Personalized Environmental Service Conﬁguration and
Delivery Orchestration: The PESCaDO Demonstrator
Leo Wanner1,2, Marco Rospocher8, Stefanos Vrochidis6, Harald Bosch3, Nadjet
Bouayad-Agha2, Ulrich B¨
ugel4, Gerard Casamayor2, Thomas Ertl3, Desiree
Hilbring4, Ari Karppinen5, Ioannis Kompatsiaris6, Tarja Koskentalo7, Simon Mille2,
urgen Moßgraber4, Anastasia Moumtzidou6, Maria Myllynen7, Emanuele Pianta8,
Horacio Saggion2, Luciano Seraﬁni8, Virpi Tarvainen5, Sara Tonelli8
1Catalan Institute for Research and Advanced Studies, 2Dept. of Information and
Communication Technologies, Pompeu Fabra University, 3Visualization Institute, University of
Stuttgart, 4Fraunhofer Institute for Optronics, System Technologies and Image Exploitation,
5Finnish Meteorological Institute, 6Informatics and Telematics Institute, Centre for Research
and Technology Hellas, 7Helsinki Region Environmental Services Authority, 8Fondazione
Abstract. Citizens are increasingly aware of the inﬂuence of environmental and
meteorological conditions on the quality of their life. This results in an increas-
ing demand for personalized environmental information, i.e., information that is
tailored to citizens’ speciﬁc context and background. In this demonstration, we
present an environmental information system that addresses this demand in its
full complexity in the context of the PESCaDO EU project. Speciﬁcally, we will
show a system that supports submission of user generated queries related to en-
vironmental conditions. From the technical point of view, the system is tuned to
discover reliable data in the web and to process these data in order to convert
them into knowledge, which is stored in a dedicated repository. At run time, this
information is transferred into an ontology-based knowledge base, from which
then information relevant to the speciﬁc user is deduced and communicated in
the language of their preference.
1 Research background
Citizens are increasingly aware of the inﬂuence of environmental and meteorological
conditions on the quality of their life. One of the consequences of this awareness is
the demand for high quality environmental information that is tailored to one’s speciﬁc
context and background (e.g. health conditions, travel preferences, etc.), i.e., which is
personalized. Personalized environmental information may need to cover a variety of
aspects (such as meteorology, air quality, pollen, and trafﬁc) and take into account a
number of speciﬁc personal attributes (health, age, allergies, etc.) of the user, as well as
the intended use of the information. For instance, a pollen allergic person, planning to
do some outdoor activities, may be interested in being notiﬁed whether the pollen situ-
ation in the area may trigger some symptoms, or if the temperature is too hot for doing
physical exercise, while a city administrator has to be informed whether the current air
quality situation requires some actions to be urgently taken.
So far, only a few approaches have been proposed with a view of how this in-
formation can be facilitated in technical terms. All of these approaches focus on one
environmental aspect and only very few of them address the problem of information
personalization , , . We aim to address the above task in its full complexity.
In this work, carried on in the context of the PESCaDO EU project, we take ad-
vantage of the fact that nowadays, the World Wide Web already hosts a great range
of services (i.e. websites, which provide environmental information) that offer data on
each of the above aspects, such that, in principle, the required basic data are available.
The challenge is threefold: ﬁrst, to discover and orchestrate these services; second, to
process the obtained data in accordance with the needs of the user; and, third, to com-
municate the gained information in the users preferred mode.
The demonstration will aim, in particular, at showing how semantic web technolo-
gies are exploited to address this challenges in PESCaDO.
2 The PESCaDO Platform: Main modules and key semantic
The challenges mentioned in Section 1 require the involvement of an elevated number
of rather heterogeneous applications addressing various complex tasks: discovery of the
environmental service nodes in the web, distillation of the data from webpages, orches-
tration of the environmental service nodes, fusion of environmental data, assessment of
the data with respect to the needs of the addressee, selection of user-relevant content and
its delivery to the addressee, and, ﬁnally, interaction with the user. Thus, in PESCaDO
we developed a service-based infrastructure to integrate all these applications.
