source: CMDI-Interoperability/CMD2RDF/trunk/docs/papers/2014-LREC-CMDcloud/CMDcloud.tex @ 4757

\documentclass[10pt, a4paper]{article}
\usepackage{lrec2014}

\usepackage{color}
\usepackage{graphicx}
\usepackage{amsmath}
%\usepackage{framed}
\usepackage{url}

%\documentclass{article}
%\documentclass{llncs}
%\usepackage{llncsdoc}
%\usepackage{color}
%\usepackage{graphicx}
%\usepackage{amsmath}

%\newcommand{\comment}[1]{}
\newcommand{\comment}[1]{\textcolor{red}{#1}}

%%% PAGE DIMENSIONS
%\usepackage{geometry} % to change the page dimensions
%\geometry{a4paper} % or letterpaper (US) or a5paper or....
%\geometry{margin=2.5cm} % for example, change the margins to 2 inches all round
%\topmargin=-0.6in
%\textheight=700pt
% \geometry{landscape} % set up the page for landscape
%   read geometry.pdf for detailed page layout information


%

\title{The CMD Cloud}

\name{Matej \v{D}ur\v{c}o, Menzo Windhouwer}


\address{ Institute for Corpus Linguistics and Text Technology (ICLTT), The Language Archive - DANS \\
               Vienna, Austria, The Hague, The Netherlands \\
               matej.durco@oeaw.ac.at, menzo.windhouwer@dans.knaw.nl\\}


\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.
}

%%
%\begin{keywords}
%semantic mapping, metadata, research infrastructure
%%metamodel, research infrastructure
%\end{keywords}
%

\begin{document}

\maketitleabstract

\section{Introduction}
%

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.

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.
As the main contribution -- grounded in the semantic mapping mechanisms of CMDI -- we propose a mechanism to compute and explore the relation/similarity among the profiles defined in CMD, delivering a bigger overall picture of the domain.

\begin{table*}
%\begin{table}[t]
\caption{The development of defined profiles and DCs over time.}
\label{table:dev}
\begin{center}
  \begin{tabular}{ l | r | r | r | r }
    \hline
date     & 2011-01 & 2012-06 & 2013-01 & 2013-06  \\
    \hline
Profiles & 40 & 53 & 87 & 124 \\
Components & 164 & 298 & 542 & 828 \\
%Expanded Components & 1055 & 1536 & 2904 & 5757 \\
Elements & 511 & 893 & 1505 & 2399 \\
% 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\% \\
% Components with DCs & 28 & 67 & 115 & 140 \\
    \hline
  \end{tabular}
\end{center}
%\end{table}
\end{table*}

%
\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. 
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). 
%(\cite{shvaiko2012ontology} even somewhat self-critically asks if after years of research``the field of ontology matching [is] still making progress?'')

%However there is a fundamental difference between the common approaches and the work presented here, in that the semantic %layer of the CMDI makes the shared semantics explicit, rendering complex matching algorithms unnecessary.

%\comment{OR (or some combination of the two)}

%due to the fact that we can harness 
%Although the semantic interoperability layer built into the core of the CMD Infrastructure, integrating the task of identifying semantic
Although the semantic layer of the CMD Infrastructure, which integrates the task of identifying semantic correspondences directly into the process of schema creation, makes to a high degree obsolete the need for complex a posteriori schema matching/mapping techniques, still, for the discussed task of schema similarity some of the techniques are relevant.
In particular, we would like to point out the work by Ehrig \cite{EhrigSure2004,Ehrig2006} who defines \emph{ontology mapping} as a function on individual ontology entities based on a \emph{similarity} function, that for a pair of entities from two ontologies computes a ratio indicating their semantic proximity. This ratio is further used to derive the \emph{ontology similarity}, operationalized as a weighted aggregation function \cite{ehrig2004qom}, combining individual similarity measures.


%Or put in terms of the schema matching methodology, the system relies on explicitly set concept equivalences as base for mapping between schema entities. By referencing a data category in a CMD element, the modeller binds this element to a concept, making two elements linked to the same data category trivially equivalent.

%\comment{Menzo: I suggest to trim the following section as in the previous paragraph we say these methods are mostly obsolete for CMDI and then we zoom in on them. In the final paper we can maybe make more clear how they are still relevant.}
%Still, we would like to point out the work by Ehrig on \emph{ontology alignment} \cite{EhrigSure2004,Ehrig2006}.
%%defining \var{ontology mapping} as a function applied on individual ontology entities that ``for each concept (node) in ontology A [tries to] find a corresponding concept (node), which has the same or similar semantics, in ontology B and vice verse''. 
%Ehrig introduces \emph{ontology mapping} as a function on individual ontology entities based on a \emph{similarity} function, that for a pair of entities from two ontologies computes a ratio indicating their semantic proximity. 
%This \emph{similarity} function %over single entities 
%is used to derive the notion of \emph{ontology similarity}, operationalized as a weighted aggregation function \cite{ehrig2004qom}, combining individual similarity measures.  computed for pairs of single entities again into one value (from the \emph{[0,1]} range) expressing the similarity ratio of the two ontologies being compared. 
%%Thus, \emph{ontology similarity} is a much weaker assertion, than \emph{ontology alignment}. In fact, the computed similarity is interpreted to assert ontology alignment: the aggregated similarity above a defined threshold indicates an alignment. 
%Based on this abstraction a large number of different comparison features, as summarized in \cite{Shvaiko2005_classification,Algergawy2010,shvaiko2012ontology}, can be integrated into one coherent model.

