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\begin{center}\Large
\includegraphics[width=5cm]{uibk_logo_4c_cmyk.pdf}\\[5mm]
\begin{large}\bf
PS 703301 -- WS 2021/22\\
Current Topics in Computer Science\\[5mm]
\end{large}
Final Report\\[20mm]
{\titlefont \huge Shaping Knowledge Graphs}\\[20mm]
Philipp Gritsch \\
Jamie Hochrainer \\
Kristina Magnussen \\
Danielle McKenney\\
Valerian Wintner\\[35mm]
supervised by\\
M.Sc. Elwin Huaman\\
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\todo{remove this part}
I added a comment colour for everyone.
\Jamie{Comment colour Jamie}
\Danielle{Comment colour Danielle}
\Philipp{Comment colour Philipp}
\Valerian{Comment colour Valerian}
\Kristina{Comment colour Kristina}
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\Jamie{We need an abstract!}
\Philipp{some small abstract idea:}
With rising interest in Knowledge Graphs and their use, the necessity of maintaining the structured information emerges. This can be achieved by ... Our tool aims to integrate all the essential processes into one framework.
\Danielle {Alternative intro sentence (replacing the first two sentences): With the massive amount of data available on the internet growing every day, a convenient, flexible, and efficient way of storing and representing data about concrete objects, abstract ideas, and connections between entities is more important than ever.}
Nowadays, more and more devices collect data very quickly. Since such a huge amount of data will get confusing, we need some way of representing this data in a useful way. This is where \emph{knowledge graphs} become important. There exist various definitions of \emph{knowledge graphs} but as the name indicates, they are basically a knowledge model that is structured as a graph. That knowledge model can contain types, entities, literals, and relationships. A \emph{knowledge graph} can make it easier to find and process facts in which one might be interested.
\Danielle{Replace the following sentence with: However, large datasets are often inconsistent and are prone to containing errors.}
However, a problem that occurs when working with large datasets is that they can be inconsistent and might contain errors.
\Danielle{Replace the following sentence with: Working with this information can be greatly facilitated by defining a consistent shape for the data based on the type of entity or idea it represents.}
In order to work with this data properly, it is necessary to shape the \emph{knowledge graph} in which this data is contained. This shaping is done by inferring constraints over the data and validating it based on these constraints. Validating a graph against constraints gives important insight into the structure of the data. For instance, when all nodes of a type conform to constraints, then it may be useful to define these as required attributes for all future nodes to ensure uniformity in the data. Non conforming nodes may also deliver important insight into where information is missing. For example, if 99\% of nodes of a given type conform to some constraints, it may be worthwhile to investigate the remaining 1\% to see if they are missing necessary information or are otherwise corrupt. \\
Our task was to implement a framework for shaping \emph{knowledge graphs}. This consisted of three major steps: fetching \emph{knowledge graphs}, inferring constraints, and validating a \emph{knowledge graph} against these constraints. We also provide a user interface to this purpose. These steps are described in Section~\ref{section:approach}. After this was done, we evaluated our framework concerning runtime and correctness which is outlined in Section~\ref{section:evaluation}. Results of our project are shown in Section~\ref{section:results}. A conclusion of our work is provided in Section~\ref{section:conclusion}.
The need for automatic tools that are able to infer meta information on the structure of knowledge graphs has already been recognized by different researchers. This stems from the fact that manual constraint inference becomes infeasible for large datasets.
\Danielle{the citing here looks strange...}
Our framework makes use of an adapted version ~\cite{werkmeister2018,werkmeister_rdf2graph_git} of \emph{RDF2Graph}~\cite{vanDam2015,original_rdf2graph_git}, which uses different \emph{SPARQL} queries to gather the structural information of each node of the underlying graph in a first phase. Subsequently, the queried information is gathered and simplified. This is achieved by merging constraint information of classes belonging to the same type and predicates. While Van Dam et al. used \emph{RDF2Graph} tool on the UniPort RDF resource, Werkmeister made adaptions to also infer Wikidata constraints.
Fernandez-{Álvarez} et al. have taken a different approach with their tool \emph{Shexer}~\cite{Shexer}. In contrast to the aforementioned tool, they avoid querying the whole underlying graph by using an iterative approach, determining whether or not the current iterated (sub-)set of triples is relevant for the constraint generation process. Given a target shape, the preselected triples are used to decorate each target instance with its constraints.