For a general overview of the running PESCaDO service platform1, and the type of
information produced, see: http://www.youtube.com/watch?v=c1Ym7ys3HCg. In
this section, we focus on presenting three tasks we addressed by applying semantic web
The back-bone of the PESCaDO service platform, exploited in each of these three
tasks, is an ontology-based knowledge base, the PESCaDO Knowledge Base (PKB),
where all the information relevant for a user request are dynamically instantiated. The
ontology, partially built exploiting automatic key-phrases extraction techniques , for-
malizes a variety of aspects related to the application context: environmental data, en-
vironmental nodes2, user requests, user proﬁles, warnings and recommendations trig-
gered by environmental conditions, logico-semantic relations (e.g. cause, implication)
between facts, and so on. The current version of the ontology consists of 241 classes,
672 individuals, 151 object properties, and 43 datatype properties.
2.1 Discovery of environmental nodes
The ﬁrst step towards the extraction and indexing of environmental information is the
discovery of environmental nodes, which can be considered as a problem of domain spe-
1A more comprehensive description of the system workﬂow can be found in 
2An environmental node is a provider of environmental data values, like for instance a web-site,
a web-service, or a measuring station
ciﬁc search. To this end, we implement a node discovery framework, which builds upon
state of the art domain speciﬁc search techniques, advanced content distillation, ontolo-
gies and supervised machine learning. The framework consists of three main parts: a)
Web search b) Post processing and c) Indexing and storage. Web search is realized with
the aid of a general-purpose search engine, which accesses large web indices. In this
implementation we employ Yahoo! Search BOSS API. In order to generate domain
speciﬁc queries, we apply two complementary techniques. First we use the ontology of
the PKB and we extract concepts and instances referring to types of environmental data
(e.g. temperature, birch pollen, PM10) and we combine them with geographical city
names automatically retrieved by geographical resources. In addition, the queries are
expanded by keyword spices , which are domain speciﬁc keywords extracted with
the aid of machine learning techniques from environmental websites.
During the post-processing step we perform supervised classiﬁcation with Support
Vector Machines to separate relevant from irrelevant nodes and we crawl each website
to further expand our search in an iterative manner. The determination of the relevance
of the nodes and their categorization is done using a classiﬁer that operates on a weight-
based vector of key phrases and concepts from the content and the structure of the
webpages. Subsequently, we parse the body and the metadata of the relevant webpages
in order to extract the structure and the clues that reveal the information presented.
Finally, the information obtained with respect to each relevant node is indexed in a
Sensor Observation Service (SOS)  compliant repository, which can be accessed and
retrieved by the system when a user request is submitted.
The whole discovery procedure is automatic, however an administrative user could
intervene through an interactive user interface, in order to select geographic regions
of interest to perform the discovery, optimize the selection of keyword spices, and
parametrize the training of the classiﬁers.
2.2 Processing raw environmental data to obtain content
The user interface of the PESCaDO system guides the user in formulating a request,
which is instantiated in all its details (e.g. type of request, user proﬁle, time period,
geographic location) in the PKB. By exploiting Description Logics (DL) reasoning on
the PKB, the system determines from the request description which are the types of en-
vironmental data which constitute the raw content necessary to fulﬁl the user needs. A
speciﬁc component of the system is then responsible of selecting from the SOS repos-
itory the actual values (observed, forecasted, historical) for the selected types of envi-
ronmental data, and to appropriately instantiate them in the PKB.
At this stage, the raw data retrieved from the environmental nodes are processed to
derive additional personalized content from them, such as data aggregations, qualitative
scaling of numerical data, and user tailored recommendations and warnings triggered by
the environmental data relevant for the speciﬁc user query. Logico-semantic relations
are also instantiated at this stage, for instance to represent whether a certain pollen
concentration value causes the triggering of a recommendation to the user, due to its
sensitiveness to that pollen.
