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An Ontology-based Decision Support Framework for Personalized Quality of Life Recommendations

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As urban atmospheric conditions are tightly connected to citizens’ quality of life, the concept of efficient environmental decision support systems becomes highly relevant. However, the scale and heterogeneity of the involved data, together with the need for associating environmental information with physical reality, increase the complexity of the problem. In this work, we capitalize on the semantic expressiveness of ontologies to build a framework that uniformly covers all phases of the decision making process: from structuring and integration of data, to inference of new knowledge. We define a simplified ontology schema for representing the status of the environment and its impact on citizens’ health and actions. We also implement a novel ontology- and rule-based reasoning mechanism for generating personalized recommendations, capable of treating differently individuals with diverse levels of vulnerability under poor air quality conditions. The overall framework is easily adaptable to new sources and needs.
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An Ontology-based Decision Support Framework for
Personalized Quality of Life Recommendations
Marina Riga1,2 (), Efstratios Kontopoulos1, Kostas Karatzas2,
Stefanos Vrochidis1 and Ioannis Kompatsiaris1
1 CERTH-ITI, Information Technologies Institute, Thessaloniki, Greece
{mriga, skontopo, stefanos, ikom}@iti.gr
2 ISAG-EI Group, Dept. of Mechan. Engineering, Aristotle Univ. of Thessaloniki, Greece
{mriga, kostas}@isag.meng.auth.gr
Abstract. As urban atmospheric conditions are tightly connected to citizens’
quality of life, the concept of efficient environmental decision support systems
becomes highly relevant. However, the scale and heterogeneity of the involved
data, together with the need for associating environmental information with
physical reality, increase the complexity of the problem. In this work, we capi-
talize on the semantic expressiveness of ontologies to build a framework that
uniformly covers all phases of the decision making process: from structuring
and integration of data, to inference of new knowledge. We define a simplified
ontology schema for representing the status of the environment and its impact
on citizens’ health and actions. We also implement a novel ontology- and rule-
based reasoning mechanism for generating personalized recommendations, ca-
pable of treating differently individuals with diverse levels of vulnerability un-
der poor air quality conditions. The overall framework is easily adaptable to
new sources and needs.
Keywords: personalized decision support, ontology, OWL 2, SPIN, air quality
recommendations, user profiling.
1 Introduction
In the environmental domain, it was early recognized that there is a positive correla-
tion between the quality of the atmospheric environment and people’s quality of life
(QoL), acknowledging the fact that sensitive parts of the population suffer as atmos-
pheric quality parameters become worse. Although air quality (AQ) experts could
benefit from rapid advances in information technology and telecommunications,
which enabled the efficient monitoring, analysis, transmission and sharing of scien-
tific AQ data, it was only after the European Union’s Directive 97/101/EC that envi-
ronmental information should mandatorily become available and easily accessible to
the public via official and direct communication channels.
As a first result, several urban air quality management and information systems
emerged. However, the delivered content was usually limited to spatiotemporal air
quality observations, emission data, or AQ forecasts [1], with no sufficient explication
of their meaning or impact to individuals’ health. The provision of the aforementioned
content in a way that can support end-users in decision-making activities is an integral
part of the so called environmental decision support system (EDSS).
The way communication of AQ information is handled in EDSSs is important for
their wider acceptance. Humans perceive environmental quality on the basis of their
personal interests; they need to be informed if caused events have an impact on the
daily living [2-3]. Hence, systems that link the location, the incident, and the individ-
ual are of high interest for the end-users [4]. In this direction, relevant initiatives, such
as PESCaDO
1
, AirForU
2
, Clean Air Nation
3
and Air Visual
4
, demonstrate the added
value of real-time, location-based AQ information and recommendation services.
However, these applications do not handle user-profiles differentiation, but produce
general advice under poor AQ conditions that universally apply to sensitive people
and not to the specific user who queries for decision support (DS).
Our motivation is to integrate the involved data and processes of an EDSS in a uni-
form, modular and user-profile centric framework. The effectiveness of this demand-
ing task lies in the following subtasks: (i) to efficiently handle the heterogeneous and
multifaceted nature of data, (ii) to adequately associate the provided experts’
knowledge and rules for targeted, user-profile driven recommendation provision, and
(iii) to facilitate scalability and reusability of the framework to third-party modules. In
this context, we take advantage of the semantic expressiveness of ontologies to deal
with the above issues. Ontologies are state-of-the-art Semantic Web technologies for
structuring and semantically integrating heterogeneous content. Among other applica-
tions, they have been successfully adopted for covering individual parts of the deci-
sion making process [5]: (i) collecting, storing, and processing data, (ii) formulating
the decision-making problem, (iii) reasoning over the data to reach decisions.
