Changeset 3818 for CMDI-Interoperability

Timestamp:

10/20/13 21:22:19 (11 years ago)

Author:

vronk

Message:

cleaned up paper, reflecting latest additions to general.ttl (:Resource)

Location:

CMDI-Interoperability/CMD2RDF/trunk

Files:

• data/general.ttl (modified) (1 diff)
• docs/papers/2014-LREC/CMD2RDF.tex (modified) (20 diffs)

CMDI-Interoperability/CMD2RDF/trunk/data/general.ttl

@@ -71 +71 @@
 #COMMENT Matej: I still yearn for something like cmdm:Resource and cmdm:MDRecord
 #COMMENT Menzo: Added cmdm:Resource
+
+#COMMENT matej: isn't there a loop now? Is it meant like this?:
+# <lr1> a  :Resource; :hasResourceType :Resource.
+# <lr1.cmd> a  :Resource; :hasResourceType  :Metadata.
+                                

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

@@ -71 +71 @@
 \maketitle
 %
-\begin{abstract}
-The hype/trend to Web of Data...
-
-Although semantic interoperability has been one of the main motivation for CLARIN Component Metadata Infrastructure, until now there has been no work on the obvious -- bringing CMDI to Semantic Web. We believe that providing the whole of CMD data  as Linked Open Data linked with external semantic resources, will allow to fully exploit the power of semantic technologies and opens a new level of processing and exploring of CMD data. In this paper, we propose an expression of the whole of the CMD data domain (from meta model to individual metadata records) in RDF.
-
-\commentx{Menzo: I don't think we can express CMD data automatically as an ontology. For that too many semantics are still hidden in CMDI. We are building blocks (e.g., RR/CLAVAS) that might enable us to do so in the future, but I think its better now to go for CMD as LOD linked into the LOD cloud ...}
-
-\end{abstract}
+%\begin{abstract}
+%\end{abstract}
 %
 \begin{keywords}
@@ -85 +79 @@
 \end{keywords}
 %
-\section{Introduction}
-%
-\commentx{Not sure how much of the introduction, CMD explain + Status of the data domain we want, may and need to reuse between the two papers...}
-
-
-The hype/trend to Web of Data...
-
-In this paper, we lay out how individual parts of the CMD framework can be expressed in RDF interlinked with existing external semantic resources (ontologies, knowledge bases, vocabularies). This conversion lays a foundation / is groundwork for providing the original dataset as a \emph{Linked Open Data} nucleus within the \emph{Web of Data}\cite{TimBL2006} as well as for real semantic (ontology-driven) search and exploration of the data.
+\section{Motivation}
+%
+Although semantic interoperability has been one of the main motivation for CLARIN Component Metadata Infrastructure, until now there has been no work on the obvious -- bringing CMDI to Semantic Web. We believe that providing the whole of CMD data  as Linked Open Data linked with external semantic resources, will opens a whole new level of processing and exploring of the CMD data by employing the power of semantic technologies. In this paper, we lay out how individual parts of the CMD framework can be expressed in RDF and interlinked with existing external semantic resources (ontologies, knowledge bases, vocabularies). 
+%This conversion lays a foundation / is groundwork for providing the original dataset as a \emph{Linked Open Data} nucleus within the \emph{Web of Data}\cite{TimBL2006} as well as for real semantic (ontology-driven) search and exploration of the data.
 
 %
@@ -131 +121 @@
 \section{CMD to RDF}
 \label{sec:cmd2rdf}
-In this section, RDF encoding is proposed for all levels of the CMD data domain: 
+In the following, RDF encoding is proposed for all levels of the CMD data domain: 
 
 \begin{itemize}
@@ -144 +134 @@
 The main entity of the meta model is the CMD component modelled as \code{rdfs:Class}. CMD profile is basically a CMD component with some extra features, implying a specialization relation. It may seem natural to translate a CMD element to a RDF property (as it holds the literal value), but given its complexity (e.g. attributes, relation to the containing component)  it too has to be a \code{rdfs:Class}. The actual literal value is a property of given element of type \code{cmdm:ElementValue}. For values that can be mapped to entities defined in external vocabularies/ semantic resources, the references to these entities are expressed in parallel properties of type \code{cmdm:ElementEntity}. The attributes are modelled analogously with \code{cmdm:Attribute, cmdm:AttributeValue, cmdm:AttributeEntity}. 
 
-The containment relation between components and elements is expressed with a dedicated property \code{cmdm:contains}, again analogously for attributes of individual components and elements \code{cmdm:containsAttribute}.
+The containment relation between components and elements is expressed with a dedicated property \code{cmdm:contains}, attributes of individual components and elements are bound with \code{cmdm:containsAttribute}.
 
