=Paper=
{{Paper
|id=Vol-1240/wasabi2014-paper4
|storemode=property
|title=Mapping Representation based on Meta-data and SPIN for Localization Workflows
|pdfUrl=https://ceur-ws.org/Vol-1240/wasabi2014-paper4.pdf
|volume=Vol-1240
|dblpUrl=https://dblp.org/rec/conf/esws/MeehanBLO14
}}
==Mapping Representation based on Meta-data and SPIN for Localization Workflows==
https://ceur-ws.org/Vol-1240/wasabi2014-paper4.pdf
Mapping Representation based on Meta-data and SPIN
for Localization Workflows
Alan Meehan, Rob Brennan, Dave Lewis, Declan O’Sullivan
CNGL Centre for Global Intelligent Content, Knowledge and Data Engineering Group, School
of Computer Science and Statistics, Trinity College Dublin, Ireland
{meehanal,rob.brennan,dave.lewis,declan.osullivan}@scss.tcd.ie
Abstract. The localization industry currently deploys language translation
workflows based on heterogeneous tool-chains. Standardized tool interchange
formats such as XLIFF (XML Localization Interchange File Format) have had
some impact on enabling more agile translation workflows. However the rise of
new tools based on machine translation technology and the growing demand for
enterprise linked data applications has created new interoperability challenges
as workflows need to encompass a broader range of tools. In this paper we pre-
sent an approach of representing mappings between RDF-based representations
of multilingual content and meta-data. To represent the mappings, we use a
combination of SPARQL Inferencing Notation (SPIN) and meta-data. Our ap-
proach allows the mapping representation to be published as Linked Data. In
contrast to other frameworks such as R2R, the mappings are executed via a
standard SPARQL processor. The objective is to provide a more agile approach
to translation workflows and greater interoperability between software tools by
leveraging the ongoing innovation in the Multilingual Web field. Our use case
is a Language Technology retraining workflow where publishing mappings
leads to new opportunities for interoperability and end-to-end tool-chain analyt-
ics. We present the results from an initial experiment which compared our ap-
proach of executing and representing mappings to that of a similar approach -
the R2R Framework.
Keywords: Multilingual Web, Semantic Mapping, Interoperability
1 Introduction
The localization industry is historically built on fragile cross-enterprise tool-chains
with strong interoperability requirements. Ongoing research and innovation by the
Multilingual Semantic Web and Linked Data communities has led to promising new
technologies that can simultaneously span the language and interoperability barriers.
However switching to an enterprise Linked Data model is not a straight forward task.
Data needs to be transformed from its original format into a RDF-based representa-
tion and multiple domain or tool-specific vocabularies are often employed within the
RDF. This increases the importance of mapping technology [1] to enable flexible end-
to-end tool-chains. Where such tool-chains are in place, there is considerable com-
mercial advantage to enabling end-to-end analytics that can monitor content flows
through the tools and the impact of mapping steps.
This paper focuses on the problem of making such mapping steps visible within a
localization tool-chain, exposing the mappings in a way that facilities discovery,
lifecycle management and the recording of mapping meta-data such as the mapping
provenance. These mappings must be executable in the sense that it is desirable to
have a framework that takes the mapping representation and can apply it as needed to
instance data. By avoiding proprietary technologies in the execution step it is hoped
that a wider range of tool vendors can be used to lower costs and simplify integration
across multiple enterprises in a localization value chain.
This leads to the following two research questions that are investigated in this pa-
per. How can mappings be expressed as Linked Data to facilitate discovery and the
recording of mapping meta-data? To what extent can standard SPARQL endpoints act
as an execution engine for these mappings?
We represent the executable RDF-to-RDF mappings as SPARQL1 construct que-
ries that can be executed on any standard SPARQL endpoint. To support mapping
publication, discovery and meta-data annotation we represent the mappings as
SPARQL Inference Notation (SPIN) [9] Linked Data. We aim to exploit this capabil-
ity by also publishing associated mapping meta-data that will lead to new techniques
for mapping lifecycle management and SPARQL-based mapping quality analytics.
Although a work in progress, the contribution of this paper is an evaluation of the
relative expressivity of representing executable mappings as SPARQL construct que-
ries compared to the mapping language of the R2R Framework [8] based on a set of
test mappings previously published by the R2R team. In addition the viability of using
SPIN to publish the mappings as Linked Data is evaluated by transforming the test
mapping set into SPIN-based RDF representations.