In contrast to existing ontology-based decision support systems (DSSs) that merely
exploit the semantic web technologies in parts of the decision making process (see
next section), our proposed approach demonstrates the extensive use of ontologies
and semantic reasoning technologies by handling both the static (representation) and
dynamic processes (realization, inference) of a DSS operation. The proposed schema
comprises a set of ontological concepts and relations for semantically representing a
primitive section of experts’ knowledge and AQ dynamics, focusing on definitions of
air pollutants’ measurements, health risks, sensitive groups, and relevant user-profile
driven recommendations. We implement a novel rule-based ontological reasoning
mechanism for routing the problem of dynamic classification and new knowledge
extraction to support personalized recommendation provision. To the best of our
knowledge, no other DSS covers this multifaceted task as a whole through the adop-
tion of ontologies. The proposed work comprises the operational EDSS of the
hackAIR EU project [6].
1
http://pescado-project.upf.edu/
2
http://newsroom.ucla.edu/releases/new-app-lets-you-check-air-quality-as-easily-as-checking-
the-weather
3
http://www.greenpeace.org/india/Clean-Air-Nation/
4
https://airvisual.com/app
2 Related Work
An EDSS literature review reveals various implementations, with respect to: (i) the
application domain for DS (e.g. urban air quality management, extreme climate risks
administration, etc.) and (ii) the technological approaches (e.g. computational intelli-
gence methods, mathematical models, etc.). A detailed review of EDSS technologies,
tools, and use cases is presented in [7-8]. In most related work items, DS is addressed
for administrative purposes, involving experts or regulatory authorities as the targeted
end-users. Instead, our proposed framework aims to support QoL services for the
general public, with an additional strategic difference against existing implementa-
tions, the process of “translatingAQ observations into user-profile driven recom-
mendations, for personalized guidance in severe atmospheric conditions.
Considering ontologies in the environmental domain, numerous implementations
exist covering the representation of abstract, general concepts (e.g. SWEET, a modu-
lar schema with 6,000 concepts in 200 ontologies describing concepts of physical and
ecological phenomena, meteorological conditions, processes, activities [9]) as well as
domain-specific, applied concepts (e.g. the PESCaDO ontology for personalized
environmental DS [5], the EnvO ontology for the concise description of environmen-
tal features, materials and habitats [10], the AIR_POLLUTION_Onto ontology for air
pollution analysis and control [11], etc.). Inspired by their expressiveness and adapta-
bility, we build our relevant representations as described in the next section.
Ontologies have been proposed in EDSS for different tasks: in [ 12] for semantic
search and easy access of structured environmental data; in [13] for integrating exist-
ing local databases of environmental data as part of the Linked Open Data cloud,
enabling the linking of data in an established context and the dissemination of envi-
ronmental information to the masses; in [14] for facilitating the process of selecting
domestic solar hot water systems according to specific criteria, and in [15] for inte-
grating heterogeneous content from multiple environmental sources. Despite the in-
creasing deployment of ontology-based solutions in DSS, their potential is merely
exploited, either for creating a structured representation of the domain of interest, or
for supporting parts of the decision making process. With our proposed framework,
we demonstrate the efficient use of ontologies and their supported technologies in all
the basic components of the DSS.
Similarly to our approach, within the context of the PESCaDO EU project, ontolo-
gies were used as the backbone of the proposed EDSS, supporting all phases of the
decision making process [16]; nevertheless, its rules along with the reasoning module
are hardcoded in the source code, resulting in a highly inflexible approach. On the
other hand, our implementation pushes the usage of ontologies one step further: both
the domain knowledge and the experts’ rules are developed at the ontology level, with
the use of the OWL language and SPIN rules (see next section). The proposed DS
framework operates as a stand-alone and uniformly developed module that can be
easily adoptable by external sources, independently of their implemented technolo-
gies. It is also flexible and extensible, in terms of easy initialization of the different
concepts, rules and recommendations even by non-ontology experts, by simply fol-
lowing the definitions of the proposed schema.