 \label{table:rdf-spec}
@@ -156 +146 @@
 %cmdm:Attribute & a & rdfs:Class .  \\
 \\
-\multicolumn{3}{l}{\# basic CMD nexting}  \\
+\multicolumn{3}{l}{\# basic CMD nesting}  \\
 cmdm:contains & a & rdf:Property ; \\
         & rdfs:domain & cmdm:Component ; \\
@@ -191 +181 @@
 \noindent
 This entities are used for modelling the actual profiles, components and elements as they are defined in the Component Registry.
-For stand-alone/top components, the IDs as issued by Component Registry can be used as entity IRIs. For ``inner'' components (that are defined as part of another component) and elements the identifier is a concatenation of the parent top component and dot-path to given component/element (Actor: \code{cr:clarin.eu:cr1:c\_1271859438197/rdf\#Actor\_Languages.Actor\_Language}).
-
-\commentx{Matej: shouldn't we add the name of the component in the IRI for human-readability?
-similar to how it is generated in profile XSDs: \textless xs:simpleType name="simpletype-MimeType-clarin.eu.cr1.c\_1290431694511"\textgreater }
-
-\commentx{Menzo: the IRI is the exact path into the CR to get the RDF representation for the profile/component. I think it should stay like that because you need to be able to fetch it to get, for example, the dcr:datcat mappings. Actually the profile/component name is there as its (in general) the first component name after the '\#'.}
+For stand-alone/top components, the IRI is the exact path into the CR to get the RDF representation for the profile/component. For ``inner'' components (that are defined as part of another component) and elements the identifier is a concatenation of the parent top component IRI and dot-path to given component/element (Actor: \code{cr:clarin.eu:cr1:c\_1271859438197/rdf\#Actor.Actor\_Languages.Actor\_Language}).\footnote{For the sake of readability, we will collapse the component IRIs, stripping them of the identifier and referring to them just by their name, prefixed with \code{cmd:}.}
 
 \label{table:rdf-cmd}
@@ -208 +193 @@
 
 \commentx{Menzo: we need more context for inner components. In the example LanguageName looks well defined, but take a Component/Element like Title. Is it the title of a book or the title of a person. Only when the semantics are clear, e.g., with a dcr:datcat, one can ignore the context and collapse all Components/Elements to a single RDF class/property.}
-\commentx{Matej: wouldn't that be remedied by cmdm:contains? or is it too much inferencing?}
 
 \begin{notex}
 Menzo: inner components don't have IDs so I propose a path build from the context up to a shareable component (we need some nice term for that, in the TDS I called it a top notion so maybe a top component. The cmd prefix also needs to be bound to a component specific URI. This URI contains the top component ID, e.g., \furl{http://catalog.clarin.eu/ds/ComponentRegistry/rest/registry/components/clarin.eu:cr1:c\_1271859438125/rdf}.
 \end{notex}
+\commentx{Matej: see above - treated sufficiently?}
 
 \subsection{Data Categories}
@@ -231 +216 @@
 \end{example3}
 
-\begin{comment}
-Encoding data categories as annotation properties is in contrast to the common approach seen with dublincore terms
-used usually directly as data properties:
-
-\begin{example3}
-<lr1> & dc:title & "Language Resource 1"
-\end{example3}
-
-However, e argue against direct mapping of complex data categories to data properties and in favour of modelling data categories as annotation properties, 
-In a specific (OWL 2) application the relation with the data categories can be expressed as \code{owl:equivalentClass} for classes, \code{owl:equivalentProperty} for properties or \code{owl:sameAs} for individuals:
-
-\begin{example3} 
-\#myPOS & owl:equivalentClass & isocat:DC-1345. \\
-\#myPOS & owl:equivalentProperty & isocat:DC-1345. \\
-\#myNoun & owl:sameAs & isocat:DC-1333. \\
-\end{example3}
- 
-\end{comment}
 