The remainder of the paper is as follows: Section 2 presents a use case to illustrate
where our approach of mapping representation would be useful; Section 3 presents
the requirements of the mapping representation; Section 4 covers related work in the
area of semantic mapping and the publication of mappings; Section 5 presents the
evaluation of our approach of representing and executing mappings against the R2R
Framework; we finish with conclusions and future work in Section 6.
2 Language Technology Retraining Workflow Use Case
This section describes a use case centered on the localization industry’s process of
providing translated content. This was chosen as an exemplar of complex real world
workflows that the authors were familiar with. We focus on a Language Technology
(LT) retraining workflow2, with the goal to provide a means for translated content to
be retrieved and used to retrain multiple machine translation tools.
1 http://www.w3.org/TR/sparql11-query/
2 Currently an ongoing development at CNGL Centre for Global Intelligent Content:
http://www.cngl.ie
Figure 1 illustrates the process whereby a piece of HTML source content under-
goes a series of processing steps, acted on by specific tools for translation, quality
assessment and post editing. An XML Localization Interchange File Format (XLIFF)3
file is used to record the processing that the content has undergone at each step. At the
end of the content flow, a custom tool, using the Extensible Stylesheet Language
Transformation (XSLT)4 language, is used to map the data from the XLIFF file into
RDF using the Global Intelligent Content semantic model (GLOBIC) 5 vocabulary and
stored in a triple store. This RDF data represents details such as the source and target
of text content that underwent a Machine Translation (MT) process, which tool car-
ried out the MT process, post edits and quality estimates associated with translated
content. By building up data in the triple store, it becomes a rich source of MT train-
ing data. A high quality training data-set is important for MT applications in order to
gain benefits during training phases.
Figure 1. Language Technology Retraining Workflow
The retraining aspect of the workflow involves retrieving suitable content to be re-
fed into the MT statistical machine learning tool. This is achieved by querying the
triple store for translated content with a quality estimate over a certain threshold val-
ue, which is easily achieved using SPARQL queries.
Heterogeneous tools looking to utilize this training data naturally need to have the
data in the triple store mapped to a schema they recognize. In Figure 1, the MT tool is
GLOBIC unaware, it is designed to use the content represented by the Internationali-
zation Tag Set6 (ITS) vocabulary. Thus the Quality Estimate (QE) and Post Edited
(PE) data that is represented in GLOBIC must be mapped to an ITS representation for
3 http://docs.oasis-open.org/xliff/xliff-core/xliff-core.html
4 http://www.w3.org/TR/xslt
5 The GLOBIC semantic model is an ongoing development at CNGL Centre for Global Intel-
ligent Content. Its purpose in this use case is to enable greater interoperability and analytics
within the workflow: http://www.scss.tcd.ie/~meehanal/gic.ttl
6 http://www.w3.org/TR/its20/
the MT tool to use it. As will be seen in Section 3, our approach to representing such
mappings allows them to be published alongside the other data in the triple store. This
allows the mappings to be discovered by users/tools through SPARQL queries and
executed by the SPARQL processor itself when transformed back to SPARQL syntax
from SPIN. Transforming from SPARQL syntax to SPIN or vice-versa can be can be
done using the SPIN RDF Converter7, which can be used as a free web service.
3 Mapping Representation Requirements and Design
This section describes the requirements for the mapping representation and an exam-
ple of how a mapping is represented under our approach.
The requirements of the mapping representation are as follows:
1. A mapping entity must be expressed as RDF, with a unique URI, allowing the
mapping to be publishable on the web and discoverable via SPARQL queries.
2. The executable mapping statement must be a SPARQL query that is executable by
a SPARQL processor.
3. The executable mapping statement must be expressed as RDF and must have a
unique URI, allowing the statement to be queried by SPARQL and linked to a
mapping entity.
4. A mapping entity is to be modeled with associated meta-data expressed as RDF,
providing additional data on the mapping which can be queried via SPARQL.
To fulfil requirement 1, a mapping entity should be given a meaningful, unique
name and modeled as an instance of the Mapping class from the GLOBIC vocabulary.
For requirement 2, the executable mapping statement should be devised as a
SPARQL construct query. For requirement 3, the SPARQL construct query should be
converted to SPIN in order to be expressed as RDF and given a unique, meaningful
name. The hasRepresentation property from the GLOBIC vocabulary should be used
to link the SPIN representation of the SPARQL construct query to the mapping entity.
For requirement 4, the associated meta-data for a mapping entity should be modeled
using the following properties. The wasCreatedBy, the mapDescription and the ver-
sion properties from the GLOBIC vocabulary and the generatedAtTime and wasRevi-
sionOf properties from the W3C PROV 8 vocabulary are used.
