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\title{Knowledge Representation}
\subtitle{Darmstadt University of Technology\\Language and Conceptualization\\Dr. Sabine Bartsch, WS 2003--2004}
\author{Aybala Celebi, Jean-Pierre Schwickerath, Jördis Hensen}
\institution{February 11th, 2004}
\email{http://schwicky.net/projects/2004/language\_and\_conceptualization/}

\slideCaption{Knowledge Representation --- Aybala Celebi, Jean-Pierre Schwickerath, Jördis Hensen}


\begin{document}
\maketitle

\begin{slide}{What is knowledge?}

\begin{enumerate}
	\item The state or fact of knowing.
	\item \textbf{Familiarity, awareness, or understanding gained through experience or study.}
	\item \textbf{The sum or range of what has been perceived, discovered, or learned.}
	\item Learning; erudition: teachers of great knowledge.
	\item \textbf{Specific information about something.}
	\item Carnal knowledge.
\end{enumerate}
\end{slide}



\begin{slide}{Why do we want to represent it?}

Knowledge Representation: Formal reconstruction of knowledge and its implementation.

\begin{itemize}
	\item Encyclopedias are not always optimal
	\item $\rightarrow$ Lots of redundant information, Cross-references, search for properties
	\item For Knowledge transmission, use something like our brain
	\item Feed computers with knowledge
	\item $\rightarrow$ Let the computer think for us: intelligent action requires $ 70.000 \pm 20.000$ knowledge units per subject area.
\end{itemize}
\end{slide}



\begin{slide}{What do we need to represent?}
Like in nature: how do we recognize things? 

Define the criteria we need: optional and needed ones.

\bigskip

\begin{minipage}[c]{5cm}
	\includegraphics[width=5cm]{images/mouse.ps}
\end{minipage}%
\begin{minipage}[c]{6cm}
\begin{itemize}
	\item Mouse: Mammalian
	\item Size: small
	\item Shape: ears, muzzle, short legs
	\item Exact acquisition of dimensions and relations
\end{itemize}
\end{minipage}

\bigskip

Use of fuzzy logic to identify slightly different elements

\end{slide}
\begin{slide}{Semantic Network}
\PDForPS{\includegraphics[height=7cm,bb= 0 40 100 455]{images/semantic_network.ps}}{\includegraphics[angle=90,totalheight=7cm,bb= 15 0 377 550]{images/semantic_network.ps}}
\end{slide}




\begin{slide}{How would we do that?}
\begin{center}
\includegraphics[height=2cm]{images/baby1.ps}
\includegraphics[height=2cm]{images/baby2.ps}
\includegraphics[height=2cm]{images/baby3.ps}
\end{center}


\begin{itemize}
	\item Automatic acquisition still very problematic: semantic recognition in speech and pictures.

	\item What we need are \textsl{double linked elements}: to go from the mouse to its properties and to get from the properties to the mouse. 

	\item Classify objects according to their properties: Let's try to define these properties
\end{itemize}
\end{slide}


\begin{slide}{Ontology}
\begin{itemize}
	\item The subject of ontology is the study of the categories of things that exist or may exist in some domain. 

	\item The product of such a study, called an \textbf{ontology}, is a catalog of the types of things that are assumed to exist in a domain of interest \textbf{D} from the perspective of a person who uses a language \textbf{L} for the purpose of talking about \textbf{D}. 

	\item An uninterpreted logic, such as predicate calculus, conceptual graphs, or KIF, is ontologically neutral. 

	\item It imposes no constraints on the subject matter or the way the subject may be characterized. 
\end{itemize}
\end{slide}



\begin{slide}{Ontology (2)}

\begin{itemize}
	\item By itself, logic says nothing about anything, but the combination of logic with an ontology provides a language that can express relationships about the entities in the domain of interest.

	\item An \textbf{informal ontology} may be specified by a catalog of types that are either undefined or defined only by statements in a natural language (Mouse example).

	\item A \textbf{formal ontology} is specified by a collection of names for concept and relation types organized in a partial ordering by the type-subtype relation (Drinks example). 
\end{itemize}
\end{slide}



\begin{slide}{Ontology (3)}
\begin{figure}
\centering
\includegraphics[width=10cm]{images/drinks_lattice.ps}
\end{figure}
\end{slide}




\begin{slide}{Ontology (4)}
\begin{itemize}
	\item Formal ontologies are further distinguished by the way the subtypes are distinguished from their supertypes: an \textbf{axiomatized ontology} distinguishes subtypes by axioms and definitions stated in a formal language, such as logic or some computer-oriented notation that can be translated to logic; 

	\item a \textbf{prototype-based ontology} distinguishes subtypes by a comparison with a typical member or prototype for each subtype. Large ontologies often use a mixture of definitional methods: formal axioms and definitions are used for the terms in mathematics, physics, and engineering; and prototypes are used for plants, animals, and common household items. 
\end{itemize}
\end{slide}



\begin{slide}{Uses of Knowledge-Based Systems}
\begin{itemize}
	\item Interpretation $\rightarrow$ Data-Mining
	\item Diagnosis
	\item Planing
	\item Robots
	\item Belief-Networks: Bayes-Nets
	\item Artificial Intelligence
\end{itemize}
\end{slide}



\begin{slide}{Examples}
\begin{tabular}{ll}
\textbf{feature} & \textbf{scope of feature value}\\
name & letter string\\
age & 0--100 years\\
sex & male, female\\
height & 0--2.10 m\\
weight & 0--150 kg\\
address & city name\\
occupation & list of occupations\\
telephone number & integer\\
\end{tabular}
\end{slide}


\begin{slide}{Examples: SQL}
Use relational algebra to define relational databases (tables) and use Structured Query Language (SQL) to store and retrieve information. 

\bigskip

\begin{Verbatim}[tabsize=5]
SELECT name FROM people
WHERE weight > '100' AND height < '1.75'
	AND ( address = 'Darmstadt'
	OR address  = 'Griesheim' );
\end{Verbatim}
\end{slide}


\begin{slide}{Examples: PROLOG}
\begin{Verbatim}[tabsize=5]
father(paul, rob).     father(rob, bev).
father(rob, ally).     father(jeff, aaron).
mother(mary, rob).     mother(dorothy, bev).
mother(dorothy, ally). mother(ally, aaron).
\end{Verbatim}

\begin{Verbatim}[tabsize=5]
parent(M,C) :- mother(M,C).
parent(F,C) :- father(F,C).
grandparent(Gparent, Gchild) :-
	parent(Gparent,X), parent(X, Gchild).
\end{Verbatim}

\begin{Verbatim}[tabsize=5]
:- sibling(bev, ally).
:- mother(M, rob) ; father(F, rob).
:- parent(X, rob).
\end{Verbatim}
\end{slide}



\begin{slide}{Thank you for your attention!}
\begin{thebibliography}{99}
	\bibitem{dictionary} \texttt{http://www.yourdictionary.com/}
	\bibitem{goerz} Günther Görz, Knowledge Representation and Reasoning
	\bibitem{wagner} Karl Heinz Wagner, Darstellung von Wissen
	\bibitem{ontology} \texttt{http://www.jfsowa.com/ontology/}
	\bibitem{toscana} TOSCANA --- a Graphical Tool for Analyzing and Exploring Data
	\bibitem{captcha} The Captcha Project \texttt{http://www.captcha.net/}
\end{thebibliography}
\end{slide}


\end{document} 
