=Paper=
{{Paper
|id=Vol-1262/paper10
|storemode=property
|title=Mapping, Enriching and Interlinking Data from Heterogeneous Distributed Sources
|pdfUrl=https://ceur-ws.org/Vol-1262/paper10.pdf
|volume=Vol-1262
|dblpUrl=https://dblp.org/rec/conf/semweb/Dimou14
}}
==Mapping, Enriching and Interlinking Data from Heterogeneous Distributed Sources==
https://ceur-ws.org/Vol-1262/paper10.pdf
Mapping, enriching and interlinking
data from heterogeneous distributed sources
Anastasia Dimou
supervised by Rik Van de Walle, Erik Mannens, and Ruben Verborgh
Ghent University iMinds Multimedia Lab
Gaston Crommenlaan 8 bus 201, 9050 Ghent, Belgium
anastasia.dimou@ugent.be
Abstract. As Linked Open Data is gaining traction, publishers incor-
porate more their data to the cloud. Since the whole Web of Data cannot
be semantically represented though, data consumers should also be able
to map any content to rdf on-demand to answer complicated queries by
integrating information from multiple heterogeneous sources distributed
over the Web or not. In both cases, the quality and integrity of the gener-
ated rdf output affects the performance of traversing and querying the
Linked Open Data. Thus, well-considered and automated approaches to
semantically represent and interlink, already during mapping, the do-
main level information of distributed heterogeneous sources is required.
In this paper, we outline a plan to tackle this problem: We propose a uni-
form way of defining how to map and interlink data from heterogeneous
sources, alternative approaches to perform the mappings and methods
to assess the quality and integrity of the resulting Linked Data sets.
1 Problem Statement
Efficiently extracting and integrating information from diverse, distributed and
heterogeneous sources to enable rich knowledge generation that can more ac-
curately answer complicated queries and lead to effective decision making, re-
mains one of the most significant challenges. Nowadays, Semantic Web enabled
technologies become more mature and the rdf data model is gaining traction
as a prominent solution for knowledge representation. However, only a limited
amount of data is available as Linked Data, because, despite the significant num-
ber of existing tools, acquiring its rdf representation remains complicated.
Deploying the five stars of the Linked Open Data schema1 is still the de-facto
way of incorporating data to the Linked Open Data (lod) cloud. Approaching
though the stars as a set of consecutive steps and applying them to separately
individual sources, disregarding possible prior definitions and links to other en-
tities, leads in failing to reach the uppermost goal of publishing interlinked data.
Manual alignment to their prior appearances is often performed by redefining
their semantic representations, while links to other entities are defined after the
1
http://5stardata.info/
2 Anastasia Dimou
data is mapped and published. Identifying, interlinking or replicating, and keep-
ing them aligned is complicated and the situation aggravates the more data is
mapped and published. Existing solutions tend to generate multiple Unique Re-
source Identifiers (uris) for the same entities while duplicates can be found even
within a publisher’s own datasets. Hence, demand emerges for a well-considered
policy regarding mapping and interlinking of data in the context of a certain
knowledge domain, either to incorporate the semantically enriched data to the
lod or to answer a query on-the-fly.
So far, there is neither uniform mapping formalisation to define how to map
and interlink heterogeneous distributed sources into rdf in an integrated and
interoperable fashion nor complete solution that supports the whole mapping
and interlinking procedure together. Apart from few domain specific tools, none
of the existing solution offer the option to automatically detect the described
domain and propose corresponding mapping rules. Except for the field of plain
text analysis where again the main focus is on semantically annotating the text
rather than describing a domain and the relationships between its entities. More-
over, there are are no means to validate and check the consistency, quality and
integrity of the generated output, apart from manual user-driven controls, and no
means to automate these tests and incorporate them in the mapping procedure.
2 Relevancy
The problem is directly relevant to data publishing and data consumption with
an emphasis on semantically-enabled data integration. In the data publishing end
of spectrum, domain level information can be integrated from a combination of
heterogeneous sources and published as Linked Data, using the rdf data model.
In the data consumption end of spectrum, the relevancy is two-fold: (i) On the
one hand, the quality and integrity of the resulting rdf representation is reflected
at the dataset’ s consumption. (ii) On the other hand, data extracts can be
mapped and interlinked on-demand and on-the-fly from different heterogeneous
sources, since not all data can be represented as Linked Data. On the whole,
the problem is relevant to the alignment and synchronisation of data’s semantic
and non-semantic representations; modifications (inserts, updates and deletions)
need to be synchronised over data’s semantic and non-semantic representation.