Another constraint generator has been introduced by Spahiu et al. with \emph{ABSTAT}~\cite{Spahiu2016ABSTATOL}. This uses an approach similar to that of \emph{RDF2Graph} by collecting structural information using \emph{SPARQL} queries and summarizing those constraints afterwards.
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\section{Approach} \label{section:approach}
%You may add any subsections you deem appropriate for your specific project. Some examples for your reference: Technology stack, Training strategy, Data, Experiments, etc.
To construct a framework that offers a way to evaluate a \emph{knowledge graph} in an automated way, we divided our project into three main subtasks.
At first, we fetch a subgraph of a \emph{knowledge graph} from the \emph{CommonCrawl} datasets, as explained in greater detail in Section~\ref{fetchingKG}.
After this, our framework infers constraints over this data set (see Section~\ref{generatingconstraints}).
In the last step, the subgraph is validated against the constraints (see Section~\ref{validatingconstraints}).
The structure of the framework can be seen in Fig.~\ref{fig:uml}.
The user can interact with this framework over the front-end (see Section~\ref{frontend}).
%Our framework offers a way to evaluate a \emph{knowledge graph} in an automated way. For this, we used \emph{knowledge graphs} from the \emph{CommonCrawl} datasets as a basis. The \emph{knowledge graphs} are imported as a static file. After this, our framework infers constraints over this data set (see Section~\ref{generatingconstraints}). These are validated automatically in the last step, see Section~\ref{validatingconstraints}. The user can interact with this framework over the front-end, see Section~\ref{frontend}. These different steps were implemented and tested separately. Once this was done, we consolidated them. The structure of our project can be seen in Fig.~\ref{fig:uml}. \todo{update figure}
\begin{figure}[ht]
\centering
\includegraphics[scale=0.35]{kg_shapes_uml.pdf}
\caption{UML diagram of the framework structure}
\label{fig:uml}
\end{figure}
\todo{refer to the readme here? Or should this happen somewhere else?}
\todo{add reference to our github repo!}
\subsection{Technology Stack}
In this section, we summarise the main technologies that we used in this project.
The framework was implemented in \emph{Java}. We used \emph{Maven} as a project management tool. We also used java framework \emph{Jena}, which offers an \emph{RDF} API as well as support for \emph{SPARQL} queries and the \emph{ShEx} language. The front-end was implemented using \emph{Vue3}\cite{Vue} as a front-end framework and \emph{PrimeVue} as a library for the different UI components. For the deployment of our application we used a single virtual machine. Access to the front-end is done via a single \emph{Apache} server. The front-end accesses the back-end via an internal \emph{REST-API}.
\subsection{Fetching Knowledge Graphs}
Because \emph{knowledge graphs} can be very large and contain many nodes, we concentrated on fetching smaller subgraphs and only working on those. With this method, the relevant subgraph gets extracted from a \emph{knowledge graph} and can be worked upon in isolation.
We take our initial \emph{knowledge graphs} from the \emph{CommonCrawl} datasets and import them as a static file.
\todo{Explain query magic that fetches the graph here}\\
\Valerian{Missing: Subsection about generating subgraph (with limit), starting from a certain type of node.} \Kristina{Wouldn't this be part of Generating constraints? I feel like that doesn't really fit into technology stack}
\Danielle{I agree with Kristina here.}
\label{generatingconstraints}
To shape a \emph{knowledge graph} we need to infer constraints on the previously fetched subgraph.
