Publish and Enrich Geospatial Data as Linked Open Data

Claire Ponciano

1 a

, Markus Schaffert

, Falk W

urriehausen

and Jean-Jacques Ponciano

1 b

i3mainz – Institute for Spatial Information and Surveying Technology, Mainz University of Applied Sciences,

Lucy-Hillebrand-Straße 2, 55128 Mainz, Germany

Federal Agency for Cartography and Geodesy, Richard-Strauss-Allee 11, 60598 Frankfurt am Main, Germany

Keywords:

Semantic Interpretation, Linked Open Data, SPARQL, Ontology, Spatial Data, Semantic Web, Neural

Machine Translation.

Abstract:

The rapid growth of geospatial data (at least 20% every year) makes spatial data increasingly heterogeneous.

With the emergence of Semantic Web technologies, more and more approaches are trying to group these data

in knowledge graphs, allowing to link data together and to facilitate their sharing, use and maintenance. These

approaches face the problem of homogenisation of these data which are not uniﬁed in the structure of the data

on the one hand and on the other hand have a vocabulary that varies greatly depending on the application

domain for which the data are dedicated and the language in which they are described. In order to solve

this problem of homogenisation, we present in this paper the foundations of a framework allowing to group

efﬁciently heterogeneous spatial data in a knowledge base. This knowledge base is based on an ontology linked

to Schema.org and DCAT-AP, and provides a data structure compatible with GeoSPARQL. This framework

allows the integration of geospatial data independently of their original language by translating them using

Neural Machine Translation.

1 INTRODUCTION

Nowadays, we live in a world lead by the informa-

tion. In the geospatial domain, it exists a lot of various

sources (e.g. governmental data, crowd sourcing data,

Linked Open Data, etc). Several approaches (Guan

et al., 2012; Gannon et al., 2020; Simon and Fr

ohlich,

2007) have attempted to implement frameworks in or-

der to be able to gather and share geospatial data more

easily. With the emergence of Semantic Web tech-

nologies, new approaches based on knowledge graphs

(such as (Karam and Melchiori, 2013; Abbas and Ojo,

2013; Larkou et al., 2013)) have proposed to deﬁne

open data structures that can be linked together in or-

der to facilitate data integration and sharing. The ma-

jor issue of data integration is to allow to enrich data

with other data from other sources (such as Open-

StreetMap, DBpedia, and Wikidata). Each source has

its own way of structuring the information. Moreover,

this information is composed of vocabulary that can

be from different languages. The diversity of struc-

tures and vocabularies that can be used in geospatial

information have pushed approaches to specialize in

https://orcid.org/0000-0001-8883-8454

https://orcid.org/0000-0001-8950-5723

few sources to reduce the complexity of integration.

It is, therefore, necessary to homogenize the differ-

ent geospatial data structure and to homogenize the

different vocabularies in different languages. Such

a homogenization requires to deﬁne a data structure

and a vocabulary sufﬁciently ﬂexible to take into ac-

count the evolution of data over time, language vari-

ations, and allowing to retrieve geospatial informa-

tion as quickly as possible. To meet these needs, in

this paper we propose the foundations of a framework

based on Semantic Web technologies, allowing us to

unify the different structures and vocabularies most

used in the geospatial domain in a single knowledge

base. This uniﬁcation allows a universal sharing of

geospatial data and facilitates collaboration between

different states and organizations worldwide. The

goal of this project is to enable the Federal Agency

for Cartography and Geodesy in Germany to (i) bring

together the geospatial information contained in the

data held by the different Bundeslaender (data in dif-

ferent formats and structures) into a single platform

in RDF form, (ii) enrich this data and link it to the

Linked Open Data, and (iii) query and use this plat-

form to create the most informative maps possible. To

reach this goal, the main contributions made are the

following:

314

Ponciano, C., Schaffert, M., Würriehausen, F. and Ponciano, J.