 \subsection{RELcat - Ontological relations}
-As described in \ref{CMDI}, relations between data categories are not stored directly in the \xne{ISOcat} DCR, but rather in a dedicated module the Relation Registry \xne{RELcat}. The relations here are grouped into relation sets and stored as RDF triples\cite{SchuurmanWindhouwer2011}. A sample relation from the \xne{CMDI} relation set expressing a number of equivalences between \xne{ISOcat} data categories and \xne{dublincore} terms: 
+\commentx{for now we could probably skip all of relcat (although it is the future of semantic mapping ;) - we spare something for the next paper.}
+
+As described in \ref{CMDI}, relations between data categories are not stored directly in the \xne{ISOcat} DCR, but rather in a dedicated module the Relation Registry \xne{RELcat}. The relations here are grouped into relation sets and stored as RDF triples \cite{SchuurmanWindhouwer2011}. A sample relation from the \xne{CMDI} relation set expressing a number of equivalences between \xne{ISOcat} data categories and \xne{dublincore} terms: 
 
 \begin{example3}
@@ -265 +234 @@
 
 \commentx{Menzo: I would use owl:sameAs rdfs:subPropertyOf rel:sameAs. I see the rel:* properties as an upper layer of a taxonony of relation types. The RELcat types are loose and the OWL ones specific, hence the subtyping. In RELcat you might also query multiple graphs with multiple vocabularies various 'same-as' properties then still need to be distinguishable but the general rel:sameAs need to be created.}
-
-\commentx{Matej: strip this stipulations - rest of the subsection or just short referrer to SPIN rules ?}
-\begin{comment}
-Is this correct:
-?? That means, that to be able to infer that a value in a CMD element also pertains to a given data category, e.g.:
-
-\begin{example2}
- cmd:PublicationYear = 2012 $\rightarrow$ & dc:created = 2012 
-\end{example2}
-
-\commentx{Menzo: yes. I do have some of the SPIN rules somewhere to generate those. My idea is that one takes a dcr:datcat annotated graph. This can be using OWL or SKOS or any other RDF vocabulary. This base graph should have been expanded depending on the reasoning one uses, i.e., all entailments are in place. The dcr:datcat can then be translated into rel:sameAs and all equivalences get expanded, so one can also query using ISOcat DCs.}
-
-\noindent
-following facts need to be present in the ontology :
-
-\begin{example3}
-<lr1> & cmd:PublicationYear & 2012\^{}\^{}xs:year \\
-cmd:PublicationYear &  owl:equivalentProperty & isocat:DC-2538 \\
-isocat:DC-2538 & rel:sameAs & dc:created \\
-owl:sameAs & rdfs:subPropertyOf &  rel:sameAs \\
-$\rightarrow$ \\
-<lr1> & dc:created & 2012\^{}\^{}xs:year \\
-\end{example3}
-\end{comment}
 
 
@@ -295 +240 @@
 In the next step, we want to express the individual CMD instances, the metadata records.
 
+We provide a generic top level class for all resources (including metadata records), the \code{cmdm:Resource}, 
+with \code{cmdm:hasResourceType} and  \code{cmdm:hasMimeType} predicates to type the resources.
+
+\commentx{not sure about the use of \code{cmdm:Resource}. both as rdf:type and :hasResourcetype? - see comment in general.ttl}
+
+\begin{example3}
+<lr1> & a  & cmdm:Resource; \\
+& cmdm:hasResourceType & cmdm:Resource. \\
+<lr1.cmd> & a  & cmdm:Resource; \\
+& cmdm:hasResourceType & cmdm:Metadata. \\
+\end{example3}
+
 \subsubsection {Resource Identifier}
-
-\commentx{Matej: I still yearn for something like cmdm:Resource and cmdm:MDRecord}
-\begin{example3}
-<lr1> & a  & cmdm:Resource; \\
-<lr1.cmd> & a & cmdm:MDRecord;
-\end{example3}
 
 It seems natural to use the PID of a Language Resource ( \code{<lr1>} ) as the resource identifier for the subject in the RDF representation. While this seems semantically sound, not every resource has to have a PID. (This is especially the case for ``virtual'' resources like collections, that are solely defined by their constituents and don't have any data on their own.) As a fall-back the PID of the MD record ( \code{<lr1.cmd>}  from \code{cmd:MdSelfLink} element) could be used as the resource identifier. 
 If identifiers are present for both resource and metadata, the relationship between the resource and the metadata record can be expressed as an annotation using the \xne{OpenAnnotation} vocabulary\furl{http://openannotation.org/spec/core/core.html\#Motivations}.
-(Note also, that one MD record can describe multiple resources, this can be also easily accommodated in OpenAnnotation:
+(Note, that one MD record can describe multiple resources. This can be also easily accommodated in OpenAnnotation).
 