For the generation of constraints, we used the adaption of the tool \emph{RDF2Graph}\cite{original_rdf2graph_git} by Werkmeister\cite{werkmeister2018}\cite{werkmeister_rdf2graph_git}. \Valerian{(this is the old version, we use the fork)} \Kristina{I adapted the sentence, do you think this works now, Valerian?} and adapted it for our purposes. As input, \emph{RDF2Graph} takes a \emph{knowledge graph} from \emph{CommonCrawl} \Jamie{Why don't we have the fetched KG as input?}\Danielle{Jamie makes a great point}. The properties of the graph are read out with several \emph{SPARQL} queries. These properties are saved in a new \emph{RDF} graph. As output, we receive a graph containing constraints for the initial input data. We use a tool offered by \emph{RDF2Graph} to extract the constraints in \emph{ShEx} syntax. \Valerian{RDF2Graph offers a tool to export the constraints to ShEx syntax.} \Kristina{I adapted the sentence, do you think this is okay now, Valerian?}
\missingfigure{ add query to graph (chosen by Philipp), e.g. multiplicy of argument etc.}
%\subsubsection{Integrating RDF2Graph with our framework}
We implemented the following steps in order to integrate \emph{RDF2Graph} into our project. We added \emph{RDF2Graph} to our framework so that they could be compiled together\Valerian{, and in the process minimally updated it to be compatible with our version of Java and Jena}. \Kristina{I would leave out the minimally, no need to downplay the work you did on this here, I think. Apart from that, I like it, feel free to put it in} In addition, we changed some of the initial parameters of the \emph{RDF2Graph}, since it originally was intended as a stand-alone application. As we are handling \emph{Models} \todo{Add explanation for Model? Maybe in glossary?} in our software, we changed the input from a \emph{RDF2Graph} to a \emph{Model}. In our application, \emph{RDF2Graph} does not use any other storage apart from the \emph{Model} data structure. Previously, such a \emph{Model} needed to be created by \emph{RDF2Graph}, now it is provided by our framework. We did this so we could have full control over the files handled by \emph{RDF2Graph}. \emph{RDF2Graph} allows for multithreaded execution, which requires a thread pool. This thread pool was initially created by \emph{RDF2Graph}. In our framework, it is provided by our application. In addition, resources which are used by \emph{RDF2Graph} had to be provided in a different way so that they are still available when running from a server environment. We also changed some of the queries. \emph{RDF2Graph} supports multiple output graphs, however, this did not work \todo{should we explain this in more detail?}. As we only work on one Model at a time, we only use one output graph.
\todo{Add explanation of limit to this section?}
\label{validatingconstraints}
Given a \emph{RDF} graph and a set of constraints, the validation consists of verifying that every node in the graph fulfils the requirements given in the constraints. A graph may contain node with different types. Each of those types must conform to its corresponding definition outlined in the constraints. The result of the validation is a multidimensional list containing every node's id, a boolean flag, and an optional 'reason' entry. The boolean flag indicates whether or not the node conforms to its type's constraints. In case of nonconformity, a reason will be given.
For the implementation of this process, an \emph{RDF} subgraph and \emph{Shex} constraints are required as input. Then, we use this to generate a \emph{shape map}, which contains all of the types that need to be validated. For the actual validation, the \emph{ShExValidator} provided by the \emph{Jena} library was used. \todo{add reference to Jena library here?} The validator requires a set of constraints defined in valid \emph{ShEx} syntax and a \emph{shape map}.
\Danielle{the following is repetetive, should be removed.}
The \emph{shape map} describes which types of nodes need to be validated against which \emph{ShEx} constraint definitions. \Valerian{We construct the shape map depending on the types available in the subgraph. See Figure \ref{fig:shape_map} for an example.}
\begin{figure}
\centering
\lstinputlisting{code_snippets/RiverBodyOfWater_RiverBodyOfWater_null.shapemap}
\caption{The shape map used for validating the full subgraph starting with RiverBodyOfWater-nodes.}
\label{fig:shape_map}
\end{figure}
\missingfigure{add picture/code snipped of shapeMap}
\Danielle{Reformulate the following paragraph as follows:
The class \emph{ShexValidationRecord} stores the result of the validation for each node of the graph. Additionally, the percentage of nodes that conform to their constraints is calculated and stored.}
The class \emph{ShexValidationRecord} stores the result of the validation for every single node of the graph. Not only is the individual result of every node checked against its relevant constraints, but we also calculate the percentage of nodes that conform to their constraints.
\subsection{Front-end}
\label{frontend}
We implemented a front-end where the user can choose a \emph{knowledge graph} as well as a type of knowledge graph and its type. \todo{check whether this is what we are doing in the finished version} In addition, the user can also set a limit on the number of nodes of the specified type that they wish to have constraints generated for. \todo{maybe also put this explanation in the query section} As output, \emph{ShEx} constraints as well as a validation of the subgraph against those constraints are returned. The constraints can be edited by the user and the selected subgraph can be re-validated against the newly edited constraints.