%%\begin{defcap}[!ht]
%%\caption{\emph{map} function for single entities and underlying \emph{similarity} function }
%%\begin{align*}
%\begin{equation}\begin{split}
%& map \ : O_{i1}  \rightarrow O_{i2} \\
%& map( e_{i_{1}j_{1}}) = e_{i_{2}j_{2}}\text{, if } sim(e_{i_{1}j_{1}},e_{i_{2}j_{2}}) \ \textgreater \ t  \text{ with } t \text{ being the threshold} \\
%& sim \ : E \times E \times O \times O \rightarrow [0,1]
%\end{split}\end{equation}
%%\end{align*} \end{defcap}

One inspiration for this work was also the well-known LOD cloud\url{\footnote{http://lod-cloud.net/}} \cite{Cyganiak2010}.

%
\section{The Component Metadata Infrastructure}
%
Naturally the core of CMDI consists of components. These components group metadata elements and possibly other components. The reusable components are managed by the Component Registry (CR). To describe a resource types a metadata modeller combines components from the CR into a metadata profile.
%A profile is a component which basically defines the root of the metadata records that instantiate the profile.
Due to the flexibility of this model the metadata structures can be very  specific to an organization, project or resource type. Although structures can thus vary considerably they are still within the domain of metadata for linguistic resources and thus share many key semantics. To deal with the variety general CMDI tools, e.g., the Virtual Language Observatory which is a facetted browser/search for CMD records, operate on a shared semantics layer. To establish these shared semantics CMD components, elements and values can be linked to so-called data categories (DC) defined in separate concept registries. The major concept registries currently in use by CMDI are the Dublin Core metadata elements and terms \cite{DCMI:2005} and the ISOcat Data Category Registry (DCR) \cite{Windhouwer+2012}. While the Dublin Core set of elements and terms is closed the ISOcat DCR is an open registry, which means that any metadata modeller can register the concepts it needs. Due to both the use of several concept registries and the open nature of some of these, multiple equivalent concepts can be created. CMDI uses the RELcat Relation Registry (RR) to create near sameness groups of these concepts.

%
\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 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.

Next to the `native' CMD profiles a number of profiles have been created that implement existing metadata formats, like OLAC/DCMI-terms, TEI Header or the META-SHARE schema. The resulting profiles proof the flexibility/expressi\-vi\-ty of the CMD metamodel. The individual profiles differ also very much in their structure -- next to flat profiles with just one level of components or elements with 5 to 20 fields (\textit{dublincore}, \textit{collection}, the set of \textit{Bamdes}-profiles) there are complex profiles with up to 10 levels (\textit{ExperimentProfile}, profiles for describing Web Services) and a few hundred elements, e.g., the maximum schema from the META-SHARE project \cite{Gavrilidou2012meta} for describing corpora has 117 components and 337 elements. 
%(when expanded\footnote{The reusability of components results in an element expansion, i.e., elements of a component (e.g. \textit{Contact}) included by three other components (\textit{Project}, \textit{Institution}, \textit{Access}) will appear three times in the instantiated record.}).


\subsubsection{Instance Data}

The main CLARIN OAI-PMH harvester\footnote{\url{http://catalog.clarin.eu/oai-harvester/}}
collects records from 69 providers on a daily basis. The complete dataset amounts to around half a million records.
16 of the providers offer CMDI records, the other 53 provide OLAC/DC records\label{info:olac-records}, that are being converted into the corresponding CMD profile after harvesting, amounting to round 139.000 records. %Next to these 81.226 original OLAC records, there a few providers offering their OLAC or DCMI-terms records already converted into CMDI, thus all in all OLAC, DCMI-terms records amount to 139.152.
On the other hand, some of the comparatively few providers of `native' CMD records expose multiple profiles (e.g. Meertens Institute uses 12 different profiles). So we encounter both situations: one profile being used by many providers and one provider using many profiles.

%\begin{table}
%\caption{Top 20 profiles, with the respective number of records}
%\begin{center}
%  \begin{tabular}{ r | l }
%\# records & profile \\
%   \hline
%155.403 & Song \\
%138.257 & Session \\
%92.996 & OLAC-DcmiTerms \\
%46.156 & DcmiTerms \\
%28.448 & SongScan \\
%21.256 & SourceScan \\
%19.059 & LiteraryCorpusProfile \\
%16519 & Source \\
%13626 & imdi-corpus \\
%10610 & media-session-profile \\
%7961 & SongAudio \\    
%7557 & SymbolicMusicNotation \\
%4485 & LCC DataProviderProfile \\
%4485 & SourceProfile \\
%4417 & Text \\
%1982 & Soundbites-recording \\
%1530 & Performer \\
%1475 & ArthurianFiction \\
%939 & LrtInventoryResource \\
%873 & teiHeader \\
%    \hline
%  \end{tabular}
%\end{center}
%\end{table}