Publish and Enrich Geospatial Data as Linked Open Data.

DOI: 10.5220/0011550600003318

In Proceedings of the 18th International Conference on Web Information Systems and Technologies (WEBIST 2022), pages 314-319

ISBN: 978-989-758-613-2; ISSN: 2184-3252

• ontology deﬁning a geospatial data structure that

can be universally used

• method of homogenization of the different

geospatial vocabularies

• integration of heterogeneous geospatial data into

linked data.

The article is organized as follows: Section 2

presents the state of the challenges related to the cre-

ation of such a framework. Section 3 presents the pro-

posed solutions and section 4 concludes with a discus-

sion and future work to be done.

2 RELATED WORK AND

PROBLEM STATEMENTS

Storing geospatial data for widespread use and shar-

ing is a challenging research problem, primarily be-

cause of the rapid growth of data, at least by 20% ev-

ery year (Lee and Kang, 2015).

In (Jung et al., 2013), the authors present an

ontology-enabled framework to ﬁnd geographic ser-

vices. They aim to solve geospatial problems. The

presented ontology for GIS data has the beneﬁt of re-

specting “OGC Abstract Speciﬁcation Topic 5: Fea-

ture” that stipulates a geospatial feature should con-

tain a particular type of geometry with an SRS and

attributes. However, their ontology does not pro-

vide a common vocabulary to describe geographic

features and attributes. In (Sun et al., 2019), the

authors present the GeoDataOnt ontology that pro-

vides a general semantic basis for integrating and

sharing geospatial data. This ontology has the ben-

eﬁt of allowing semantic issues to be resolved at a

coarse level. However, the descriptions of some fea-

tures of some geospatial data are not integrated or

not precise enough, which limits the resolution of the

corresponding feature semantic problems using this

ontology. In (Budak Arpinar et al., 2006), the au-

thors provide an interesting ontology for geospatial

semantic analytics but do not deal with geospatial at-

tributes integration and uniformization. In (Dsouza

et al., 2021), the authors present the knowledge graph

WorldKG, which aims at representing geographic en-

tities in OpenStreetMap semantically. This work

facilitates using OpenStreetMap, a rich source of

openly available geographic information, on the Se-

mantic Web and among Linked Open Data, thanks

to its links with Wikidata and DBpedia. However,

they do not deal with the integration of heterogeneous

spatial data. In (Karalis et al., 2019), the authors

present YAGO2geo, an extension of YAGO2 (knowl-

edge graph based on Wikipedia, WordNet, and GeoN-

ames). It aims at adding geospatial information rep-

resented by geometries encoded by Open Geospatial

Consortium standards. They also integrate other at-

tributes; however, they add properties speciﬁc to the

dataset into YAGO2geo and do not use a common

vocabulary. Although these research projects do not

group heterogeneous spatial data in a knowledge base

at an advanced level and using a common vocabulary,

projects such as (Jung et al., 2013; Budak Arpinar

et al., 2006; Dsouza et al., 2021) demonstrate that in-

telligent data provisioning using Semantic Web meth-

ods appears to be an effective solution for simpliﬁed

(from a technical point of view) linking of ofﬁcial and

unofﬁcial data for geodata users. Moreover, projects

using ontology-based frameworks such as (Dsouza

et al., 2021; Sun et al., 2019; Karalis et al., 2019)

highlight the growing interest in structuring geospa-

tial data in the form of knowledge graphs. This struc-

turing method brings more ﬂexibility than traditional

database structures, which is essential in view of the

fast evolving nature of data. Such graphs are stored

in triplestores (such as Apache Marmotta

, Apache

Jena

, Eclipse RDF4J

, Strabon

, Oracle Spatial and

Graph

, GraphDB

, Stardog

, and Virtuoso Univer-

sal Server

) allowing fast read and write access by

SPARQL.

