=Paper=
{{Paper
|id=Vol-1769/paper05
|storemode=property
|title=Semantic Enhancement of Volunteered Geographic Information
|pdfUrl=https://ceur-ws.org/Vol-1769/paper05.pdf
|volume=Vol-1769
|authors=Laura Di Rocco
|dblpUrl=https://dblp.org/rec/conf/aiia/Rocco16
}}
==Semantic Enhancement of Volunteered Geographic Information==
https://ceur-ws.org/Vol-1769/paper05.pdf
Semantic Enhancement of Volunteered
Geographic Information
Laura Di Rocco
DIBRIS - Università degli Studi di Genova
Abstract. The continue use of social media has created a revolution in
the production of data in terms of heterogeneity and volume. This huge
amount of data is often georeferenced, and the geospatial position of
data is a very relevant dimension for analysis. The considerable hetero-
geneity and the variable quality of user-generated data prevent however
the full exploitation of this information. In this PhD project, we focus
on user-generated geospatial data, commonly referred to as Volunteered
Geographic Information, where geographical objects are annotated by
using tags. The simplicity to use tags allows users to put a lot of tags
on objects. This creates a big and noisy tagspace in which it is hard to
find data tagged by other people. To overcome this heterogeneity and
quality problem, a solution is to rely on ontologies to classify spatial
entities tags and names. This problem is addressed in the PhD project
with specific reference to the tags used in OpenStreetMap for describ-
ing georeferenced objects relevant in urban context, producing as result
an ontology for classifying such objects, and a corresponding ontology
population approach.
Keywords: Geographic Ontology, Implicit/Explicit Geographical Information,
Volunteered Geographical Information, Semantic Classification
1 Introduction
In recent years, we are witnessing a revolution in the production of data, resulting
in a significant increase in data complexity, in terms of volume, heterogeneity, and
distribution. Data are increasingly being gathered by ubiquitous information-
sensing mobile devices, sensor networks, Web applications, software logs. Such
a huge amount and variety of data is a valuable source from which to extract
information and knowledge. This huge amount of data is often georeferenced,
and the geospatial position of data is a very relevant dimension for analysis. The
advent of new pervasive communication tools like social media plays a relevant
role in such a revolution in data production. Consistent user-generated data
represents indeed a valuable source for the extraction of new types of information
patterns and knowledge. The multifaceted nature of user-generated data, along
with its geographic component, can be exploited to better understand social
dynamics and propagation of information.
Georeferencing information can be explicit, if the user-generated data is ex-
plicitly associated with a geographic position –consider for instance the case of
content generated from a mobile phone which GPS coordinates are known to
the application gathering the content– or implicit, that is it can be deduced,
for instance, from the content itself. A typical example is the bulk of geospatial
information that can be extracted from short text messages exchanged by users
on Twitter. In this case, explicit geographic information can be available in the
metadata associated with the tweet (user profile location and GPS coordinates of
the device) or can be inferred, with variable degree of confidence, by the message
content itself, which may contain images, names of entities with known spatial
location, or by the users social relationships and activities.
Users play an important role as information producers also for what concerns
geospatial information itself, in Volunteered Geographic Information (VGI). Good-
child [5] defines VGI as “[...] a special case of the more general Web phenomenon
of user-generated content[...] ”. Crowdsourced geospatial data is becoming very
popular mainly due to its free availability and its constant updating. Among
all projects for spatial data crowdsourcing, OpenStreetMap (OSM) is by far the
most popular. OSM is a collaborative project to create a free editable map of
the world via crowdsourced data. The data uploaded to OSM is continuously
increasing. However, the considerable heterogeneity and the variable quality of
user-generated data prevent the full exploitation of this information. Specifi-
cally, in crowdsourced geospatial data, geographical objects are annotated by
tags. Tagging seems to be the natural way for people to classify objects as well
as an attractive way to discover new material. The simplicity in using tags allows
users to put a lot of tags on objects [2]. This results in a big and noisy tagspace
in which it is hard to find data tagged by other people. This is due also to the
subjectivity of tagging.
To overcome these heterogeneity and quality problems, a solution is to lift
from the syntactic to the semantic level both for what concerns spatial entities
tags and names, relying on ontologies. The paper is structured as follow: Section
1 presents the problem, Section 2 presents a comparison to state of the art
and methodology of our solution and Section 3 presents the case of study and
application of our ontology.
2 Problem Statement
The PhD project addresses the problem of classification of explicit geographic in-
formation coming from Volunteered Geographic Information. We want to seman-
tically enhance explicit geospatial information available in open source format
(OpenStreetMap), thus overcoming the heterogeneity and quality issues inherent
in crowdsourced data and tagging discussed above. In order to exploit OSM for
georeferencing, OSM tags need to be used.