The computation of this inferred content is performed by the decision support ser-
vice of the PESCaDO Platform by combining some complementary reasoning strate-
gies, including DL reasoning and rule-based reasoning. A two layer reasoning infras-
tructure is currently in place. The ﬁrst layer exploits the HermiT reasoner for the OWL
DL reasoning services. The second layer is stacked on top of the previous layer. It uses
the Jena RETE rule engine, which performs the rule-based reasoning computation.
2.3 Generating user information from content
As is common in Natural Language Generation, our information generator is divided
into two major modules: the text planning module and the linguistic generation module
(with the latter taking as input the text plan produced by the former).
Text Planning The text planning module is divided into a content selection module and
discourse structuring module. As is common in report generation, our content selection
is schema- (or template-) based. Therefore, the ontology of the PKB introduced above
deﬁnes a class Schema with an n-ary schema component object property whose range
can be any individuals of the PKB itself.
Similar to , we assume the output of the discourse structuring module to be a
well-formed text plan which consists of (i) elementary discourse units (EDUs) that
group together individuals of the PKB, (ii) discourse relations between EDUs and/or
individuals of the PKB, and (iii) precedence relations between EDUs. This structure
translates into two top classes of the ontology of the PKB: EDU with an n-ary EDU
component relation and a linear precedence property, and Discourse Relation
with nucleus and satellite relation. A set of SPA RQ L query rules are deﬁned to instantiate
the various concepts and relations.
Content Selection (CS) operates on the output of the decision support service. It
selects the content to be included in the report and groups it by topic, instantiating a
number of schemas for each topic. The inclusion of a given individual in a schema
can be subject to some restrictions deﬁned in the queries; for example, if the minimum
and maximum air quality index (AQI) values are identical, or if the maximum AQI
value triggers a user recommendation or warning, then only the maximum AQI value is
selected (the minimum AQI rating is omitted).
Discourse structuring is carried out by a pipeline of three rule-based submodules: (i)
Elementary Discourse Unit (EDU) Determination, which groups topically related PKB
individuals into propositional units starting from the schemas determined during CS;
(ii) Mapping logico-semantic relations to discourse relations; and (iii) EDU Ordering,
which introduces a precedence relation between EDUs using a number of heuristics
derived from interviews with domain communication experts.
Linguistic generation Our linguistic generation module is based on a multilevel lin-
guistic model of the Meaning-Text Theory (MTM) , such that the generation con-
sists of a series of mappings between structures of adjacent strata (from the conceptual
stratum to the linguistic surface stratum): Conceptual Structure (ConStr)⇒Semantic
Structure (SemStr)⇒Deep-Syntactic Structure (DSyntStr)⇒Surface-Syntactic Struc-
ture (SSyntStr)⇒Deep-Morphological Structure (DMorphStr)⇒Surface-Morpholog-
ical Structure (SMorphStr)⇒Text. Starting from the conceptual stratum, for each pair
of adjacent strata Siand Si+1, a transition grammar Gi
i+1 is deﬁned; see .
The ConStr is derived from each text plan produced by the text planning component.
In a sense, ConStr can thus be considered a projection of selected fragments of the
ontologies onto a linguistically motivated structure. ConStrs are language-independent
and thus ideal as starting point of multilingual generation.
3 System Demonstration
The system demonstration will show how the PKB is instantiated and exploited by the
different services composing the PESCaDO Platform in the context of two different
application scenarios, one about health safety decision support for end users and one
about administrative decision support. In particular, the demo attendees will have the
chance to see how the raw environmental data are dynamically processed with ontology-
based techniques to obtain reports. Furthermore, we will demonstrate how to use and
set-up the tool for environmental node discovery.
The work described in this paper has been partially funded by the European Commis-
sion under the contract number FP7-248594, PESCaDO (Personalized Environmental
Service Conﬁguration and Delivery Orchestration) project.
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