 \commentx{Menzo: also there can be multiple resource proxies. Maybe we can use an RDF list?} 
@@ -359 +310 @@
 \begin{example3}
 \_:actor1  & a & cmd:Actor . \\
-?? <lr1> ? & cmd:contains & \_:actor1 . \\
-?? <lr1.cmd> ? & cmd:contains & \_:actor1 . \\
+\_:actor1lang1  & a & cmd:Actor.Actor\_Language . \\
+\_:actor1  & cmd:contains & \_:actor1lang1 . \\
+\end{example3}
+
+Additionally, we have to hook the top component to its containing metadata record.
+
+\begin{example3}
+\_:coll1 & a & cmd:collection. \\
+\_:coll1 & cmdm:describesResource & <lr1.cmd> . \\
 \end{example3}
 
@@ -367 +325 @@
 As explained before, CMD elements have to be typed as \code{rdfs:Class}, the actual value expressed as \code{cmds:ElementValue} property and the corresponding data category expressed as annotation property.
 
-While generating triples with literal values seems straightforward, the more challenging but also more valuable aspect is to generate object property triples with the literal values mapped to semantic entities. Following example show the whole chain of statements from metamodel to literal value. The mapping process is detailed in \ref{sec:values2entities}.
+While generating triples with literal values seems straightforward, the more challenging but also more valuable aspect is to generate object property triples (predicate \code{cmdm:hasElementEntity}) with the literal values mapped to semantic entities. Following example shows the whole chain of statements from metamodel to literal value and corresponding semantic entity. The mapping process is detailed in next section \ref{sec:values2entities}.
 
 \begin{example3}
@@ -391 +349 @@
 \end{example3}
 
-\begin{comment}
-\begin{example3}
-cmd:timeCoverage  & a   & cmds:Element \\
-cmd:timeCoverageValue & a & cmds:ElementValue \\
-cmd:timeCoverage  & dcr:datcat  & isocat:DC-2502 \\
-<lr1> & cmd:contains & \_:timeCoverage1 \\
-\_:timeCoverage1 & a & cmd:timeCoverage \\
-\_:timeCoverage1 & cmd:timeCoverageValue & "19th century" \\
-\end{example3}
-
-\commentx{Menzo: no need to repeat dcr:datcat in the instance.}
-
-\begin{example3}
-\var{cmds:Element} & \var{cmds:ElementValue\_?} & \var{xsd:anyURI}\\
-\_:organisation1 & cmd:OrganisationValue\_? & <org1> \\
-\end{example3}
-
-\begin{notex}
-Don't we need a separate property (predicate) for the triples with object properties pointing to entities, 
-i.e. \code{cmd:Organisation\_} additionally to \code{cmd:Organisation} 
-\end{notex}
-\end{comment}
-
 
 %%%%%%%%%%%%%%%%%
@@ -421 +356 @@
 \commentx{this is probably definitely too much for one abstract  - so we could just anounce the need for this mapping process.}
 
-This task is a prerequisite to be able to express also the CMD instance data in RDF. The main idea is to find entities in selected reference datasets (controlled vocabularies, ontologies) matching the literal values in the metadata records. The obtained entity identifiers are further used to generate new RDF triples. It involves following steps:
+This task is a prerequisite to be able to express also the CMD instance data in RDF. The main idea is to find entities in selected reference datasets (controlled vocabularies, ontologies) matching the literal values in the metadata records. The obtained entity identifiers are further used to generate new RDF triples, representing outbound links.
+
+It involves following steps:
 
 \begin{enumerate}
@@ -434 +371 @@
 % This is almost equivalent to the definition of ontology mapping function as given by \cite{EhrigSure2004, amrouch2012survey}: ``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''.
 
-The transformation of the data has been partly described in previous section. It can be trivially automatically converted into RDF triples as :
-
-\begin{example3}
-\_:organisation1 & \multicolumn{2}{l}{cmd:hasOrganisationElementValue \quad 'MPI'\^{}\^{}xs:string ;} \\
-\end{example3}
-
-However for the needs of the mapping task we propose to reduce and rewrite to retrieve distinct concept-value pairs:
+\subsubsection{Identify vocabularies}
+
+One generic way to indicate vocabularies for given metadata fields or data categories being discussed in the CMD community is to use dedicated annotation property in the schema or data category definition (tentatively labeled \code{@clavas:vocabulary}, cf: \emph{CMD 1.2}). 
+
+The primary provider of relevant vocabularies is \xne{ISOcat} and \xne{CLAVAS} – a service for managing and providing vocabularies in SKOS format. However, in general we have to assume/consider a number of different sources.
+
+\subsubsection{Extract input data}
+Starting from the literal triples as defined in previous section (\code{cmdm:hasElementValue}) we aggregate the elemnt values to retrieve distinct \emph{concept-value pairs}:
 
 \begin{example3}
@@ -447 +385 @@
 \end{example3}
 
-\subsubsection{Identify vocabularies}
-
-One generic way to indicate vocabularies for given metadata fields or data categories being discussed in the CMD community is to use dedicated annotation property in the schema or data category definition (tentatively labeled \code{@clavas:vocabulary}, cf: \emph{CMD 1.2}). 
-
-The primary provider of relevant vocabularies is \xne{ISOcat} and \xne{CLAVAS} – a service for managing and providing vocabularies in SKOS format. However, in general we have to assume/consider a number of different sources.
-
 \subsubsection{Lookup}
 
