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Add FAIRK2 web reasoning

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Tian Cheng (Riccardo) Xia
2023-12-02 21:10:28 +01:00
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src/fundamentals-of-ai-and-kr/module2/main.tex
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\input{sections/_logic.tex}
\input{sections/_ontologies.tex}
\input{sections/_descriptive_logic.tex}
\input{sections/_semantic_web.tex}
\end{document}

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\chapter{Web reasoning}
\section{Semantic web}
\begin{description}
\item[Semantic web] \marginnote{Semantic web}
Method to represent and reason on the data available on the web.
Semantic web aims to preserve the characteristics of the web, this includes:
\begin{itemize}
\item Globality.
\item Information distribution.
\item Information inconsistency of contents and links (as everyone can publish).
\item Information incompleteness of contents and links.
\end{itemize}
Information is structured using ontologies and logic is used as inference mechanism.
New knowledge can be derived through proofs.
\item[Uniform resource identifier] \marginnote{URI}
Naming system to uniquely identify concepts.
Each URI correspond to one and only one concept, but multiple URIs can refer to the same concept.
\item[XML] \marginnote{XML}
Markup language to represent hierarchically structured data.
An XML can contain in its preamble the description of the grammar used within the document.
\item[Resource description framework (RDF)] \marginnote{Resource description framework (RDF)}
XML-based language to represent knowledge.
Based on triplets:
\begin{center}
\texttt{<subject, predicate, object>}\\
\texttt{<resource, attribute, value>}
\end{center}
RDF supports:
\begin{descriptionlist}
\item[Types] Using the attribute \texttt{type} which can assume an URI as value.
\item[Collections] Subjects and objects can be bags, sequences or alternatives.
\item[Meta-sentences] Reification of the sentences (e.g. "X says that Y\dots").
\end{descriptionlist}
\begin{description}
\item[RDF schema] \marginnote{RDF schema}
RDF can be used to describe classes and relations with other classes (e.g. \texttt{type}, \texttt{subClassOf}, \texttt{subPropertyOf}, \dots)
\item[Representation] \phantom{}
\begin{descriptionlist}
\item[Graph] A graph where nodes are subjects or objects and edges are predicates.
\begin{example} \phantom{}
\begin{center}
\includegraphics[width=0.4\textwidth]{img/rdf_graph_example.png}
\end{center}
The graph stands for:
\texttt{http://www.example.org/index.html} has a \texttt{creator} with staff id \texttt{85740}.
\end{example}
\item[XML]
\begin{example} \phantom{}
\begin{lstlisting}[mathescape=true, language=xml]
<rdf:RDF
xmlns:rdf=http://www.w3.org/1999/02/22-rdf-syntax-ns#
xmlns:contact=http://www.w3.org/2000/10/swap/pim/contact#>
<contact:Person rdf:about="http://www.w3.org/People/EM/contact#me">
<contact:fullName>Eric Miller</contact:fullName>
<contact:mailbox rdf:resource="mailto:em@w3.org"/>
<contact:personalTitle>Dr.</contact:personalTitle>
</contact:Person>
</rdf:RDF>
\end{lstlisting}
\end{example}
\end{descriptionlist}
\item[Database similarities]
RDF aims to integrate different databases:
\begin{itemize}
\item A DB record is a RDF node.
\item The name of a column can be seen as a property type.
\item The value of a field corresponds to the value of a property.
\end{itemize}
\end{description}
\item[RDFa] \marginnote{RDFa}
Specification to integrate XHTML and RDF.
\item[SPARQL] \marginnote{SPARQL}
Language to query different data sources that support RDF (natively or through a middleware).
\item[Ontology web language (OWL)] \marginnote{Ontology web language (OWL)}
Ontology based on RDF and description logic fragments.
Three level of expressivity are available:
\begin{itemize}
\item OWL lite.
\item OWL DL.
\item OWL full.
\end{itemize}
An OWL has:
\begin{descriptionlist}
\item[Classes] Categories.
\item[Properties] Roles and relations.
\item[Instances] Individuals.
\end{descriptionlist}
\end{description}
\section{Knowledge graphs}
\begin{description}
\item[Knowledge graph] \marginnote{Knowledge graph}
Knowledge graphs overcome the computational complexity of T-box reasoning with semantic web and description logics.
\begin{itemize}
\item Use a simple vocabulary with a simple but robust corpus of types and properties adopted as a standard.
\item Represent a graph with terms as nodes and edges connecting them.
Knowledge is therefore represented as triplets \texttt{(h, r, t)} where \texttt{h} and \texttt{t} are entities and \texttt{r} is a relation.
\item Logic formulas are removed. T-box and A-box can be seen as the same concept. There is no reasoning but only facts.
\item Data does not have a conceptual schema and can come from different sources with different semantics.
\item Graph algorithms to traverse the graph and solve queries.
\end{itemize}
\item[KG quality] \marginnote{Quality}
\begin{description}
\item[Coverage] If the graph has all the required information.
\item[Correctness] If the information is correct (can be objective or subjective).
\item[Freshness] If the content is up-to-date.
\end{description}
\item[Graph embedding] \marginnote{Graph embedding}
Project entities and relations into a vectorial space for ML applications.
\begin{description}
\item[Entity prediction] Given two entities \texttt{h} and \texttt{t}, determine the relation \texttt{r} between them.
\item[Link prediction] Given an entity \texttt{h} and a relation \texttt{t}, determine an entity \texttt{t} related to \texttt{h}.
\end{description}
\end{description}