If a node is deemed invalid, a reason is given, e.g. "Cardinality violation (min=1): 0". The user can download the subgraph that was validated. The interaction between user, front-end and server can also be seen in Fig.~\ref{fig:sequence}.
\todo{explain how different limit influences data output}
\missingfigure{add screenshot of the front-end}
\todo{update and scale sequence diagram and refer to it}
\begin{figure}[ht]
\centering
\includegraphics[scale=0.5]{kgshapes_sequence.pdf}
\caption{Sequence diagram showing the interaction between web application, user and server}
\label{fig:sequence}
\end{figure}
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\section{Evaluation} \label{section:evaluation}
\todo{ensure that images are well-placed}
\todo{check what Elwin said concerning Evaluation on meeting 20.01.2022}
For taking measurements, the application was started locally on our hardware. \Danielle{give info on what kind of hardware this was}
This was done to minimise side-effects of other applications running on the virtual machine where the live-instance is deployed. Additionally, the JVM was set up to use up to 16 GB of main memory for its heap to allow parallel queries without compromising the runtime of the executions, arising from extensive swap usage. \Kristina{This sentence is very long, maybe we can split it somehow?}
The measurements were taken on a local machine, which uses a \emph{Ryzen 9 3900x} CPU with 12x3.8GHz processors, DDR4 RAM and an SSD. The subgraphs fit into memory.
Figures~\ref{fig:exec_times_per_limit} and \ref{fig:exec_times_per_triples} show the measurements we obtained by changing the \emph{LIMIT} input parameter. This parameter limits the size of the start-node subset, from which connected nodes are queried. All the measurements are shown in Tables~\ref{table:runtimes_wo_limit} and \ref{table:runtimes_w_limit}.
\begin{figure}[h!]
\begin{subfigure}{\textwidth}
\centering
\includegraphics[width=0.65\linewidth]{img/limit_legend.pdf}
\end{subfigure}
\begin{subfigure}{0.33\textwidth}
\includegraphics[width=0.9\linewidth]{img/Canal_limit.pdf}
\caption{RDFType = Canal}
\label{fig:limit_canal}
\end{subfigure}
\begin{subfigure}{0.33\textwidth}
\includegraphics[width=0.9\linewidth]{img/RiverBodyOfWater_limit.pdf}
\caption{RDFType = RiverBodyOfWater}
\label{fig:limit_rbow}
\end{subfigure}
\begin{subfigure}{0.33\textwidth}
\includegraphics[width=0.9\linewidth]{img/Service_limit.pdf}
\caption{RDFType = Service}
\label{fig:limit_service}
\end{subfigure}
\caption{Execution times per RDFType, per size of start-node subset on RiverBodyOfWater dataset}
\label{fig:exec_times_per_limit}
\end{figure}
The results shown in Figure~\ref{fig:exec_times_per_limit} were to be expected. First of all, the runtime of constructing the desired subset of the graph is considerably larger than the time needed to create the \emph{ShEx} constraints, or to validate the constraints on the graph.
Secondly, the runtime of constructing the subgraph scales with the \emph{LIMIT}. This becomes especially evident in Figures~\ref{fig:limit_canal} and \ref{fig:limit_service}.
To understand the behaviour shown in Figure~\ref{fig:limit_rbow}, we want to look at Figure~\ref{fig:exec_times_per_triples}, which shows the same runtimes, but grouped by the number of triples in the subgraph, on which the constraints are created. Unlike in Figures~\ref{fig:triple_canal} and \ref{fig:triple_service} the maximum number of triples (shown in the x-coordinate in Figure~\ref{fig:triple_rbow}), is 1769.