The problem is emphasized in cases of knowledge acquisition, searching or
query answering that information integration is required from a combination of
distributed and heterogeneous (semantic and/or non-semantic) data sources. Es-
pecially when it is taken into consideration data that cannot be easily traversed
else, for instance the deep Web or large volumes of published data files. Seman-
tic Web technologies together with the rdf data model allows to deliberately
concatenate the extract of data that is relevant.
There are several stakeholders that could take advantage of such information
integration enhanced with semantic annotation. Such key stakeholders are those
who publish and consume large volumes of data that might be distributed and
appear in heterogeneous formats. For instance, governments that publish and
Mapping data from heterogeneous distributed sources 3
consume, at the same time, Open Data, scientists that combine data from differ-
ent sources and re-publish processed information or (data) journalists that need
extracts of data from several sources to acquire knowledge and draw conclusions.
3 Related Work
Several solutions exist to execute mappings from different file structures and seri-
alisations to rdf. Different mapping languages beyond rrml were defined [6] in
the case of relational databases and several implementations already exist2 . Sim-
ilarly, mapping languages were defined to support conversion from data in csv
and spreadsheets to the rdf data model. For instance, the XLWrap’s mapping
language [10] that converts data in various spreadsheets to rdf, the declarative
owl-centric mapping language Mapping Master’s M2 [11] that converts data
from spreadsheets into the Web Ontology Language (owl), Tarql3 that follows
a querying approach and Vertere4 that follows a triple-oriented approach as
rrml does too. The main drawback in the case of most row-oriented mapping
solutions is the assumption that each row describes an entity (entity-per-row
assumption) and each column represents a property.
A larger variety of solutions exist to map data from xml to rdf, but to
the best of our knowledge, no specific languages were defined for this, apart
from the wc standardized grddl5 that essentially provides the links to the
algorithms (typically represented in xslt) that maps the data to rdf. Instead,
tools mostly rely on existing xml solutions, such as xslt (e.g., Krextor [9] and
AstroGrid-D6 ), xpath (e.g., Tripliser7 ), and xquery (e.g., XSPARQL [1]).
In general, most of the existing tools deploy mappings from a certain source
format to rdf (per-source approaches) and only few tools provide mappings
from different source formats to rdf. Datalift [12], The DataTank8 , Karma9 ,
OpenRefine10 , RDFizers11 and Virtuoso Sponger12 are the most well-known. But
those tools actually either employ separate source-centric approaches for each of
the formats they support, for instance Datalift, or rely on converting data from
other formats to a master which in most cases is table-structured, for instance
Karma or Open Refine. Furthermore, none of them provides an approach where
the mapping definitions can be detached from the implementation.
Beyond pure execution of mappings to rdf, most of the existing tools do
not provide any recommendations regarding how the data should be mapped,
2
http://www.w3.org/2001/sw/rdb2rdf/wiki/Implementations
3
https://github.com/cygri/tarql
4
https://github.com/knudmoeller/Vertere-RDF
5
http://www.w3.org/TR/grddl/
6
http://www.gac-grid.de/project-products/Software/XML2RDF.html
7
http://daverog.github.io/tripliser/
8
http://thedatatank.com
9
http://www.isi.edu/integration/karma/
10
http://openrefine.org/
11
http://simile.mit.edu/wiki/RDFizers
12
http://virtuoso.openlinksw.com/dataspace/doc/dav/wiki/Main/VirtSponger
4 Anastasia Dimou
namely how to model the domain described. Only Karma offers mapping recom-
mendation, however it relies on a training algorithm that improves when several
domain-relevant data sources are mapped. Among the other tools, only Open
Refine supports recommendations to a certain extend, but its recommendations
have the form of disambiguating named entities appearing in the lod.
As described, existing tools are solely focused on mapping data to the rdf
model, rather than interlinking the entities of the source to existing entities ap-
pearing on the Web. Only Open Refine allows to reconcile and match entities to
resources published as Linked Data and Datalift which incorporates interlink-
ing functionality but only as a subsequent step executed after the mapping is
completed. Overall, till nowadays, mapping and interlinking are considered two
steps that are executed consecutively. A lot of work has been done in the field of
text analysis, natural language processing (nlp) and named entity recognition
(ner) to identify and disambiguate entities with resources appearing in the lod
cloud. However such techniques are mainly focused on semantically annotating
the text rather than modelling the domain described. Moreover these techniques
are not applied in the case of (semi-)structured mappings.
