=Paper=
{{Paper
|id=Vol-1550/article-08
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1550/article-08.pdf
|volume=Vol-1550
|dblpUrl=https://dblp.org/rec/conf/semweb/DoTWKAT14
}}
==None==
https://ceur-ws.org/Vol-1550/article-08.pdf
Multiscale Exploration of Spatial Statistical
Datasets: A Linked Data Mashup Approach
Ba-Lam Do, Tuan-Dat Trinh, Peter Wetz, Elmar Kiesling,
Amin Anjomshoaa, and A Min Tjoa
Vienna University of Technology, Vienna, Austria
{ba.do,tuan.trinh,peter.wetz,elmar.kiesling,
amin.anjomshoaa,a.tjoa}@tuwien.ac.at
Abstract. Many national and international organizations today lever-
age semantic web technologies to make statistical datasets available as
Linked Open Data (LOD). A key advantages of this approach is that
the data not only becomes publicly available, but also machine-readable
and hence suitable for automated discovery and exploration. Whereas
this has great potential to support interesting use cases, it remains dif-
ficult for end users today to utilize and combine these statistical Linked
Data. Three challenges are: (i) directing users to relevant data sources
based on a specified location; (ii) facilitating data integration despite a
lack of outgoing links between datasets; and (iii) offering flexible means
to integrate and aggregate data from various sources. As time and loca-
tion are highly relevant dimensions in most statistical data, we address
the identified challenges by first constructing geographical metadata for
statistical sources. Following a mashup approach, we introduce mecha-
nisms to recommend interesting datasets to end users and automatically
enable data integration, visualization, and comparisons based on user-
defined criteria.
1 Introduction
In recent years, statistical data communities have increasingly adopted seman-
tic web technologies in order to formalize and link their published datasets. In
particular, many organizations make use of the W3C RDF Data Cube vocabu-
lary [2] to publish their data as LOD. The data hence becomes accessible in a
standardized format. However, means for end users that allow them to utilize
and combine statistical LOD datasets on the web are lacking, which may be
attributed to the following reasons:
(i) There are a large and growing number of statistical sources covering a
wide range of domains. Those are valuable assets, but without an appropriate
catalogue, users are unable to discover relevant statistical information.
(ii) Each statistical LOD dataset typically uses a distinct terminology and
does not consistently include outgoing links to other sources. Consolidating
equivalent entities stored in different sources therefore poses a significant chal-
lenge. For example, Ireland’s 2011 census data1 includes thousands of geograph-
ical entities (county, province, town, city, etc.), none of which are linked to any
external resources. Other LOD sources refer to Irish cities with different URIs.
Collecting all statistical information on a city available as LOD is therefore dif-
ficult.
(iii) Current statistical data visualization applications typically focus on dis-
playing a given dataset of a particular granularity (e.g. country level, city level,
etc.). They do not, however, provide mechanisms for locating relevant datasets
suitable for meaningful comparisons.
This paper addresses these issues, focusing on the geographical dimension
which is highly relevant in most statistical datasets. In particular, we use geo-
graphical points as input for the data source discovery process. Based on a given
location, we can find appropriate datasets that provide statistical information.
For instance, a user may locate his/her home on a map and trigger a retrieval
process that yields datasets that contain data about the specified location. Using
the supplied location, we determine the geographical entities that the location is
situated in at different levels and allow users to compare data on the respective
areas to appropriate similar areas, e.g., compare the population of their coun-
try to that of others, or compare income levels in their district to that in other
districts in the same city.
To deal with the first issue of discovering relevant statistical datasets, we
analyze available SPARQL endpoints to construct geographical metadata, which
connects each dataset to the related locations. We use Google Geocoding API2
to collect information of a geographical area based on their name.
The second issue, i.e., lacking support for data integration due to missing or
inconsistent links, is addressed by using our constructed data catalogue. This
catalogue uses a consistent unique identifier for the same location, which can
refer to different identifiers in disparate datasets. This approach is possible even
in cases where the original location entities do not reference common external
entities.
To allow users to automatically integrate data from different statistical sources
and overcome the third issue, we make use of the Linked Widget platform [7] –
a flexible, interactive mashup platform. This platform implements semantic web
principles and allows users to model widgets and data flows. It also provides
mechanisms for semantic widget discovery, terminal matching, and automatic
widget composition. In this paper, we describe the design of a collection of wid-
gets targeted towards three use cases dealing with statistical data.