3 Proposed Approach
In computer science, an ontology is defined as a formal explicit specification of the
terms and relations that describe a domain of discourse in a structured and semantical-
ly rich way [17]. The adoption of ontologies enables the understanding, sharing and
reuse of information among different systems. Their capabilities fit perfectly to the
task of describing and integrating heterogeneous content, and of dynamically inferring
new knowledge, in a multidisciplinary field of study such as air quality. In this paper,
we do not exhaustively represent the complete dynamics and facts or relevant associa-
tion rules existing in the AQ domain; instead we demonstrate a practical paradigm of
ontology use for real scenarios, conforming to related literature suggestions stating
that citizens as end-users seek personalized information services, with timely or in
advance AQ information provision, with respect to their location of interest [4].
The proposed approach handles all three basic components of a general DSS [18]
through the use of ontologies: (i) the data component, i.e. the ontology-based schema
developed for representing an excerpt of AQ domain experts’ knowledge and of AQ
information provision; (ii) the model component, which is an ontology-based and
augmented with rules dynamic representation of principles that generate recommen-
dations by combining different disciplines in the field of interest; and (iii) the user-
system interaction component, which involves the communication of the user with the
system. All of the above are more thoroughly described in the following subsections.
Fig. 1. Overall architecture and workflow of the proposed approach
In this context, we implemented a 3-layered decision support knowledge base (KB)
on the basis of three interconnected ontologies: (1) the TBox, i.e. the bottom layer that
formalizes information relevant to the concepts of discourse (user profile, AQ meas-
urements, requests and personalized recommendations) and their interrelations, (2) the
ABox, i.e. the middle layer that is based on the schema from the TBox declarations
and formalizes information relevant to membership/attribute assertions (actual users,
observations, etc.), and (3) the rules layer, i.e. the upper layer that is based on both the
aforementioned layers and formalizes the set of rules for reasoning (inference) differ-
ent levels of interpretations. A general overview of the proposed ontology-based
architecture and information workflow is presented in Fig. 1
3.1 Domain Knowledge Modelling
The representational primitives utilized in ontologies are: classes referring to concepts
or (abstract) entities that are assumed to exist in some domain of interest, individuals,
which are instantiations (i.e. objects) of the classes, and properties, i.e. relationships
that hold among objects. Their formal definition within the ontology typically carries
information about concepts’ meaning (semantics) and constraints that exist within the
actual context of the domain of discourse.
To elicit the requirements that our ontology should satisfy, we followed the guide-
lines proposed by the NeOn methodology [19]. First, we defined the multifaceted
purpose of use of our ontology, which includes the following goals: (i) to serve as an
operational framework for the representation and orchestration of heterogeneous
environmental, health, user profile-related data; (ii) to integrate the rules that govern
air pollution and their impact to QoL, according to provided environmental and health
experts’ knowledge; and (iii) to support user-oriented recommendation services, with
respect to personal health/user preferences (i.e. activities, daily routine, asthma, etc.)
and to current AQ conditions for the location of interest.
Then, we identified the ontology’s intended users as: (i) individuals, people with
health sensitivities, those working or exercising outdoors, all those interested in re-
ceiving information about existing AQ conditions, so as to limit their exposure to
hazardous conditions, or to increase their awareness about the impact of air pollution
under special circumstances; (ii) public administrators or environmental experts,
interested in receiving AQ information for professional reasons; (iii) technology ex-
perts, developers or ontology engineers, interested in adopting and expanding the
ontology model for relevant DSSs and services.
The aforementioned definitions implicitly define the content and structure of the
ontology. The latter is developed in OWL 2, a W3C standard ontology language [20].
Here, we present an excerpt of conceptualizations that are significant for adequately
structuring the DS process following a user request. An indicative graph of the main
classes and their relationships, developed in Grafoo [21], is presented in Fig. 2.
Class Person encapsulates those individuals’ characteristics that are required for
the reasoning process, specified on the basis of the available health-related advice and
knowledge. We define distinct subclasses of class Person, with respect to the follow-
ing parameters: (i) year of birth (ChildPerson, ElderlyPerson etc.), (ii) health
sensitivity (PregnantFemalePerson, SensitiveHealthPerson), (iii) daily pre-
ferred activities (SportsWalkingPerson, OutdoorJobPerson etc.). Specific on-
tology rules (see next section) and class expression axioms (e.g. Equivalent-
Classes (OutdoorJobPerson ObjectSomeValuesFrom (worksOutdoors
"true"^^xsd:boolean))) declare the underlying semantics and restrictions.