Last but not least, none of the existing tools offer a complete solution that
allows to refine the executed mappings based on the users’ feedback, the results of
data cleansing tools, reasoning over the ontologies used or studying the integrity
and connectedness of the resulting dataset considering it as a graph. A summary
of existing approaches for assessing data quality that could be incorporated
for refining the mappings according to the result of a mapping can be found
at [13]. Among the pioneer tools for rdf data cleansing are the user-driven
TripleCheckMate [8] and the test-driven RDFUnit [7]. Again only Karma is
capable of refining its proposed mapping according to users’ intervention.
4 Research Questions
The main question in my doctoral research is:
– How can we access and represent domain level information from distributed het-
erogeneous sources in an integrated and uniform way?
On the one hand, the accessing aspect needs to be investigated:
– How can we enable querying distributed heterogeneous sources on the Web in a
uniform way?
On the other hand, the representation aspect needs to be investigated:
– How can we identify if entities of a source have already been assigned a URI and
enrich this unique representation with new properties and links?
– How can we interlink newly generated resources with existing ones already during
mapping considering the available domain information we have?
And the overall result raises the following questions:
– How can we assure that if we map some sources the domain is accurately modelled?
– How well the entities of the dataset are linked with each other?
– How well the dataset is linked with the lod cloud?
Mapping data from heterogeneous distributed sources 5
5 Hypotheses
The main hypotheses related to my research are:
– Integrated mapping and interlinking of data in heterogeneous sources gener-
ates fewer overlapping entities and models better the domain’s semantics.
– Reusing Unique Resource Identifiers (uris) leads to more robust and uniform
datasets that have higher integrity and connectedness.
– Interlinking such datasets raises the integrity and connectedness of the whole
lod and improves the performance of its consumption.
– Not all media can be published as Linked Open Data, thus mapping extracts
of multiple heterogeneous data to rdf might occur on demand.
6 Approach
At this PhD, we propose a generic mapping methodology, that maps the data
independently of the source structure (source-agnostic), puts the focus on map-
pings and their optimal reuse and considers interlinking already during map-
pings. Therefore, the initial learning costs remain limited, the potential for the
custom-defined mapping’s reuse augments and a richer and more meaningful
interlinking is achieved. This is a prominent advancement compared to the ap-
proaches followed so far. As a result, the per-source mapping model followed
so far gets surpassed, leading to contingent data integration and interlinking.
Beyond the language that facilitates the mapping rules’ definition and is the
core of our solution, we propose a complete approach that aims to facilitate and
improve the mappings definition and execution.
In our proposed approach we aim to maximize the reuse of existing unique
identifiers (uris) and rely on the links between them and the newly generated
entities to achieve the interlinking of the new dataset with the lod. The disam-
biguated entities are assigned the corresponding uris and their representation
is enriched with properties and relationships of the newly incorporated dataset.
In contrast to the approaches followed so far, custom-generated uris are only
assigned to the entities that were not identified in the lod cloud (not disam-
biguated). Based on the relationships between the newly generated entities and
the disambiguated ones, the interlinking of the newly generated resources with
the lod is achieved. In order to identify such entities, we propose applying ner
techniques to the sources and use them against datasets of the lod.
Besides increasing the integrity of the dataset and reinforcing its interlinking
with the lod cloud, the whole domain needs to be modelled. Recommendations
based on vocabularies used for the description and for the relationships of the
disambiguated entities or other entities that are identified to model the same
domain and those appearing in a vocabularies’ repository, such as lov13 , can be
taken into consideration. The domain can be further refined after the execution of
the mappings and the assessment of the output dataset using tools for evaluating
13
http://lov.okfn.org
6 Anastasia Dimou
the data quality or taking into considerations the users’ feedback. In these cases,
the mapping rules can be adjusted to incorporate the emerging rules.
7 Preliminary results
We already defined a generic language adequate for defining rules to map het-
erogeneous sources into rdf in a uniform and integrated way [3]. This language
is the rdf Mapping Language (rml) 14 , defined as a superset of the wc stan-
dardized mapping language rrml. rml broadens rrml’s scope and extends
its applicability to any data structure and format. rml came up as a result of
our need to map heterogeneous data to rdf. Initially, rrml was extended to
map data from hierarchically structured sources e.g., xml or json, to rdf. De-
tails about how we extended the row-oriented rrml to deal with hierarchy, and
other structures in general, are described in detail at our previous work [5].