Below is an example of a mapping representation that concerns the mapping of a
MT quality score from the GLOBIC vocabulary to the ITS vocabulary. The SPIN
representation of the SPARQL construct query is as follows:
01: @PREFIX gic: .
02: @PREFIX itsrdf: .
03: @PREFIX sp: .
04: @PREFIX ex: .
7 http://spinservices.org/spinrdfconverter.html
8 The PROV ontology contains classes and properties that can be used to model provenance
data about an entity, agent or activity: http://www.w3.org/TR/prov-o/
05: ex:globic_to_its_mtScore_sp_2 a sp:Construct;
06: sp:templates ([ sp:object _:b1;
07: sp:predicate itsrdf:mtConfidence;
08: sp:subject _:b2 ]);
09: sp:where ([ sp:object _:b1;
10: sp:predicate gic:qualityAssessment;
11: sp:subject _:b2 ]).
12: _:b2 sp:varName "s"^^xsd:string.
13: _:b1 sp:varName "val"^^xsd:string.
The mapping entity plus associated meta-data is as follows:
14: ex:globic_to_its_mtScore_map_2 a gic:Mapping;
15: gic:hasRepresentation ex:globic_to_its_mtScore_sp_2;
16: gic:wasCreatedBy ex:person_1;
17: prov:generatedAtTime “2014-01-01”^^xsd:date;
18: gic:mapDescription “Used to map X to Y etc…”;
19: gic:version “1.1”^^xsd:float;
20: prov:wasRevisionOf ex:globic_to_its_mtScore_map_1.
Examining the example above, line 14 contains the name of the mapping entity.
Line 15 links the mapping to the SPIN representation of the SPARQL construct query
on line 05. Line 16 indicates what person/application is responsible for creating the
mapping. Line 17 indicates when the mapping was created. Line 18 provides a human
readable description of what the mapping does. Line 19 indicates the current version
of the mapping. Line 20 provides a link to the previous version of the mapping.
4 Related Work
There is a rich body of research in semantic mapping undertaken by the semantic web
community [1]. A wide variety of approaches have been adopted to tackle the map-
ping challenge, from rule-based representations [2], to axiomatic representations [3],
to SPARQL query representations [4-5].
Keeney et al. [6] evaluated these three mapping approaches and found that the
SPARQL query approach in general excels in terms of execution time and efficient
use of computational resources. Although there are some particular circumstances
where there are downsides to this approach. Keeney et al. conclude that for tasks
where applications wish to map and use relatively small, specific data, the SPARQL
approach would be ideal.
Little research has been undertaken into publishing mappings in order for them to
be discovered and re-used. Thomas et al. [7] propose that the lack of ontology and
mapping meta-data impedes the task of discovering relevant mappings between on-
tologies. They propose a thirty-three element mapping meta-data ontology, OM2R,
based on the mapping lifecycle. We plan to build on this work to extend the scope of
the meta-data collected on mappings, however in our approach the W3C PROV vo-
cabulary will be used as the basis of lifecycle fields such as creation date.
A notable approach to publishing mappings however is the R2R Framework [8],
which is a framework for executing mappings between RDF ontologies. The R2R
framework has its own language called the R2R mapping language9 for publishing
mappings on the web. Similar to our approach of representing mappings, the R2R
mappings are instances of a Mapping class, from the R2R mapping language, not the
GLOBIC vocabulary. The mappings are modeled with meta-data and use the proper-
ties sourcePattern and targetPattern to represent the triple patterns which are execut-
ed by the R2R Framework. Our approach differs in that mapping instances are linked
to a SPIN representation of a SPARQL construct query, which is ultimately executed
by a SPARQL processor.
The SPARQL Inference Notation (SPIN) is a set of vocabularies that are used to
represent business rules and constraints via SPARQL queries [9]. Tools that imple-
ment SPIN, such as TopBraid Composer 10 have been used in a wide range of applica-
tions [10-13]. Such tools also allow custom functions (which may not appear in the
SPARQL specification) to be declared and executed, which make SPIN tools versatile
at establishing data constraints and even data mapping [14].
5 Evaluation
This section describes two initial experiments of a series of planned experiments; the
two here were carried out in order to evaluate our approach of representing mappings.
The first experiment compared SPARQL’s mapping capabilities with that of the R2R
Framework. The second experiment involved testing the expressiveness of SPIN with
regard to expressing SPARQL construct queries as RDF.
5.1 Experiment 1: Comparing SPARQL’s mapping capabilities to the R2R
Framework
Hypothesis: It is possible to represent all of the 7011 R2R Framework test mappings
as SPARQL construct queries and the execution of these SPARQL construct queries
will produce identical results as the R2R Framework mapping results.