In our proposed schema, a user may be classified as an instance of more than one
types (e.g. SensitiveHealthPerson and SportsWalkingPerson), unless such
combinations are forbidden (e.g. DisjointClasses (ElderlyPerson Child-
Person)). Moreover, a Person instance can be associated with other linked user
profiles, enabling a one-to-many DS provision. An example case could be a mother
with respiratory problems requesting personalized AQ recommendations for herself
and her child, where separate recommendations will be produced from a single re-
quest, with respect to each profile’s characteristics.
Fig. 2. An excerpt of classes and relations of the proposed ontology; properties with dashed line
are inferred via rules.
Class Activity represents instances of indoor/outdoor activities. On the basis of
rules, recommendations to the users differ according to the nature or intensity of the
activity; e.g. in case of harmful AQ conditions, activities like running or biking may
lead to increased oxygen uptake and should be avoided or replaced with alternatives.
Class HealthProblem represents health problems (e.g. respiratory, cardiovascu-
lar, circulatory diseases) that the DSS takes into account when generating recommen-
dations with respect to users’ health sensitivities, under poor air quality conditions.
Class Location represents the location of interest (city, country or as coordi-
nates). The more discretization introduced in the ontology, the more complex rule-
definitions are needed to support the decision making process, i.e. to propose alterna-
tive areas with less pollution or close to the area of interest.
Class EnvironmentalData represents environmental measurements, i.e. observa-
tions from different sources regarding air pollutants, pollen, weather or any other
measurable environmental aspect that is involved in the recommendation process.
Class Request structures the content of a user-request for DS. An instance of that
type connects all these notions (user profile characteristics, the location of interest, the
existing AQ measurements, and preferred activities) that are fed into the rule-based
DSS for personalized recommendation provision.
Class Recommendation represents messages with fixed content, as defined by en-
vironmental experts for recommendation, together with details on: (a) which type of
user such messages concern, and (b) under which AQ conditions they should be in-
formed. An instance of that type may contain the actual message to be inferred to the
user. Rules defined in the respective layer of the framework handle the matching
between categories of users and defined recommendations.
3.2 Reasoning over Domain Knowledge
For the semantic interpretation and inference of new knowledge, we implemented a
rule-based reasoning layer that fully complies with the SPARQL Inferencing Notation
(SPIN) framework, which is a well-established standard for representing SPARQL
rules and constraints on Semantic Web models [22]. SPIN rules are linked to ontology
classes and stored alongside the domain model as RDF triples, thus supporting a ho-
listic, dynamic, semantically enriched approach which fits perfectly to the require-
ments of our defined DS problem.
In the proposed framework, the rule layer is implemented separately from the ab-
stract schema (TBox) and the assertions (ABox), and the rules are grouped in two
different levels. Thus, the reasoning process operates incrementally, where inferences
from one step serve as input to rules of the following steps, moving from more gen-
eral to more specific derivations. Such a distinction and hierarchy of rules facilitates
the extensibility of the reasoning module, eliminating the intervention to high levels
of rules when lower levels change, and vice versa.
In order to fully exploit SPIN’s capabilities, we capitalize on the SPIN vocabulary
(http://spinrdf.org/spl); among the available concepts, we adopt/extend three specific
types: spin:Functions, spin:Rule, and spin:MagicProperties, which serve
different functionalities (select/construct triples, reuse SPARQL blocks, etc.).
Fig. 3. An example rule that combines inferred data and function’s output to create new triples.
The first level of rules (109 in total) takes into account the schema and populated
instances and produces low level derivations for the next level. These rules handle the
transformation of age values to relevant age groups, of AQ observations to AQI scales
and the classification of user profiles into those profile categories for which reco m-
mendations are available, on the basis of provided environmental experts’ knowledge.
Considering the rule-driven user classification process, a user is classified in one of
the available basic profiles (subclasses of class Person), or in any permissible com-
bination of them. Rules handle complex profiles automatically, by “downgrading”
them into those combinations for which recommendations are defined. A user may be
provided with more than one recommendation messages, with respect to its profile
characteristics and the rule-based classification output.
The second level of rules (150 in total) takes into account preceding results from
user- and AQI-related inferences, as input to relevant SPIN functions, which in turn
generate the text of the most relevant recommendation per case from a list of experts’
recommendations residing in the ontology. An example of the second level’s SPIN
rule is presented in Fig. 3, where a recommendation is formulated on the basis of
preceding inferences: the user belongs to a specific combined category (triple #6) and
the user’s location was inferred to have a bad AQI (triple #11) at the time of request
for DS. The user’s preferred language (triples #8-9) is also considered for the final
recommendation outcome, since multilingual messages are integrated in the ontology.
