the interesting part of the result from any legal reasoning is the finding of non-compliance or the compliance with the given policy and the associated facts~\cite{kkw}. Therefore, instead of 
enabling the user to explore the proof tree, this pane gives a summarized view, which should be very familiar for a legal professional. This includes the Issue, Rules, Facts, Analysis and the Conclusion as shown in Figure~\ref{fig-law}.
{\bf Issue} is the main problem investigated. In a typical scenario, this is described in the~\emph{transaction log}. The {\bf Rule} is the policy with which the issue in the scenario is checked against. If the policy file has an ~\emph{rdfs:comment} which describes what the intended rule of the policy is, that is taken to be the content for this section. {\bf Facts} are the ground truths presented in the log file. However, only the facts relevant to the scenario are presented, which are basically ~\emph{air:descriptions} of the outer ~\emph{air:rules} in the proof tree (i.e. intermediate rules that were fired in the reasoning). {\bf Analysis} is the summary of how the facts relate to the rules given in the policy. This usually comprises of the the \emph{air:description} of the final \emph{air:rule} in the proof tree.
  Finally, the {\bf Conclusion} is the final outcome of the policy reasoning which specifies whether the log is compliant with the policy or not.

\comment{
\begin{itemize}
	\item {\bf Issue}: This is the main issue investigated. The ~\emph{log} is a link to the log file URI. This is extracted from the full URI with the log and policy parameters. The ~\emph{policy} is a link to the policy with which the compliancy was checked. This is extracted from the proof tree.
   	\item {\bf Rule}: This is the rule with which the issue in the scenario is checked against. The rule is extracted from the the policy file. If the policy file has an ~\airname{rdfs:comment} which describes what the 
	intended rule of the policy is, that is taken to be the content for this section. Otherwise, the content defaults to "Rule is specified in the [policy]".
   	\item {\bf Facts}: These are the ground truths presented in the log file. However, only the facts relevant to the scenario are presented here. All these are ~\airname{air:descriptions} of the outer ~\airname{air:rules} in the proof tree (i.e.intermediate rules that were fired in the reasoning).
   	\item {\bf Analysis}: This is a summary of how the facts relate to the rules given in the policy. Specificaly this comprises of the the \airname{air:description} of the final \airname{air:rule} in the proof tree.
   	\item {\bf Conclusion}: This is the final outcome of the reasoning which specifies whether the log is compliant with the policy or not.
\end{itemize}
}
 

%Not sure what to include in the Related Work Section - anyway this paper has exceeded the limit!

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



%Section Future Work
\section{Future Work}
\label{future-work}

The Justification User Interface is still undergoing development to make it more intuitive to end-users. At the moment it is only using some specific things in the AIR ontology to filter out the policy decision and display the relevant panes in the outline view. But we intend to 
make it more flexible by allowing other types of filters as well. We also wish to incorporate transaction log specific queries. For instance, it is useful to know how many violations there are in a given 
set of transaction log entries, how many violations are due to some particular policy, which policies would a set of transactions violate, and so on.  It would be nice to have a what-if-analysis with the Justification User Interface as well: 
i.e. it would be interesting to know the consequences of changing one rule in the policy and see how many more or fewer violations would have occured in a given set of transactions in the log.

Also, in addition to providing a retrospective approach for policy reasoning with the use of the Pane System  as described in this paper, we also intend to integrate policy aware capabilities into Tabulator. It will then be able to do reasoning in real-time as the 
actions are performed. We hope that this will make the browser more intelligent and advice users against potential policy violations by keeping track of the provenance information for the data, and the user interactions on the browser.

%Section Conclusion
\section{Conclusion}
\label{conclusion}

Tabulator provides a browsing environment  for RDF data where not only can the user explore the data space, but can also vary the level of explanation which they are given at any point. The Justification User Interface with the integrated Explanations View and the Lawyer's View have demonstrated this fact. Although this particular 
application is centered around the legal reasoning domain, the same principle could be applied to develop other such applications. 
It has allowed a new dimension to browsing linked data and the user can easily slip into a more sophisticated level wherever in the graph of data.
Therefore, we believe we have demonstrated that it is possible to provide very different levels of user-friendliness to different types of users, and these specialized applications can be integrated with a generic data browsing environment such as the Tabulator very easily.

%Section Conclusion
\section{Acknowledgements}

Research described here was supported by from the National Science Foundation Cybertrust Grant (award 04281) and IARPA (award FA8750-07-2-0031).
Also, many to thanks to Daniel Weitzner, Lalana Kagal and K. Krasnow Waterman for their valuable feedback on the project, UI design advice and comments on the paper.

%Not in the CSW template. But I feel this should be included as well. However, already exceeded the page limit

%Section Acknowledgements
%\section{Acknowledgements}
%\label{acknowledgements}


\bibliography{references} \bibliographystyle{cswapa}

\end{document} 