An alternative approach is proposed by OSMOnto1 [3]. This is a relevant
related project proposing an ontology for tags. The purpose of the ontology of
tags is to stay as close as possible to the structure of the OSM files in order to
facilitate database querying. However this work is not relevant for our research
as it does not try to correct any possible conceptual mistakes in the taxonomy
of OSM tags, but rather to reflect it faithfully in the structure of the ontology.
1
It is possible to find more information here: http://wiki.openstreetmap.org/wiki/
OSMonto
The semantics of OSM tags is quite poor, this work aims at enhancing it
through the definition of a properly structured ontology to classify OSM tags.
This choice is due to limitation of existing semantic gazetteer. Indeed, classical
gazetteer, like Geonames2 , are not very useful as they are too coarse-grained. The
proposed approach is thus aimed at extracting accurate and complete georefer-
enced data (in terms of types of spatial objects) from crowdsourced information,
through an appropriate classification. The developed ontology allows to use the
data present in OSM as instances of an ontology. We will extract non-spatial
information from geospatial data in order to create a classification hierarchy. In
this way, OSM users will be able to choose the most suitable tag in OSM for
describing a geospatial object. Since the goal of developing a general-purpose
ontology for describing geospatial entities is very ambitious and different efforts
have been made with different specific goals [7,3,1], we restrict our goal as follows.
Our approach aims at classifying geospatial objects that are relevant in urban
contexts, thus, that may appear in a generic city, trying to avoid, whenever pos-
sible, to focus on the specificities of a particular city. To define our ontology,
we keep into account that we mainly want to “semantify” OSM tags, thus we
start from the analysis of OSM tags employed in the context of a specific urban
context: the city of “London, UK”. We start from London (UK) because there is
a big amount of OSM data, thus London (UK) provides a good dataset, likely
covering most of the relevant concepts in a urban context. Our proposed solution
will utilize a classification technique based on “facets”. We exploit the possibility
to model the domain in three mutual exclusive facets in order to obtain a total
generalization.
3 OpenStreetMap Faceted Ontology
Ontologies have been used for a set of tasks: improving communication between
agents (human or software), reusing data models, developing knowledge schemas,
etc. All these tasks deal with interoperability issues and can be applied in dif-
ferent domains.
State of the art. Several researchers try to extract other kind of information
from tweets, for example, to perform sentiment analysis. The main approaches
in this field rely on: (a) machine learning techniques, (b) AI techniques, agents
and ontologies. For example, the work of Kontopoulos et al. [6] describes two
possible approaches to create a domain ontology for sentiment analysis: Formal
Concept Analysis and Ontology Learning. In order to create an ontology, we
need a collection of objects together with some properties.
For sentiment analysis applications, it is possible to extract this information
from a collection of data, i.e., a training dataset.
These two techniques are not suitable for our goal for the following reasons.
First of all, because we want to use our ontologies for georeferencing microblogs
information (we do not have text to extract information) and secondly because
the geographic domain related to a city is not generalizable. Indeed, these two
techniques are very good in sentiment analysis domain and in natural language
processing field, as we have seen.
2
http://www.geonames.org/
Methodology. In our work we need to create an ontology that allows us to
search explicit geographic information only.
There are multiple ontology engineering methodologies that facilitate the
process of developing, maintaining and in overall handling complete life-cycle
management. Examples include KACTUS, METHONTOLOGY, SENSUS and
DILIGENT. In this work we do not focus on the methodological aspects of
ontology development, using rather a simple approach to develop our ontology.
This choice is due to the limited complexity of the domain and also because of
peculiarity of the project proposal, which requires a manual process to be used.
We do not want to put an over-structure on OSM tags. Ontologies exist that
want to put an over-structure on OSM tags. In particular, LinkedGeoData [9] is
a project that aims at linking OSM data to other LinkedData repositories (such
as GeoNames and/or other online ontologies) by converting it to RDF so that
it can be queried from a SPARQL endpoint. However, LinkedGeoData does not
include all OSM entities and therefore it is not very useful for our purposes.
Our target ontology aims at classifying geospatial objects that are relevant
in urban contexts. We cannot exploit existing Gazetteers, like GeoNames, that
have a low level of detail respect to internal subdivision of states and are thus too
coarse-grained for our purposes. We rather need a semantic gazetteer providing
a high level of detail (e.g., inside a city).
