I am a beginner in preparing beamer presentations. I don't know the advanced options available in it that make it more appealing and more sensible. Here is my code for the beamer presentation. I need suggestions on how to improve it further:
\documentclass[12pt,xcolor=dvipsnames]{beamer}
\usetheme{CambridgeUS}
\useinnertheme{rectangles}
\useoutertheme{infolines}
\setbeamercolor{frametitle}{fg=Brown,bg=Brown!20}
\setbeamercolor{section in head/foot}{bg=Brown}
\setbeamercolor{author in head/foot}{bg=Brown}
\setbeamercolor{date in head/foot}{fg=Brown}
\usepackage{tcolorbox}
\title[Semantic Web ranking]{Ranking of Web Documents using Semantic Similarity}
\author{Subham Soni}
%\date{19.8.2014}
\date{\parbox{\linewidth}{\centering%
\today\endgraf\bigskip
Submitted to\\
Dr. (Mrs.) K. Saruladha\\ B.Tech., M.Tech., Ph.D.\endgraf\medskip
Dept.\ of Computer Science and Engineering \endgraf
Pondicherry Engineering College}}
\begin{document}
\maketitle
\begin{frame}{Agenda}
\tableofcontents
\end{frame}
\section{Abstract}
\begin{frame}{Abstract}
\begin{itemize}
\item To relate documents that have similar meaning, semantic similarity approach is used by matching the keywords which are extracted
\item Here, all the relevant relations between the keywords describing user's context and then calculating the fraction of these relations on each web page to determine their relevance to user's query
\end{itemize}
\begin{tcolorbox}
\textbf{Proposal:} A Ranking algorithm for the \textit{semantic web \mbox{documents}} by finding the semantic similarity between the \mbox{documents} and the user's query
\end{tcolorbox}
\end{frame}
\section{Introduction}
\begin{frame}{Introduction}
\begin{itemize}
\item A huge of information is stored in the WWW in the form of \emph{interlinked} web pages
\item Traditional Search Engines use \textit{lexical matching} and \textit{link analysis} to produce the results
\item To overcome the limitation of lexical matching, it is necessary to determine the \textbf{relationship} between the keywords
\item Other methods such as Ontology based matching, Semantic Analysis, etc. exist but here a novel ranking model is explored which provides the result-set according the user query and semantics of the document
\end{itemize}
\end{frame}
\section{Related Work}
\begin{frame}{Related Work}
\begin{itemize}
\item Existing Ranking models:
\begin{itemize}
\item Boolean Model
\item Statistical Model
\item Hyperlink Based Model
\item Conceptual Model
\item Ontology based similarity
\end{itemize}
\end{itemize}
\end{frame}
\section{Proposed Ranking Model for Semantic Web Documents}
\begin{frame}{Proposed Ranking Model for Semantic Web Documents}
\begin{center}
\includegraphics[scale=0.33]{architecturenew}
\end{center}
\end{frame}
\begin{frame}
\begin{enumerate}
\item Determine the keywords of the document using syntactic analysis to make vector space model of the documents
\begin{enumerate}
\item A domain specific dictionary is prepared
\item The words in the dictionary are assigned weights based on their relevance to the domain using \emph{fuzzy set approach}
\item Then, the mapping of each of the words in the vector space model stored in the document repository is done with the domain specific dictionary
\end{enumerate}
\item The document similarity w.r.t the user query is also
found by extracting the concepts and the relationship that
exists between the concepts present in a document and the
mapping of the same is then done by the ontology processor
\item Finally the maximum value gives the overall relevance of the document with respect to the query
\end{enumerate}
\end{frame}
\section{Example}
\begin{frame}{Example}
\begin{itemize}
\item Consider four documents w.r.t education domain
\item The dictionary based weights are as shown:
\end{itemize}
\begin{center}
\includegraphics[scale=0.5]{dictionary}\end{center}
\end{frame}
\begin{frame}
\begin{itemize}
\item The ontology based weights are as shown:
\end{itemize}
\begin{center}
\includegraphics[scale=0.5]{ontology}\end{center}
\end{frame}
\begin{frame}
\begin{itemize}
\item The documents considered are :
\item[D1:]Education is a lifelong process. A person learns through
his experience. It goes on forever from his birth to death
without any break or barrier.
\item[D2:]Education of man does not begin at school but begins at
birth. It ends not when he graduates from university but ends
at his death. Hence, Education is a lifelong process.
\item[D3:]Education is not only academics but social also. It is
important in one's person life.
\item[D4:] In a person life everyone needs to be educated and social.
Everyone learns through experiences gained in one's life.
\end{itemize}
\end{frame}
\begin{frame}
Results:
\begin{center}
\includegraphics[scale=0.7]{results}
\end{center}
\end{frame}
\section{Future Work}
\begin{frame}{Future Work}
\begin{itemize}
\item To design more meaningful and exhaustive ranking strategy by using the semantic analysis of web pages and by deeply statistical analysis relevance of documents
\item To make the approach scalable to the semantic web
\end{itemize}
\end{frame}
\end{document}
The images I have used are the following:
architecturenew.png
dictionary.png
ontology.png
results.png
The tables could have been prepared in TeX but due to the lack of time, I had to copy from the document and paste it.