This is also the amrefer to figures paperount \Kristina{What does this mean?} of triples contained in the subgraph that we get without providing any limit. Therefore, providing a limit larger than 200 won't enrich the constructed graph, keeping the time almost constant in regards to the \emph{LIMIT} parameter.
\begin{figure}[h!]
\begin{subfigure}{\textwidth}
\centering
\includegraphics[width=0.65\linewidth]{img/triple_legend.pdf}
\end{subfigure}
\begin{subfigure}{0.33\textwidth}
\includegraphics[width=0.9\linewidth]{img/Canal_triple.pdf}
\caption{RDFType = Canal}
\label{fig:triple_canal}
\end{subfigure}
\begin{subfigure}{0.33\textwidth}
\includegraphics[width=0.9\linewidth]{img/RiverBodyOfWater_triple.pdf}
\caption{RDFType = RiverBodyOfWater}
\label{fig:triple_rbow}
\end{subfigure}
\begin{subfigure}{0.33\textwidth}
\includegraphics[width=0.9\linewidth]{img/Service_triple.pdf}
\caption{RDFType = Service}
\label{fig:triple_service}
\end{subfigure}
\caption{Execution times per RDFType, per number of triples on RiverBodyOfWater dataset}
\label{fig:exec_times_per_triples}
Figure \ref{fig:exec_times_no_limit} shows the runtime without limiting the construction of the subgraph. Note the much larger runtime needed for querying the graph, despite resulting in the same amount of triples when providing a large enough \emph{LIMIT}.
\begin{figure}[h!]
\begin{subfigure}{\textwidth}
\centering
\includegraphics[width=0.65\linewidth]{img/no_limit_legend.pdf}
\end{subfigure}
\begin{subfigure}{\textwidth}
\centering
\includegraphics[width=0.6\linewidth]{img/no_limit.pdf}
\caption{Execution times per RDFType of the RiverBodyOfWater dataset (containing 49915 triples)}
\label{fig:exec_times_no_limit}
\end{figure}
\subsection{Correctness}
We thought \emph{Shexer}, which was already mentioned in Section~\ref{section:related_work}, was a good fit for cross validating our \emph{ShEx}-generation. However, due to our limited knowledge of operating this tool, we did not manage to generate proper constraints for our RiverBodyOfWater-dataset. Our attempt at using this tool is shown in Figure~\ref{code:shexer}, which generated the trivial, non-restrictive constraints shown in Figure~\ref{fig:shexer_output}.
Therefore, we checked the generated constraints manually for small subgraphs (see Figures~\ref{fig:shex_canal_50}, \ref{fig:shex_rbow_50} and \ref{fig:shex_service_50}) and identified two issues with our tool.
Firstly, if the dataset consists of only stand-alone blank nodes, as seen in Figure~\ref{code:blank_nodes}, then \emph{Rdf2Graph} does not infer any \emph{ShEx}-constraints. This was the case for the generated subgraph using RiverBodyOfWater with a \emph{LIMIT} of 50, and the resulting \emph{ShEx} can be seen in Figure~\ref{fig:shex_rbow_50}.
Secondly, optional properties are not always inferred and therefore missing from the generated \emph{ShEx}-constraints. This also happens for unlimited subgraphs (see Figures~\ref{fig:shex_canal_wo_limit}, \ref{fig:shex_rbow_wo_limit} and \ref{fig:shex_service_wo_limit}), with the exception of the RiverBodyOfWater-RDFtype, where it looks like the constraints are complete, \Kristina{Would put a full stop here and start new sentence "However,..."} however due to the large graph manually checking for correctness is infeasible. We did not see a correlation between missing constraint-properties and the shape of the graph.
\todo{No subsection for just 2 sentences}
The generated \emph{ShEx}-constraints for small subgraphs (Canal with \emph{LIMIT} 50, Service with \emph{LIMIT} 50) were cross validated using the online-tool \emph{RDFShape}\cite{RDFShape}. The validation result was the same as in our tool.
\begin{figure}
\centering
\lstinputlisting{code_snippets/blank_nodes.ttl}
\caption{Blank Nodes in Turtle File}
\section{Results} \label{section:results}
Our framework automatically infers constraints and validates the given data based on those constraints. This can be done on two different \emph{CommonCrawl} datasets. The user can choose one of those datasets and a limit \todo{explain this limit in more depth, maybe in front-end? -> also mention that the user can select a type} using the front-end. User can also edit constraints.
