Changeset 4820 for CMDI-Interoperability

Timestamp:

03/22/14 07:49:48 (10 years ago)

Author:

xnrn@gmx.net

Message:

update for final version
added abstract and some paragraphs

Location:

CMDI-Interoperability/CMD2RDF/trunk/docs/papers/2014-LREC-CMDcloud

Files:

• CMDcloud.pdf (added)
• CMDcloud.tex (modified) (12 diffs)

CMDI-Interoperability/CMD2RDF/trunk/docs/papers/2014-LREC-CMDcloud/CMDcloud.tex

@@ -41 +41 @@
 
 \abstract{
-The CLARIN Component Metadata Infrastructure (CMDI) established means for flexible resouce descriptions for the domain of language resources with sound provisions for semantic interoperability weaved deeply into the meta model and the modules of the infrastructure. Based on this solid grounding, the infrastructure accomodates a growing collection of metadata records. 
-In this paper, we give a short overview of the current status in the CMD data domain and harness the installed mechanisms for semantic interoperability to explore the relations/ similarity between individual profiles/schemas.
+The CLARIN Component Metadata Infrastructure (CMDI) established means for flexible resource descriptions for the domain of language resources with sound provisions for semantic interoperability weaved deeply into the meta model and the modules of the infrastructure. Based on this solid grounding, the infrastructure accommodates a growing collection of metadata records. 
+In this paper, we give a short overview of the current status in the CMD data domain on the schema and instance level and harness the installed mechanisms for semantic interoperability to explore the similarity relations between individual profiles/schemas. We propose a method to use the semantic links shared among the profiles to generate/compile a similarity graph. This information is further rendered in an interactive graph viewer – the SMC Browser. The resulting interactive graph offers an intuitive view on the complex interrelations of the discussed dataset revealing clusters of more similar profiles. This information is useful both for metdata modeller, for metadata curation task as well as for general audience seeking for a 'big picture' of the complex CMD data domain.
 }
 
@@ -61 +61 @@
 The Component Metadata Infrastructure (CMDI, \cite{Broeder+2010}) conceived within the CLARIN project is now 5 years old and thriving. By allowing a flexible yet harmonized definition of metadata schemas, it has offered a robust common framework for consolidating the scattered landscape of resource descriptions in the LRT community, without trying to impose/prescribe one schema to cover all the resources (which seems futile in the light of the variety of resources to be described).
 
-A look into the data domain shows that the fundamental concept of a flexible metamodel with integrated semantic layer is being taken up by the community. Metadata modellers are increasingly making use not only of the infrastructure, but are also reusing the modelling work done so far.
+A look into the data domain shows that the basic concept of a flexible metamodel with integrated semantic layer is being taken up by the community. Metadata modellers are increasingly making use not only of the infrastructure, but are also reusing the modelling work done so far.
 
 In this paper, we first -- for methodical foundation -- briefly summarize previous work, then give a short overview of the current status of the infrastructure both on the schema and instance level.
@@ -71 +71 @@
 \label{table:dev}
 \begin{center}
-  \begin{tabular}{ l | r | r | r | r }
+  \begin{tabular}{ l | r | r | r | r | r}
     \hline
-date     & 2011-01 & 2012-06 & 2013-01 & 2013-06  \\
+     & 2011-01 & 2012-06 & 2013-01 & 2013-06  & 2014-01 \\
     \hline
-Profiles & 40 & 53 & 87 & 124 \\
-Components & 164 & 298 & 542 & 828 \\
+Profiles & 40 & 53 & 87 & 124 &  158\\
+Components & 164 & 298 & 542 & 828 & 1110 \\
 %Expanded Components & 1055 & 1536 & 2904 & 5757 \\
-Elements & 511 & 893 & 1505 & 2399 \\
+Elements & 511 & 893 & 1505 & 2399 & 3101 \\
 % Expanded Elements & 1971 & 3030 & 5754 & 13232 \\
-Distinct data categories & 203 & 266 & 436 & 499 \\
-Data categories in the Metadata profile & 277 & 712 & 774 & 791 \\
-Ratio of elements without DCs & 24,7\% & 17,6\% & 21,5\% & 26,5\% \\
+Distinct data categories & 203 & 266 & 436 & 499 & 737 \\
+% Data categories in the Metadata profile & 277 & 712 & 774 & 791 \\
+Ratio of elements without DCs & 24,7\% & 17,6\% & 21,5\% & 26,5\% & 24,2\%\\
 % Components with DCs & 28 & 67 & 115 & 140 \\
     \hline
@@ -93 +93 @@
 \section{Previous work}\label{lit}
 %
-Our task of determining similarity between schemas is a variant of the schema/ontology matching problem. % -- trying to find correspondences between two schemas. 
+Our task of determining similarity between schemas can be formulated as the schema/ontology matching problem. % -- trying to find correspondences between two schemas. 
 There is a plethora of work on methods and technology in the field of \emph{schema and ontology matching} as witnessed by a sizable number of publications providing overviews, surveys and classifications of existing work %\cite{Kalfoglou2003,Shvaiko2008,Noy2005_ontologyalignment,Noy2004_semanticintegration,Shvaiko2005_classification} 
 (\cite{Kalfoglou2003,Noy2005_ontologyalignment,shvaiko2012ontology,amrouch2012survey} and more). 
@@ -129 +129 @@
 %%\end{align*} \end{defcap}
 