In the domain of geospatial, the GeoSPARQL

(Battle and Kolas, 2011) approach has greatly im-

proved access to geospatial data from a triplestore,

allowing to retrieve and update geospatial data with

simpliﬁed queries. However, GeoSPARQL requires

that the stored data be structured according to a par-

ticular form in order to be usable, but most of the

triplestores from the LOD have not added the struc-

ture proposed by GeoSPARQL. This constraint limits

the use of GeoSPARQL for the LOD.

The other main issue is the heterogeneity of the

geospatial data. Usually these data are stored in spe-

Apache Marmotta: https://marmotta.apache.org, ac-

cessed on 2022-07-07

Apache Jena: https://jena.apache.org/documentation/

tdb/, accessed on 2022-07-07

Eclipse RDF4J: https://rdf4j.org, accessed on 2022-

07-07

Strabon: http://strabon.di.uoa.gr, accessed on 2022-

07-07

Oracle Spatial and Graph: https://www.oracle.com/d

atabase/technologies/spatialandgraph.html, accessed on

2022-07-07

GraphDB: https://www.ontotext.com/products/graphd

b/, accessed on 2022-07-07

Stardog: https://www.stardog.com, accessed on 2022-

07-07

Virtuoso Universal Server: https://virtuoso.openlinks

w.com, accessed on 2022-07-07

Publish and Enrich Geospatial Data as Linked Open Data

315

ciﬁc formats (such as GeoJSON (Butler et al., 2016),

Shapeﬁle

, OpenStreetMap (Ramm et al., 2014))

having their own structure and being in different lan-

guages. This variation in vocabulary poses a major

challenge for the integration of data from different

sources. It requires either the creation of queries spe-

ciﬁc to the original structure of the data and the lan-

guage used, thus limiting the sharing and exploitation

of these data, or transform the data after deﬁning a

common data schema (such as INSPIRE) and convert

all other schema to the chosen one and do analyses

afterwards, which adds more steps to the processing.

We can deduce from this study that (i) the use of

knowledge base (in the form of ontology and triple-

store) is the most ﬂexible and promising way to store

large amounts of geospatial data, (ii) that there is cur-

rently no known platform to efﬁciently group hetero-

geneous spatial data in a knowledge base with a com-

mon vocabulary for all integrated datasets, (iii) that

the major challenge for the integration of geospatial

data is the diversity of structures and vocabularies

used to organize and express this information.

3 SOLUTION PROPOSED

In order to meet the challenge of storing vast amount

of geospatial data for universal sharing, we outline

a framework for integrating heterogeneous geospatial

data from different sources and languages in a struc-

tured and consistent manner. Figure 1 illustrates the

goal of this framework. As a position paper, we focus

on how to implement this framework using existing

research, rather than explaining all the mechanisms

that serialized it, which is part of our future work.

In section 3.1, we highlight solution for the inte-

gration of heterogeneous geospatial data into a Uni-

versal Spatial Knowledge Base (USKB). In Section

3.2 we highlight possibility to create common vocab-

ulary, on the basis of which universal queries can be

performed on data in RDF format. This common vo-

cabulary aims to bring together vocabularies used in

data from different sources such as Linked Open Data

and geospatial formats.

3.1 Automatic Integration of

Heterogeneous Data into a

Universal Spatial Knowledge Base

Among the various approaches developed to unify

geodata structure and vocabularies, the Ordnance Sur-

Shapeﬁle: https://www.nationalarchives.gov.uk/pron

om/x-fmt/235, accessed on 2022-07-07

Figure 1: Goal of the proposed framework.

vey’s Linked Data Platform proposes an approach

using OWL (Antoniou and Harmelen, 2004) to link

GeoSPARQL concepts to equivalent concepts in its

ontology. GeoSPARQL has thus become a stan-

dard for representing and querying linked geospatial

data for the Open Geospatial Consortium (OGC) (van

Rees, 2013) Semantic Web.