The remainder of this paper is organized as follows. In Section 2, we discuss
the development of a geographical metadata catalogue. Section 3 illustrates our
mashup approach with a collection of widgets and three practical use cases.
Section 4 provides pointers to related work and we conclude in Section 5 with
an outlook on future research.
1
http://data.cso.ie/datasets/index.html
2
https://developers.google.com/maps/documentation/geocoding/
2 Geographical Metadata for Statistical Data
Countries are typically subdivided into hierarchical administrative units, e.g.,
regions, provinces, cities, districts, communes. Current statistical LOD sources
usually provide information describing specific characteristics of these adminis-
trative units in chronological order. Time and location are therefore typically
the most important dimensions for this kind of statistical data.
We therefore develop a catalogue for spatial statistical data. If users provide
an address, e.g., Donaufelder Strasse 54, Austria, or simply define a point on
a map, we detect corresponding administrative areas and use those to select
relevant datasets. We then provide suggestions to compare these areas with
other areas at the same administrative level. For example, users can compare
the population of their district to that of other districts in their city, or contrast
the income of their city to other cities in the country; on a higher level, they
can compare the happiness index of their country to that of other countries in
the world.
We use SPARQL endpoints as input of the catalogue creation process and
perform four steps: (i) identify all datasets of the source; (ii) identify all dimen-
sions and measures for each dataset; (iii) from the set of detected dimensions,
determine the location dimension, (iv) for each text value of the dimension lo-
cation, identify its administrative area and create a unique resource in the cat-
alogue. We follow the results returned by the Google Geocoding API to deal
with different geographical hierarchies of countries, ensuring consistent entity
classification.
The metadata structure of a dataset is illustrated in Listing 1. A definitive
mapping between the administrative area and the resource URI in the metadata
catalogue must exist so that same locations from different sources have the same
unique URI in the metadata catalogue. This facilitates integration and aggrega-
tion. We define the mapping rule and provide a URI mapping service to convert
an administrative area to the corresponding URI in the catalogue.
PREFIX qb : < http :// purl . org / linked - data / cube # >
PREFIX lc : < http :// location - based - catalogue . ifs . tuwien . ac . at / >
[] a lc : SPARQL Endpoint ;
lc : hasDataSet [ lc : describes [ a lc : A d m i n i s t r a t i v e A r e a ];
qb : dimension [ a qb : D i m e n s i o n P r o p e r t y ];
qb : measure [ a qb : Me a su re Pr o pe rt y ];
qb : attribute [ a qb : A t t r i b u t e P r o p e r t y ]; ]
Listing 1: A SPARQL query for terminal matching
3 Widget and Mashup approach
3.1 Linked Widget Platform
To enable users to flexibly select and combine statistical datasets and synthesize
desired information, we follow a visual programming paradigm implemented in
the Linked Widgets platform – a widget-based mashup platform. Its key ele-
ments are Linked Widgets, an extension of standard web widgets backed by a
semantic model that follows Linked Data principles. This model describes data
input/output and metadata (such as data provenance and licensing terms) that
is useful for widget search and auto composition features.
There are three types of widgets, i.e., data, process, and visualization wid-
gets. The platform provides a graphical interface for creating a data flow and
composing various applications by connecting widgets in different ways. Other
stakeholders can develop widgets independently and contribute widgets to the
platform to extend its functionality. Whereas existing similar platforms are ori-
ented more towards low-level data processing with generic widgets, the platform
is more problem-oriented and supports modeling on a higher, semantic level.
3.2 Statistical Widget Collection
In the platform, widgets are grouped into widget collections. Each collection
addresses a different problem domain. We developed a collection3 for statistical
data exploration based on spatial context. Each widget can have multiple inputs,
but only a single output. The output of a widget can serve as input of another
one, if their semantic models can be matched, i.e., they have a certain level of
overlap. Our exemplary statistical widget collection consists of the following four
widgets:
Spatial Entity Recognizer (data widget): This widget accepts an address
text or a user-defined location point as an input and calls the Google Geocoding
API service to obtain the corresponding spatial entities at different levels, i.e.,
country level, administrative area level one to five, and sublocality level. It then
uses the URI mapping service to return the URIs of these areas from the cata-
logue. Note that these URIs may not be in the catalogue, because the catalogue
does not create instances of all locations in the world; it only contains instances
of locations that exist in the analyzed statistical datasets.