-One inspiration for this work was also the well-known LOD cloud\url{\footnote{http://lod-cloud.net/}} \cite{Cyganiak2010}.
+One inspiration for this work was also the well-known LOD cloud\footnote{\url{http://lod-cloud.net/}} \cite{Cyganiak2010}.
 
 %
@@ -141 +141 @@
 \section{Current status of the joint CMD Domain}
 %
-%In the following section, we give an overview of the current status in the CMD domain, both on the schema level, i.e. with regard to the defined profiles and data categories used, as well as on the instance level, the actual CMD records.
-
-\subsubsection{CMD Profiles }
+In the following section, we give an overview of the current status in the CMD domain, both on the schema level, i.e. with regard to the defined profiles and data categories used, as well as on the instance level, the actual CMD records.
+
+\subsection{CMD Profiles }
 In the CR 133\footnote{All numbers are as of 2013-09 if not stated otherwise} public Profiles and 696 Components are defined. Table \ref{table:dev} shows the development of the CR and DCR population over time.
 
@@ -150 +150 @@
 
 
-\subsubsection{Instance Data}
+\subsection{Instance Data}
 
 The main CLARIN OAI-PMH harvester\footnote{\url{http://catalog.clarin.eu/oai-harvester/}}
@@ -229 +229 @@
 
 \subsection{SMC browser}
-The technological base for the presented method is the \textit{SMC browser}\footnote{\url{http://clarin.aac.ac.at/smc-browser}}, a web application being developed by the CMDI team, that lets the metadata modeller explore the information about profiles, components, elements and the usage of DCs as an interactive graph. This allows for example to examine the reuse of components or DCs in different profiles. The graph is accompanied by statistical information about individual `nodes', e.g., counting how many elements a profiles contains, or in how many profiles a DC is used.
+The technological base for the presented method is the \textit{SMC browser}\footnote{\url{http://clarin.oeaw.ac.at/smc-browser}}, a web application being developed by the CMDI team, that lets the metadata modeller explore the information about profiles, components, elements and the usage of DCs as an interactive graph. This allows for example to examine the reuse of components or DCs in different profiles. The graph is accompanied by statistical information about individual `nodes', e.g., counting how many elements a profiles contains, or in how many profiles a DC is used.
 
 \subsection{Basic approach}
 The basic idea for constructing the CMD cloud is to
-1) collect the size of each profile (as the number of components and elements, or number of distinct data categories used); 2) compute the pairwise similarity ratio between the profiles based on some similarity measure; 3) generate a graph with profiles as nodes and the pairwise similarity relation expressed as edges between them.
-The size of the nodes in the graph reflects the size of the profile as computed before. 
+1) collect the size of each profile (as the number of components and elements, or number of distinct data categories used); 2) compute the pairwise similarity ratio between the profiles based on some similarity measure; 3) generate a graph with profiles as nodes and the pairwise similarity relation expressed as weighted edges between them.
+When rendered, the size of the nodes in the graph reflects the size of the profile as computed before. 
 The absolute number of matching identities is expressed as edge weight and the similarity ratio
 as \emph{link strength} (inversely proportional to link distance), drawing more similar profiles nearer together.
@@ -240 +240 @@
 
 \subsection{Similarity ratio}
-In the first level of this experiment, the similarity ratio is based on the most reliable information, the reuse of % components and/or 
+At the core of the discussed method is the concept of similarity between entities and the challenge how to operationalize it.
+In the initial step, the similarity ratio is based on the most reliable information, the reuse of % components and/or 
 data categories, computed as the average of the quotients of matching distinct data categories for each of the two profiles.
 