Inspired by this approach, we highlight the rele-

vance of creating an ontology combining the concepts

of GeoSPARQL with the work (Quarati et al., 2021).

Moreover, the work (Stadler et al., 2012) allows for

describing metadata quality concepts, and link them

with the LinkedGeoData.

Such an ontology aims to bring together geo-

information from different sources by converting

common ﬁle formats in the geodomain and their

application schemas, such as GeoJSON, Shapeﬁle,

OpenStreetMap, RDF (Pan, 2009). The work (Pa-

troumpas et al., 2014) proposed such conversion with-

out considering other ontologies. Thus, they are lim-

ited to the vocabulary used in the data ﬁle for creating

RDF concepts. It is therefore necessary to adapt the

RDF vocabulary and structure to those of the ontol-

ogy for which we intend to integrate them.

The RDF structure adaptation can be performed

with the help of an alignment of the ontology struc-

ture as proposed in (Li et al., 2008) using automatic

integration approaches developed in the works (Prud-

homme et al., 2020; Prudhomme et al., 2017).

However, such approaches required different

parametrisation according to the data source qual-

ity. This quality mainly depends of the source type.

Therefore an evaluation of data quality is needed to

guide the integration process.

As an exemple, such evaluation can be based on

the ”5-star Open Data”

principle as follows:

1. 1 star: Data available on the Web (in any format)

under an open license,

2. 2 stars: Data available as structured data (spread-

https://5stardata.info/en/, accessed on 2022-07-07

WEBIST 2022 - 18th International Conference on Web Information Systems and Technologies

316

sheet) in a proprietary format,

3. 3 stars: Data available in a non-proprietary open

format (such as CSV),

4. 4 stars: Data available linked to URIs that to de-

note things,

5. 5 stars: Data available and linked to other data to

provide context.

Automatic integration of data into an ontology

without human supervision can lead to an inconsis-

tent structure due to the accumulation of errors. Thus,

to ensure the consistency of the added data, each con-

cept is described using OWL2 (Consortium et al.,

2012) constraints. These constraints allow to verify

that the added data is consistent with the structure

imposed by the ontology. This consistency check is

performed using reasoner (such as HermiT (Glimm

et al., 2014), Owlgres (Stocker and Smith, 2008), Pel-

let (Sirin et al., 2007), DeLorea (Bobillo et al., 2012)).

3.2 Universal Vocabulary

The data vocabulary provided varies depending on

the source of the Linked Open Data (e.g. Wikidata

(van Veen, 2019), DBpedia

) and the data format

(e.g. GML, KML, GeoJSON). The establishment of

a common vocabulary allows for gathering geospatial

knowledge in one place and thus, facilitating the re-

trieval of geographic data. Access to the data knowl-

edge can then be done via a web interface.

To be universal, this language can be based on the

English lexicon provided by WordNet (Miller, 1995).

Related works such as (Frontini et al., 2016; Bond

and Bond, 2019) have succeed in linking GeoNames

Ontology and WordNet. Other approaches such as

Schema.org (Guha et al., 2016) and KBpedia

are

fully exploiting the power of linked open data to pro-

vide a varied and structured vocabulary in the En-

glish language. Recently DCAT- AP (Kirstein et al.,

2019) and GeoDCAT-AP

are planning to link their

knowledge to Schema.org

, making Schema.org a

promising base for the geospatial domain. Moreover,

Schema.org currently support complex geometries

and WKT literals, which would make the adoption of

GeoSPARQL straightforward.

Since the vocabulary comes from various lan-

guages, we propose to use a Neural Machine Trans-

DBpedia: https://www.dbpedia.org, accessed on 2022-

07-07

https://kbpedia.org, accessed on 2022-07-07

https://inspire.ec.europa.eu/good-practice/geodcat-ap,

accessed on 2022-07-07

https://www.w3.org/2015/spatial/wiki/ISO 19115 -

DCAT - Schema.org mapping, accessed on 2022-07-07

lation approach to automatically translate each term

into English while being able to automatically detect

the original language. The works (Koehn, 2020) and

(Stahlberg, 2020) provide a review of different Neural

Machine Translation approaches. Recently, the work

of (Zhao et al., 2021) proposes to improve these ap-

proaches by combining them with a knowledge graph,

thus improving the accuracy of the translation.