Spatial Data Locator (process widget): This widget returns a list of datasets
related to the input spatial entity. It contains options to filter the output datasets
based on different domains, e.g., census, transportation, or income.
Spatial Comparator (process widget): From the input of two administra-
tive areas, this widget returns a list of datasets that contain information on the
two areas that can be compared.
Google Chart (visualization widget): This widget presents the input of a
single dataset or a list of datasets in appropriate charts.
All datasets returned by the two process widgets follow the W3C RDF Data
Cube vocabulary and represent the data in JSON-LD format.
3
http://linkedwidgets.org/MashupPlatform.html?widgetCollectionId=
SpatialStatisticalCollection
Spatial Spatial Spatial
Entity Data Google Chart (i) (ii) Entity
Recognizer Locator Recognizer
Spatial
Google Chart
Comparator
Spatial
Entity
Recognizer
Fig. 1: Sample mashup use cases.
3.3 Available Mashups
Sample mashups created from the four widgets are shown in Fig. 1: (i) discovery
of statistical information on an area (at different administrative levels) based on
a user’s address;4 (ii) users select different criteria to compare a pair of areas,
which have the same administrative level and the same parent area.5
4 Related Work
In the research field of Geographic Information Systems (GIS) the problem of
spatial dataset discovery and integration has also been addressed. Hariharan et
al. [9] create summaries of GIS datasets which are then used for source dis-
covery. They adapt histogram-based techniques that take textual and spatial
information into account. Jones et al. [10] present an architecture of a spatially-
aware search engine to discover and access geo-datasets on the web. Their so
called SPIRIT Spatial Search Engine uses a geo-ontology and spatial indexing
to deal with query disambiguation, relevance ranking, and metadata extraction.
Li et al. [11] enable intelligent data discovery of geo-referenced data based on
the combination of Latent Semantic Analysis and Two-Tier Ranking. This tech-
nique allows them to build a semantic search engine called Semantic Indexing
and Ranking (SIR), which outperforms existing keyword-matching approaches.
However, these approaches do not rely on Linked Data principles.
In the Linked Data domain, with the growing number of statistical SPARQL
endpoints available, several useful applications have emerged from the semantic
web community. The Linked Data Query Wizard is a web-based tool for display-
ing, accessing, filtering, exploring, and navigating Linked Data [3]. It aims at
providing a tabular interface to cope with users lack of knowledge about Linked
Data and its graph structure. CubeViz is a facetted browser for statistical data
utilizing the RDF Data Cube vocabulary [6]. Based on a dataset CubeViz gen-
erates a facetted browser that can be used to filter observations which in turn
can be analyzed, explored, and visualized. Stats.270a.info provides an interface
to compare statistical data retrieved from different sources on the web [1]. The
application makes use of the RDF Data Cube vocabulary to discover statistical
data in a uniform way. Furthermore, federated SPARQL queries are employed
to gather data from disparate sources.
4
http://linkedwidgets.org?id=MashupSpatialDataLocator
5
http://linkedwidgets.org?id=MashupSpatialDataComparator
Zapilko et al. [8] discuss issues and challenges for using Linked Open Data
for analyzing and enriching statistical data. They reveal problems regarding
data integration, link generation, aggregation level of the data, and complexities
within the data structures. Kämpgen [5] proposes to use expressive semantic
web ontologies to build a conceptual model of statistical Linked Data from dis-
parate sources. However, he also describes challenges that need to be addressed
as for instance discovering datasets, or integrating data of different granularity.
Kalampokis et al. [4] argue that the availability of previously closed governmen-
tal statistical datasets now enables to gain unexpected and unexplored insights
into different domains. They support their claim by describing a linked open
government data analytics vision along with its technical requirements. Finally
they present a use case dealing with UK general election data.