\Danielle{maybe mention that while the validation of the subgraph works as expected, the constraints generated are prone to missing an optional url attribute and would benefit from more tests and tuning. }
\missingfigure{Maybe add small figure that shows workflow of project here? Something similar like we did in presentation but more professional?}
\todo{describe results of benchmark tests here}
\section{Future work}
\todo{Possible future work could be: more data sets, more possibilities for user inputs}
Our application currently only handles two different datasets. For future work, this could be expanded so that the framework could handle more and bigger datasets. Currently, the size of the datasets that can be handled is limited by the RAM on the virtual machine. One possible solution for this could be to only work on parts of the graph.
One problem we encountered when handling datasets from \emph{CommonCrawl} was the quality of these datasets. Many datasets include \emph{non-unicode} characters, which are replaced by Jena with \emph{unicode} characters. This takes a lot of computing time. In addition, many files include invalid \emph{RDF} syntax or are otherwise damaged. This means that in order to handle additional datasets, some way of processing these datasets would have to be implemented. Processing could include filtering for broken files and invalid syntax and fixing this before handling the dataset in the framework.
\todo{Should we add proper SPARQL endpoints here? Might not be possible?}
In addition, more possibilities for user interaction could be added. For instance, a feature could be added where a user can upload their own dataset and have it validated.
\label{conclusion}
\todo{Which challenges did we face during the implementation? (Maybe depth of SPARQL query, outdated RDF2Graph?)}
\todo{Did we achieve what we wanted to do? How well and reliably does the framework work?}
% ------------------------------------------------------------------------
% Bibliography
% ------------------------------------------------------------------------
\bibliography{./bibliography.bib}
\bibliographystyle{abbrv}
\appendix
\section{Contribution Statements}
Each member of the group contributed in an enthusiastic and equal manner, leveraging their individual skills to contribute to the parts of the project where they could make the most impact. Additionally, everyone was eager to learn new skills and teach others what they knew. We are all satisfied with how much everyone contributed and how well we worked together. The following presents a brief summary of each group member's contribution to the project:
\begin{itemize}
\item Danielle's programming and organizational skills were a great asset to the team. She ensured that meetings had structure, with clear goals, responsibilities, and deadlines defined. She worked most on the implementing the shex validation and developing the backend app, leading several pair programming sessions with her team members. She also participated in the final presentation and parts of the report.
\item Jamie contributed most with her teamwork skills and adaptability. Although she did not have as much experience programming in java and javascript as other team members, she readily made an effort to learn and thrived with the pair programming method we implemented, working largely on the webapp and parts of the rdf2 implementation. Additionally, she was heavily involved in the designing the presentations and reviewing the report.
\item Kristina contributed most in the research and planning of the project. Her research skills were heavily utilized in the initial phase of the project, which greatly helped the others when it came to choosing libraries and overcoming difficulties in the implementation of technical problems. She worked most on programming parts of the project requiring knowledge of shex. Her extra research also proved useful in delivering thorough and well thought out presentations and drafting the report.
\item Philipp's technical skills were highly useful in the programming part of the project. He advised the selection of the tech stack and led many pair programming sessions, readily sharing his technical knowledge with the other team members. This also came in handy when contributing the evaluation and 'related work' sections of the report.
\item Valerian, similarly to Philipp, also had strong technical skills that he applied in various areas of the project. He worked on the sparql parts of the programming and on creating the frontend app, often leading a pair programming session. He also contributed to the evaluation and writing up the results of this.
\end{itemize}
\section{Appendix}
You may use appendices to include any auxiliary results you would like to share, however cannot insert in the main text due to the page limit.