Method: First, a data-set12 was collected. The creators of the R2R Framework de-
vised 72 test mappings13 between DBpedia and 11 other data-sources to test their
framework. We collected instance data, related by the test mappings, via SPARQL
endpoints and data dump files. Then the test mappings were executed against the
data-set using the R2R Framework. This resulted in 70 output files consisting of new-
9 http://wifo5-03.informatik.uni-mannheim.de/bizer/r2r/spec/
10 http://www.topquadrant.com/tools/IDE-topbraid-composer-maestro-edition/
11 Note that only 70 of the 72 test mappings were carried out as data from BookMashup could
not be obtained
12 Data from this experiment can be found at: http://www.scss.tcd.ie/~meehanal/Experiment1/
13 http://wifo5-03.informatik.uni-mannheim.de/bizer/r2r/examples/DBpediaToX.ttl
ly inferred triples. Next the data-set was loaded into an Apache Jena triple-store with
Fuseki SPARQL server14. The 70 test mappings were represented as SPARQL con-
struct queries and executed against the data in the triple-store. This resulted in 70
output files consisting of newly inferred triples. Lastly, the output files from the R2R
Framework were compared with the respective output files derived from the SPARQL
construct queries.
Results: It was found that the SPARQL construct queries created identical outputs as
the R2R Framework for all of the 70 test mappings, which indicates that the SPARQL
construct queries were accurate representations of the R2R Framework test mappings.
Discussion: The results are promising in showing that SPARQL is as capable as the
R2R Framework for executing mappings on RDF data sets. The SPARQL 1.1 specifi-
cation standardized a number of functions, such as string manipulation functions,
which allow for more complex mappings to be carried out. Prior to SPARQL 1.1, the
R2R Framework test mapping that involved string manipulation would not be possi-
ble using the SPARQL 1.0 specification, allowing the R2R Framework to represent
more complex mappings than SPARQL.
5.2 Experiment 2: Testing SPIN’s Expressivity
Hypothesis: It is possible to express all 70 of the SPARQL construct queries (from
Experiment 1) as RDF via SPIN.
Method: This test used the SPIN RDF Converter and TopBraid Composer 4.4.0 (free
edition) to transform the 70 SPARQL construct queries to SPIN syntax. An error is
produced by the converter and composer if it cannot represent a SPARQL query.
Results: It was found that SPIN could represent all 70 of the SPARQL construct que-
ries. The construct queries were categorized according to Scharffe’s correspondences
patterns [15]. All 70 fell into 3 patterns: Equivalent Class, Equivalent Relation and
Property Value Transformation as illustrated in Figure 2 (some queries span across
two patterns).
Figure 2. R2R Test Mappings broken down by Correspondence Pattern Type
14 http://jena.apache.org/documentation/serving_data/
Discussion: Initially it was found that the SPIN RDF Converter could only represent
64 of the 70 SPARQL construct queries. Specifically the SPARQL 1.1 standardized
functions REPLACE, STRBEFORE and STRAFTER could not be represented. The
creators of SPIN were contacted and the SPIN RDF Converter is using an outdated
version of SPIN and will be updated in the near future. However, TopBraid Composer
4.4.0 uses the latest version of SPIN and this was used to represent the 6 SPARQL
construct queries that the SPIN RDF Converter could not.
6 Conclusions and Future Work
In this paper we have proposed a semantic mapping representation, between RDF data
sources, that allows the mapping to be published, discovered and executed. The goal
of the mapping representation is to provide an approach towards greater interoperabil-
ity between heterogeneous tools operating within a localization tool-chain.
We represent the executable mapping statement as a SPARQL construct query
which is expressed as RDF via SPIN. Mappings are modelled with meta-data, also
expressed as RDF using the GLOBIC and W3C PROV vocabularies. All aspects of a
mapping are published in a triple store alongside other data, where they can be dis-
covered, queried and ultimately executed by a SPARQL processor.
We have shown that SPARQL construct queries are just as expressive as the R2R
Mapping Language for representing a wide variety of mappings and that these queries
can be represent in RDF via the SPIN syntax.
Future work will investigate a model of SPARQL-based mapping quality analytics
and lifecycle management where all aspects of a mapping (meta-data and SPIN repre-
sentation) can be queried and even updated/deleted using SPARQL queries.
Acknowledgements. This research is supported by the Science Foundation Ireland
(Grant 12/CE/I2267) as part of CNGL Centre for Global Intelligent Content
(www.cngl.ie) at Trinity College Dublin.
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