@@ -250 +251 @@
 
 Note though, that there is a number of other features and formulas that can be used to assess the similarity of two schemas (structures) (cf. \ref{ext}).  
+
 
 %and several factors that need to be considered even with this presumably simple feature:
@@ -267 +269 @@
 %\end{description}
 
-Initial results showed that there is a very high degree of interconnectedness in the generated graph, (There are 7.835 links between the 157 profiles. A fully connected graph would have 12.403 edges.) resulting from the fact, that every profile shares at least one or two data categories with many other profiles. However, besides making the resulting graph illegible and difficult to lay out, such a result is also not a good answer to the question of similarity. Therefore a threshold has to be introduced to only consider links above a certain similarity ratio.
-
-\subsection{Result}
-As SMC browser allows to select different subgraphs and adapt layout options, figure \ref{fig:CMDcloud} depicts just one possible visual output of the analysis. The graph shows nicely the clusters of strongly related profiles in contrast to the greater distances between more loosely connected profiles.
-
+
+\subsection{Results}
+The basic result is the graph of profiles with links based on their similarity. There are various ways to render this information.
+As SMC browser allows to select different subgraphs and adapt layout options, figure \ref{fig:CMDcloud} depicts just one possible visual output of the analysis. This view shows nicely the clusters of strongly related profiles in contrast to the greater distances between more loosely connected profiles. SMC Browser also features alternative more detailed views that allow to detect visually which components and data categories are shared by which profiles. In a way a zoom in on the links between the nodes in the CMD cloud.
+
+The generated graph manifests a very high degree of interconnectedness in the generated graph (There are 7.835 links between the 157 profiles. A fully connected graph would have 12.403 edges.) resulting from the fact, that every profile shares at least one or two data categories with many other profiles. However, besides making the rendered graph illegible and difficult to lay out, such a result is also not a good answer to the question of similarity. Therefore a threshold was introduced to only consider links above a certain similarity ratio.
+
+\subsection{Applications}
+The SMC Browser and CMD cloud were developed primarily for assisting the task of metadata modelling. A modeller can get a quick overview of the existing profiles, their structure and their interrelations, allowing her to choose the most suitable one for describing the resources at hand. 
+
+When enriched with statistical information about instance data it can also serve as an alternative advanced interface for exploring the joint CLARIN metadata domain. It will offer the much needed 'big picture' for this huge heterogeneous collection of resources, an intuitively comprehensible visualization of its complex interrelations. This makes the tool also applicable for the metadata curation task, allowing to easily recognize structures and values that are being reused often ('hot spots') in contrast to outliers ('weak links'). With appropriate linking established the user can get from the structural overview (graph) directly to the corresponding records.
 
 \begin{figure*}
 \begin{center}
-\hspace{-0.1\textwidth}\includegraphics[width=1.1\textwidth]{just_profiles_9}
+%\hspace{-0.1\textwidth}
+\includegraphics[width=\textwidth]{just_profiles_9}
 \end{center}
 \caption{A graph view of the similarity relations between CMD profiles (\textit{threshold=0.6})}
@@ -281 +290 @@
 \end{figure*}
 
-\subsection{Possible extensions}
+\subsection{Planned extensions}
 \label{ext}
-There are a number of further factors, that could be taken into account, when computing the profiles similarity.  
-The obvious next step is to consider the component reuse. Applying the relations between data categories as defined in Relation Registry would further grow the similarity ratios. Also, we need to cater for profiles with little data categories coverage. This can be resolved by including the data-category-coverage-ratio into the calculation. One could compute the graph on the basis of the instance data, with the node size representing the number of instances and edge width the amount of data in the shared data categories. 
-
-We also plan to adopt more sophisticated approaches to compute entity and aggregated schema similarity as proposed in \cite{ehrig2004qom,Ehrig2006}.
+There are a number of further factors, that can be taken into account, when computing the profiles similarity.  
+The obvious next step is to consider the component reuse. Applying the relations between data categories as defined in Relation Registry would further raise the similarity ratios. Also, we need to cater for profiles with little data categories coverage. This can be resolved by including the data-category-coverage-ratio into the calculation. 
+
+We also plan to adopt more sophisticated approaches to compute entity and aggregated schema similarity as proposed in \cite{ehrig2004qom,Ehrig2006}, like string or structural similarity between 'nodes'.
+
+A very important planned addition opening a whole new field of applications is to integrate statistical information about instance data into the generation of the graph. In the 'instance'-mode node size would represent the number of instances for given profile and edge width the amount of data in the shared data categories.  On instance level, also the ratio of shared values between fields/elements  could be computed and used as another similarity indicator (though computationally very demanding).
+
 
 \section{Conclusions} % and Future Work is already in the extensions