%\begin{table}
%\caption{Top 20 collections, with the respective number of records}
%\begin{center}
% \begin{tabular}{ r | l }
%\# records & colleciton \\
%    \hline
%243.129 & Meertens collection: Liederenbank \\
%46.658 & DK-CLARIN Repository \\
%46.156 & Nederlands Instituut voor Beeld en Geluid Academia collectie \\
%29.266 & childes \\
%24.583 & DoBeS archive \\
%23.185 & Language and Cognition \\
%14.593 & talkbank \\
%14.363 & Acquisition \\
%14.320 & Institut fÃŒr Deutsche Sprache, CLARIN-D Zentrum, Mannheim \\
%12.893 & MPI CGN \\
%10.628 & Bavarian Archive for Speech Signals (BAS) \\
%7.964 & Pacific And Regional Archive for Digital Sources in Endangered Cultures\\
%7.348 & WALS RefDB \\
%5.689 & Lund Corpora \\
%4.640 & Oxford Text Archive \\
%4.492 & Leipzig Corpora Collection \\
%3.539 & Institut fÃŒr Deutsche Sprache, CLARIN-D Zentrum, Mannheim \\
%3.280 & A Digital Archive of Research Papers in Computational Linguistics \\
%3.147 & CLARIN NL \\
%3.081 & MPI fÃŒr Bildungsforschung \\    
%\hline
%  \end{tabular}
%\end{center}
%\end{table}

We can also observe a large disparity on the amount of records between individual providers and profiles. Almost half of all records is provided by the Meertens Institute (\textit{Liederenbank} and \textit{Soundbites} collections), another 25\% by MPI for Psycholinguistics (\textit{corpus} + \textit{Session} records from the \textit{The Language Archive}). On the other hand there are 25 profiles that have less than 10 instances. This can be owing both to the state of the respective project (resources and records still being prepared) and the modelled granularity level (collection vs. individual resource). There is ongoing work to make the various granularity levels more explicit.

\section{CMD cloud}

As the data set keeps growing both in numbers and in complexity, there is a rising need for advanced ways to explore it.
In this work, we present a method to analyze and visualize the relations among defined CMD profiles, 
with the \emph{schema matching} -- in particular, the mapping and similarity function proposed by \cite{EhrigSure2004,Ehrig2006} -- serving as methodical basis.
%formulated as an application of the \emph{schema matching} task. In particular,  the work on mapping and underlying similarity function as introduced in \cite{EhrigSure2004,Ehrig2006} shall serve as methodical basis.

\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.

\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. 
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.
Additionally, a variable threshold governs the level of similarity to be rendered as link.

\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 
data categories, computed as the average of the quotients of matching distinct data categories for each of the two profiles.

\begin{equation}\begin{split}
 sim_{p1} &:= \cfrac{count(distinct(Datcats_{match}))}{count(distinct(Datcats_{p1}))} \\
 sim_{p2} &:= \cfrac{count(distinct(Datcats_{match}))}{count(distinct(Datcats_{p2}))} \\
 sim &:= \frac{(sim_{p1} + sim_{p2})}{2}
\end{split}\end{equation}

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:
%\begin{description}
%\item[How to handle data categories used multiple times in one profile?] 
  %Count every data category only once.
%\item[How to incorporate reused components?] Count distinct identities, i.e. all of the shared structures, but every component or element only once, even if reused.
%\item[How to cater for missing data categories?] If data categories cover only a small portion of profiles fields, good matches with other profiles can result, even though the profiles can be quite different. This can be resolved by including the data-category-coverage-ratio into the calculation.

% \comment{Mate: I am afraid this is too bare of any statistical methods}
%\item[What to use as base/denominator for the quotient?] The overall number of used data categories in one of the profiles, the sum of both or average of the individual quotients.

%denominator of the quotient is the sum of data category counts from both profiles:
%\begin{equation}\begin{split}
% sim := \cfrac{count(distinct(Datcats_{match}))}{count(distinct(datcats_{p1})) + count(distinct(datcats_{p2}))}
%\end{split}\end{equation}
%\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.


\begin{figure*}
\begin{center}
\hspace{-0.1\textwidth}\includegraphics[width=1.1\textwidth]{just_profiles_9}
\end{center}
\caption{A graph view of the similarity relations between CMD profiles (\textit{threshold=0.6})}
\label{fig:CMDcloud}
\end{figure*}

\subsection{Possible 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}.

\section{Conclusions} % and Future Work is already in the extensions
In this paper, we gave a short overview of the current status of the CMD data domain as basis for the main contribution: an analysis of the semantic similarity between the profiles.
%In our view, this work does not just render a fancy picture, but 
This work offering a bird's eye view on the CMD data domain can serve as alternative starting point for exploring the dataset and provides valuable input for metadata modellers and the metadata curation task.

\bibliographystyle{lrec2014}
\bibliography{CMDcloud}

\end{document}