Even though the language’s extensibility is self-evident as rml relies on an ex-
tension over rrml, its scalability was also proven by further extending it to map
data published as html pages to the rdf data model. Results of the mappings
from html to rdf using rml were presented at the Semantic Web publishing
challenge of the 11th Extended Semantic Web Conference (ESWC14) [2]. At the
moment, in total, rml and the prototype processor support, but are not limited,
mappings from data in csv, xml, json and html to the rdf data model.
A prototype processor15 was designed and implemented as a proof-of-concept
to accompany the rml mapping language. As rml extends rrml, the proces-
sor is implemented using an existing open-source rrml processor16 . The rml
processor was designed to have a modular architecture where the extraction and
mapping modules are independently executed and the extraction module can
be instantiated depending on the possible inputs. Short discussion regarding
alternative approaches for processors supporting rml were discussed at [5].
Finally, some preliminary work on mapping rules’ refinements by incorporat-
ing data consumers’ feedback was presented at [4]. We showed how provenance
generated during mapping can be used later on to identify the mapping rules
that should be adjusted to incorporate data consumers’ feedback.
8 Evaluation plan
There are different aspects of the proposed solution which need to be assessed
and we are aiming to evaluate: the RML mapping language itself, the semantic
annotations and the entities interlinking, the quality and integrity of the resulting
dataset and the performance of the mapping execution.
– the language’s potential in regard to (i) the range of input sources supported
and their possible combinations for providing integrated mappings, namely
14
http://rml.io
15
https://github.com/mmlab/RMLProcessor
16
https://github.com/antidot/db2triples
Mapping data from heterogeneous distributed sources 7
the language’s scalability and extensibility; (ii) the language’s expressivity,
namely the coverage of possible alternative mapping rules, mainly in com-
parison to other languages (or approaches) and (iii) last, how reusable and
interoperable the mapping descriptions are.
– the validity, consistency and relevance (especially when the domain is mod-
elled according to automated recommendations) of the vocabularies used by
the mapping rules to describe the domain knowledge.
– the quality of the output. To achieve this, both automated solutions assess-
ing data quality and domain experts will be used to evaluate the resulting
dataset in regard to the identified or generated entities, the provided seman-
tic annotations, the interlinking and the overall modelling of the domain.
– the accuracy and the precision and recall of the retrieved, identified and en-
riched entities in conjunction with the confidence for the interlinked entities.
– the integrity of the resulting dataset and the overall analysis of the output’s
datasets in respect to its graph-based representation, for instance in and out
degree, its connectivity, its density, bridges, paths etc.
– the impact of the resulting dataset’s structure and interlinking in respect to
its subsequent consumption. To be more precise, how traversing and querying
the dataset is affected by the choices taken while modelling the knowledge
domain. In the case of querying, we aim to examine both the complexity of
the queries definition and the time and overload to execute them.
– finally, while the performance is important to verify that the mappings can
be executed in reasonable time, the performance of an rml processor is not
the main focus of this work. However, the two fundamental ways of executing
the mappings (mapping-driven or data-driven) will be evaluated and com-
pared to identify best use-cases. The execution planning of the mapping rules
though is more interesting and will be deeper investigated and evaluated.
9 Reflections
The main difference of our approach compared to existing works on mapping
data is that we (i) introduce the idea of a uniform way of dealing with the
mapping of heterogeneous sources and (ii) introduce the aspect of interlinking
while we perform the mapping of data to the rdf data model. We approach the
mapping from a domain modelling perspective where the data is either incor-
porated to a partially described domain or is mapped combined, forming their
own domain. This way, we achieve generating datasets with higher integrity that
are already interlinked among each other and with the lod and thus we reduce
the effort for subsequent interlinking of resources and offer better conditions for
their subsequent consumption.
Acknowledgement
The research described in this paper is funded by Ghent University, the Flemish
Department of Economy, Science and Innovation (EWI), the Institute for the
8 Anastasia Dimou
Promotion of Innovation by Science and Technology in Flanders (IWT), the Fund
for Scientific Research-Flanders (FWO-Flanders), and the European Union.
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