We then generalize the ontology classes to encompass concepts that may
occur in a generic city, trying to avoid, whenever possible, to focus on the speci-
ficities of a particular city. To this aim, we selected the faceted approach to
develop the ontology.
Facet ontology [8] classifies objects using multiple taxonomies. A facet is a
hierarchy of homogeneous concepts describing an aspect of the domain, where
each term denotes an atomic concept. Each facets is designed separately, and
models a distinct aspect of the domain. Each facet consists of a terminology, i.e.,
a finite set of names or terms, structured by a subsumption relation.
In our ontology, facets correspond to geophysical, geopolitical, and Point of
Interest aspects. The developed ontology allows data present in OSM to be used
as instances of an ontology. A significant semantic support is brought to volun-
teered geographic data allowing the search at a conceptual level. Conscious of
the heterogeneous nature of geospatial data, we do not provide any contribution
to the spatial component, already well structured. Instead we aim at improv-
ing the non-spatial content which is per se heterogeneous and only syntactically
semi-structured. The non-spatial content is now accessible through a semantic
structure which allows for the conceptual search. The use of “facet” takes into
account the different aspects involved (i.e., natural area, political area and Point
of Interest) thus obtaining a complete characterization of the domain of interest.
Population Approach. Differently from what happens for standard ontologies,
we decide to manage individuals of our ontology using a different approach. The
idea is to have individuals in form of rows of views on a relational database.
We will use a new technology able to manage unstructured data with a paral-
lel support. This technology is similar to NoSQL databases but it maintains a
relational structure.
The population approach is chosen relying on how our ontology will be used
by the future applications. In cases where individuals exist in ontologies, they
represent “concrete” objects that have to be classified. However, an ontology need
not include any individuals, but one of the general purposes of an ontology is
to provide a means of classifying individuals, even if those individuals are not
explicitly part of the ontology. For these reasons, we will use database views to
manage our individuals. In this way, we are able to directly manage the string
type returned by databases. This choice is made to simplify future usage. Indeed,
if we have an individual, we have an URI to represent the corresponding object.
With our approach, we perform a matching between OSM objects and our
concepts, using simple standard queries on a relational database. With matching
we mean the relationship between particular tags and concepts that allow to
automatise the population process.
4 Application & Use of Ontology
The developed approach finds a relevant application in the context of our ongo-
ing research project aimed at realtime integration of textual data coming from
microblogs and crowdsourced geospatial data [4]. Specifically, the target appli-
cation first gathers in realtime from Twitter, through the use of the suitable
streaming API, both explicitly georeferenced tweets and tweets missing an ex-
plicit geospatial information. Then, tweet contents are geoparsed relying on the
textual descriptions of OSM objects. Social relationships among users and their
activities (such as mentions and retweets) are then exploited to further refine
tweet (and corresponding user) geopositioning, since some social relations are
strong indicators of spatial proximity. Georeferencing information belonging from
different sources (content vs social interaction analysis), appropriately weighted
according to the respective confidence, are then merged. Since explicit tagging is
used only in a small percentage of tweets, we will use the geospatial information
implicit in the messages to improve the resolution of the georeferencing process.
This is useful for several applications. For example we could create heat maps
to highlight areas from which tweets are generated or areas which tweets refer
to. This could be very useful in application such as emergency response.
In our ongoing project [4], we proposed to extract implicit geoinformation
contained in tweet contents using a semantic gazetteer. The use of external
knowledge like a gazetteer can help us to detect toponyms in the tweet contents.
In order to extract toponyms from text, we plan to consider three different
approaches:
1. Perform a simple string matching with toponyms extracted from OSM.
2. Improve this matching by relying on a geospatial ontology. A semantic sup-
port can help us to find toponyms using also the context of the tweet (e.g.
the presence of word "cinema" allow us to search a toponym in the proper
class of the ontology), improving the precision of matching.
3. Rely on NLP classifiers in order to identify reference to geographical lo-
cations from texts in order to identify toponyms by the context of terms
(prepositions, verbs, ...).
These three approaches can help us to understand and evaluate the extend to
which semantic support improves geotagging of non-geotagged tweets.
The semantic enrichment allows us to obtain new geographical knowledge
that can be exploited to improve the set of extracted geonames and thus the
quality of geotagging.
To evaluate the use of ontology in this type of application, we have a clear
plan for a two-fold evaluation. More specifically, we will perform the following
two types of evaluation:
1. comparison between manually geotagged tweets and automatically geotagged
tweets;
2. for geotagged tweets, comparison between the known (exact) position and
the location inferred by our approach (only in case we have tweets with
coordinates and containing geonames). The first aim of our evaluation is to
understand if our approach works correctly on a specific dataset.
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