\subsection{Code listings and additional data}
\begin{figure}
\centering
\lstinputlisting[language=python]{code_snippets/shexer.py}
\caption{Running shexer on the full graph}
\label{code:shexer}
\end{figure}
\begin{figure}
\centering
\lstinputlisting{code_snippets/shexer_out.shex}
\caption{Shexer output}
\label{fig:shexer_output}
\end{figure}
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\begin{figure}
\centering
\lstinputlisting{code_snippets/RiverBodyOfWater_Canal_50.shex}
\caption{Generated \emph{ShEx}-constraints of Canal with \emph{LIMIT} 50}
\label{fig:shex_canal_50}
\end{figure}
\begin{figure}
\centering
\lstinputlisting{code_snippets/RiverBodyOfWater_RiverBodyOfWater_50.shex}
\caption{Generated \emph{ShEx}-constraints of RiverBodyOfWater with \emph{LIMIT} 50}
\label{fig:shex_rbow_50}
\end{figure}
\begin{figure}
\centering
\lstinputlisting{code_snippets/RiverBodyOfWater_Service_50.shex}
\caption{Generated \emph{ShEx}-constraints of Service with \emph{LIMIT} 50}
\label{fig:shex_service_50}
\end{figure}
\begin{figure}
\centering
\lstinputlisting{code_snippets/RiverBodyOfWater_Canal_null.shex}
\caption{Generated \emph{ShEx}-constraints of Canal without a \emph{LIMIT}}
\label{fig:shex_canal_wo_limit}
\end{figure}
\begin{figure}
\centering
\lstinputlisting{code_snippets/RiverBodyOfWater_RiverBodyOfWater_null.shex}
\caption{Generated \emph{ShEx}-constraints of RiverBodyOfWater without a \emph{LIMIT}}
\label{fig:shex_rbow_wo_limit}
\end{figure}
\begin{figure}
\centering
\lstinputlisting{code_snippets/RiverBodyOfWater_Service_null.shex}
\caption{Generated \emph{ShEx}-constraints of Service without a \emph{LIMIT}}
\label{fig:shex_service_wo_limit}
\end{figure}
\begin{table}[!ht]
\centering
\begin{tabular}{|l|r|r|r|r|}
\hline
Rdftype & Triples & [$t_{graph}$] = ms & [$t_{shex}$] = ms & [$t_{validation}$] = ms \\ \hline
Canal & 16961 & 360000 & 737 & 45 \\ \hline
GeoCoordinates & 204 & 468000 & 585 & 4 \\ \hline
RiverBodyOfWater & 1769 & 468000 & 613 & 15 \\ \hline
Service & 7334 & 462000 & 618 & 19 \\ \hline
\end{tabular}
\caption{Execution times per RDF-Type, queried on full graph of the RiverBodyOfWater dataset (containing 49915 triples)}
\label{table:runtimes_wo_limit}
\end{table}
\begin{table}
\centering
\begin{tabular}{|l|r|r|r|r|r|}
\hline
Rdftype & Limit & Triples & [$t_{graph}$] = ms & [$t_{shex}$] = ms & [$t_{validation}$] = ms \\ \hline
Canal & 50 & 226 & 2420 & 923 & 44 \\ \hline
Canal & 100 & 328 & 2260 & 709 & 13 \\ \hline
Canal & 200 & 765 & 1740 & 637 & 8 \\ \hline
Canal & 400 & 1588 & 2020 & 559 & 9 \\ \hline
Canal & 800 & 3176 & 3270 & 661 & 8 \\ \hline
Canal & 1600 & 6817 & 5030 & 654 & 17 \\ \hline
Canal & 3200 & 13504 & 9100 & 736 & 33 \\ \hline
Canal & 6400 & 16961 & 10790 & 665 & 25 \\ \hline
RiverBodyOfWater & 50 & 192 & 2680 & 615 & 5 \\ \hline
RiverBodyOfWater & 75 & 291 & 2490 & 586 & 4 \\ \hline
RiverBodyOfWater & 100 & 1187 & 2700 & 624 & 7 \\ \hline
RiverBodyOfWater & 200 & 1769 & 4890 & 643 & 17 \\ \hline
RiverBodyOfWater & 400 & 1769 & 4840 & 641 & 8 \\ \hline
RiverBodyOfWater & 800 & 1769 & 4980 & 619 & 18 \\ \hline
Service & 50 & 615 & 1640 & 602 & 7 \\ \hline
Service & 100 & 1022 & 1790 & 562 & 6 \\ \hline
Service & 200 & 1852 & 2300 & 577 & 9 \\ \hline
Service & 400 & 3041 & 2880 & 601 & 6 \\ \hline
Service & 800 & 5437 & 4500 & 674 & 30 \\ \hline
Service & 1600 & 7334 & 5350 & 639 & 26 \\ \hline
\end{tabular}
\caption{Execution times per RDF-Type, limited size of start-node subset (using the RiverBodyOfWater dataset)}
\label{table:runtimes_w_limit}