-In abstract term, the lookup function takes as input the identifier of data category (or CMD element) and a literal string value and returns a list of potentially matching entities. Before actual lookup, there may have to be some string-normalizing preprocessing.
+In abstract terms, the lookup function takes as input the identifier of data category (or CMD element) and a literal string value and returns a list of potentially matching entities, ideally with some confidence score. Before actual lookup, there may have to be some string-normalizing preprocessing.
 
 %\begin{definition}[{signature of the lookup function}]
 \begin{equation}
-lookup \ ( \ DataCategory \ ,  \ Literal \ )  \quad \mapsto \quad ( \ Concept \ | \ Entity \ )*
+lookup \ ( \ DataCategory \ ,  \ Literal \ )  \quad \mapsto \quad ( \ \textless  Concept \ | \ Entity  ,\ confidenceScore \textgreater \ )*
 \end{equation}
 %\end{definition}
 
 In the implementation, there needs to be additional initial configuration input, identifying datasets for given data categories, 
-which will be the result of the previous step \
+which will be the result of the previous step -- identification of vocabularies. \
 
 
@@ -475 +407 @@
 
 As for the implementation, in the initial setup the system could resort to the \code{find}-interface provided by \xne{OpenSKOS}.
-However, in the long term a more general solution is required, a kind of hybrid \emph{vocabulary proxy service} that allows to search in a number of datasets, many of them distributed and available via different interfaces.
+However, in the long term a more general solution is required, a kind of hybrid \emph{vocabulary proxy service} that allows to search in a number of datasets, many of them distributed and available via varying interfaces.
 
 \subsubsection{Candidate evaluation}
@@ -484 +416 @@
 \section{Implementation}
 
-The transformation of profiles and instances into RDF/XML is accomplished by a set of XSL-stylesheets.
-Once the data is available it has to be stored and published in a RDF triple store. The most promising solution seems to be \xne{Virtuoso}, a integrated feature-rich hybrid data store, able to deal with different types of data (``Universal Data Store''). \cite{Haslhofer2011europeana} 
+The transformation of profiles and instances into RDF/XML is accomplished by a set of XSL-stylesheets, that are currently being tested on a sample dataset. In the near future, a test with the whole CMD dataset will be performed.
+
+Once the data is available it has to be stored and published in a RDF triple store. The most promising solution seems to be \xne{Virtuoso}, an integrated feature-rich hybrid data store, able to deal with different types of data (``Universal Data Store''). \cite{Haslhofer2011europeana} 
 
 % Although the distributed nature of the data is one of the defining features of LOD and theoretically one should be able to follow the data by dereferencable URIs, in practice it is mostly necessary to pool into one data store linked datasets from different sources that shall be queried together due to performance reasons. This implies that the data to be kept by the data store will be decisively larger, than ``just'' the original dataset.
@@ -491 +424 @@
 
 \section{Conclusions and Future Work}
-In this paper, we proposed an encoding of the whole of the CMD data domain in RDF, with special focus on the core model the general component schema. Additionally, some technical considerations were discussed regarding exposing this dataset as Linked Open Data and the implications for real semantic ontology-based data exploration. 
-In the near future, a test with the whole CMD dataset will be performed.
-And work on mapping values to entities.
-
-With this new enhanced dataset, the groundwork is laid for the full-blown semantic search as proposed in the original goals, i.e. the possibility of exploring the dataset using external semantic resources.
-The user can access the data indirectly by browsing external vocabularies/taxonomies, with which the data will be linked like vocabularies of organizations or taxonomies of resource types.
-
+In this abstract, we sketched the work on encoding of the whole of the CMD data domain in RDF, with special focus on the core model -- the general component schema and the mapping of element values to semantic entities. Additionally, some technical considerations were discussed regarding exposing this dataset as Linked Open Data.
+
+With this new enhanced dataset, the groundwork is laid for a full-blown \emph{semantic search}, i.e. the possibility of exploring the dataset indirectly using external semantic resources (like vocabularies of organizations or taxonomies of resource types) to which the CMD data will then be linked to.
 
 
@@ -503 +432 @@
 \bibliography{CMD2RDF}
 
-\end{document}
+\end{document}s