The translated vocabulary is then aligned with the

knowledge base vocabulary using an ontology align-

ment approach such as (Zhang et al., 2014). Follow-

ing this alignment, the aligned terms between the vo-

cabulary used in the RDF ﬁles and the vocabulary of

the knowledge base is submitted to the user for ap-

proval. The user can then validate and complete the

vocabulary match in order to allow the integration of

the data to enrich the knowledge base. The alignment

of the terms is then stored to allow for better automa-

tion in the future.

Figure 2 summarizes this process of supervised

learning and alignment.

Figure 2: Overview of supervised learning and alignment

process.

Let us take as an example a spatial data con-

taining the geometries and attributes of schools in

the Bundesland Rheinland-Pfalz in Germany in or-

der to illustrate the proposed solution. In this

dataset, a school has a geometry represented by a

point and the following list of attributes in Ger-

man:

Offnungszeiten, FaxNummer, and Name. The

names of these attributes will be translated into En-

glish as: opening hours, fax number and name

respectively. An individual of type dcat:Dataset

will be created to represent the data. Then for

each school contained in the data, an individual

will be created. This individual will be of type

geo:Feature (from GeoSPARQL) and schema:School

(from Schema.org). Then, an equivalence to the at-

tributes will be matched with Schema.org in order to

add the information related to this individual. In our

example, the values of the attributes

Offnungszeiten,

FaxNummer, and Name will be added respec-

tively with the properties schema:openingHours,

schema:faxNumber, schema:name thanks to the En-

glish translation done before. Finally, the individ-

ual will be linked to its geometry with the prop-

erty geo:hasGeometry. Its geometry will be of type

geo:Point and will have a value linked by the prop-

erty geo:asWKT.

Publish and Enrich Geospatial Data as Linked Open Data

317

4 DISCUSSION

In this position paper, we propose conceptual guide-

lines for the development of a framework to unify

the integration of geospatial data within a Universal

Spatial Knowledge Base (USKB) and to easily link

geospatial data to Linked Open Data. This frame-

work is composed of an ontology, whose concepts

are mainly based on standards (such as GeoSPARQL)

and well-known ontologies (as GeoNames). The var-

ious data sets contained in ﬁles (such as GeoJSON,

Shapeﬁle, OpenStreetMap, INSPIRE) are integrated

into this ontology using structured mapping. The con-

sistency of the data integration is ensured by a rea-

soning applied on the constraints deﬁned for each

concept, which ensures a continuous consistency of

the data. The data vocabulary is translated into En-

glish from its original language by a Neural Machine

Translation approach. The translated vocabulary is

mapped to the ontology vocabulary. The resulting

mapping is submitted to the user for veriﬁcation, who

can modify or approve the proposed mapping. The

decisions taken are then saved as a mapping table to

allow continuous learning of the geospatial vocabu-

lary and thus improve the mapping. The future work

consists in enriching the basic concepts of the ontol-

ogy and the mapping of the vocabulary by supervised

learning in order to make the integration process as

automated as possible. Furthermore, the quality of

the data integration must be thoroughly evaluated by

experts. The exploitation of expert knowledge mod-

elled in an ontology can be considered in order to au-

tomate this evaluation, e.g. by generating correspon-

dence concepts to semantically map the different data

systems used to the respective INSPIRE systems.

5 ONLINE RESOURCES

The framework ontology and source code is available

at https://github.com/JJponciano/SpaLod from the au-

thors.

ACKNOWLEDGEMENTS

This research is funded by the Federal Agency for

Cartography and Geodesy in Germany.

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