Similar to the related work, we try to provide an easy to use interface for lay
users to discover and explore statistical datasets. However, in contrast to current
implementations, we want to provide the user with greater control to influence
the process of dataset discovery, on the one hand, and data exploration, on the
other hand. This is achieved by employing an approach based on widgets. Users
can apply widgets to control the data processing flow from the beginning, i.e.,
dataset discovery, to the end, i.e., data visualization, allowing for the creation of
many different scenarios. Furthermore, through creating a metadata catalogue,
we offer a flexible means to compare different datasets, which are related via
administrative levels, or statistical criteria they offer.
5 Conclusion and Future Work
This paper addresses two crucial issues in statistical Linked Data: (i) Where
and how can users discover statistical data? (ii) How can data reconciliation,
ontology matching and instance matching be performed with statistical data?
We address these issues by building a data catalogue, describing identified
datasets, and finally fostering data discovery by adopting a mashup-based ap-
proach. Our approach is built around geographical context information that
users provide by specifying a geographical point or an address. In future work,
we will focus on the temporal dimension of the data. For instance, users pro-
vide a certain timespan they are interested in and our system suggests datasets,
which support this timespan. Furthermore, we will support users in discovering
datasets based on keyword search.
The current mashups work well with datasets that have a fairly limited num-
ber of dimensions. For larger datasets, it is difficult to compare different areas in
a single chart. We can standardize locations through available geocoding APIs,
but to standardize dimensions or measures that do not have the same meaning
is still an open challenge. For instance, the year dimension has different URIs in
different statistical data sources. At present, our implementation is applicable
for datasets of the same statistical LOD source only and we need to improve this
in future.
We currently develop a Dataset Recommender widget which accepts a single
spatial entity as input and finds all spatial entities that share the same parent.
Then, based on the criteria chosen by the user, it returns a list of datasets that
contain statistical information that relate to the areas. When connecting its
input with the output of Spatial Entity Recognizer and its output with the input
of Google Chart, we have another mashup enabling users to compare their area
to appropriate, automatically identified, other areas.
Additionally, we plan to automatically create outer links to DBpedia for the
location entities of the metadata catalogue. This would allow other developers
to more easily integrate statistical data with the Web of Data.
Finally, it will be necessary to evaluate precision and recall of the location
detection process during the metadata construction process in a dedicated ex-
periment.
References
1. Capadisli, S., Auer, S. Riedl, R.: Linked Statistical Data Analysis, ISWC SemStats
(2013), http://csarven.ca/linked-statistical-data-analysis
2. Cyganiak, R. et al.: The RDF Data Cube Vocabulary. W3C Recommendation (2014)
3. Hoefler, P. et al.: Linked Data Query Wizard: A Novel Interface for Accessing
SPARQL Endpoints. In Proceedings of the Workshop on Linked Data on the Web,
CEUR-WS (2014)
4. Kalampokis, E. et al.: Linked Open Government Data Analytics. In: Wimmer, M.
et al. (eds.) Electronic Government, pp. 9–110. Springer Berlin Heidelberg (2013)
5. Kämpgen, B.: DC Proposal: Online Analytical Processing of Statistical Linked Data.
In: Aroyo, L. et al. (eds.) The Semantic Web – ISWC 2011, pp. 301–308. Springer
Berlin Heidelberg (2011)
6. Mader, C. et al.: Facilitating the Exploration and Visualization of Linked Data. In:
Auer, S. et al. (eds.) Linked Open Data – Creating Knowledge Out of Interlinked
Data, pp. 90–107. Springer Berlin Heidelberg (2014)
7. Trinh, T.-D. et al.: Open Linked Widgets Mashup Platform. Proceedings of the AI
Mashup Challenge 2014 ESWC Satellite Event, CEUR-WS (2014)
8. Zapilko, B. et al.: Enriching and Analysing Statistics with Linked Open Data.
In NTTS-Conference on New Techniques and Technologies for Statistics, Brüssel.
(2011)
9. Hariharan, R. et al.: Discovering GIS Sources on the Web using Summaries. In
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries, pp.
94–103. ACM (2008)
10. Jones, C. B. et al.: The SPIRIT Spatial Search Engine: Architecture, Ontologies
and Spatial Indexing. In Geographic Information Science, pp. 125–139, Springer
Berlin Heidelberg (2004)
11. Li, W. et al.: Towards Geospatial Semantic Search: Exploiting Latent Semantic
Relations in Geospatial Data. In International Journal of Digital Earth, 7(1), pp.
17–37, Taylor & Francis (2014)