3.3 Communication with Ontology-based Framework
We establish an interoperable communication between the decision support KB and
external modules, with the implementation of RESTful services. Complex ontological
definitions and rules are hidden behind the developed web services, which handle the
dynamic population of the ontology with actual data (AQ observations, user profile
details, requests for DS) and the automated inference of personalized recommenda-
tions. Services were implemented in Java EE 7, with the adoption of Apache Jena
framework (https://jena.apache.org/) for manipulating RDF graphs and the SPIN API
(http://topbraid.org/spin/api/) for performing rule-based inference.
4 Test Cases and Inference Results
Our proposed ontology-based framework comprises the operational DS module of the
hackAIR platform for generating meaningful QoL information, personalized accord-
ing to the citizens’ profile requirements. In this context, we demonstrate its function-
ality through the following indicative scenario: Valeria, a 32-year-old woman, preg-
nant with respiratory problems (asthma), queries the hackAIR application for infor-
mation about existing AQ in an area where she usually goes for long walks. At the
time of request, the PM10 values are extremely high (e.g. 150μg/m3). The process
executed is given below:
(1) The request is sent from the hackAIR app to the ontology-based module
through the supported web service;
(2) The user-profile details (Table 1-A) as well as the location of interest (Table 1-
B) and AQ measurement (Table 1-C) are dynamically populated in the ABox
according to the schema declared in the TBox;
(3) An instance of Request type is additionally formulated (Table 1-D) by inte-
grates all involved information declared in the previous step;
(4) The recommendation module is triggered. SPIN rules are activated according
to the level of suitability to the case. Results are produced dynamically (Table
1-E), moving from more general to more specific inferences;
(5) Inferences asserted as recommendations to the user, are dynamically extracted
from the ontology and pushed back to the application for visualization.
Table 1. Instances in Turtle format
5
as populated/inferenced in our proposed framework
A. User profile
abox:Valeria
rdf:type TBox:Person ; tbox:hasAge 32^^xsd:integer ;
tbox:hasGender tbox:female ; tbox:isSensitiveTo tbox:Asthma ;
tbox:isPregnant "true"^^xsd:boolean ;
tbox:hasLocation abox:location_V ;
tbox:hasPreferredActivity tbox:walking_activity ; .
B. Location
abox:location_V
tbox:hasEnvironmentalData abox:PM10EnvData_location_V .
C. Environmental data
abox:PM10EnvData_location_V
rdf:type tbox:AirPollutantEnvironmentalData ;
tbox:hasEnvironmentalDataType tbox:PM10 ;
tbox:hasNumericalValue abox:PM10Value_location_V ; .
abox:PM10Value_location_V
rdf:type tbox:AirPollutantValue ;
tbox:hasUnit abox:microGramsPerCubicMeter ;
tbox:hasValueValue "150.0"^^xsd:double ; .
D. Request
abox:request_XYZ
rdf:type tbox:Request ;
tbox:involvesEnvironmentalData abox:PM10EnvData_location_V ;
tbox:involvesLocation abox:location_V ;
tbox:involvesPerson abox:Valeria ;
E. Inferences
abox:Valeria
rdf:type tbox:AdultPerson ;
rdf:type tbox:PregnantFemalePerson ;
rdf:type tbox:SensitiveHealthPerson ;
rdf:type tbox:SportsWalkingPerson ;
rdf:type tbox:Pregnant_Sensitive_Person ;
rdf:type tbox:Pregnant_SportsWalking_Person ; .
abox:location_V tbox:hasRelatedIndex tbox:AQI_bad ; .
abox:Valeria
tbox:isProvidedWithRecommendation [
rdf:type tbox:LimitExposureRecommendation ;
tbox:hasDescription "You should go for a walk in an area with
cleaner air."@en ;
tbox:hasDescriptionIdentifier "walking" ; ] ;
tbox:isProvidedWithRecommendation [
rdf:type tbox:LimitExposureRecommendation ;
tbox:hasDescription "Consider avoiding any intense outdoor activ-
ity in your area! The existing air quality might be harmful
for your health."@en ;
tbox:hasDescriptionIdentifier "general personalized" ; ] ; .
5
Details about the Turtle format are available at: https://www.w3.org/TR/turtle/
Focusing on the inference results presented in Table 1-E, we may distinguish the
move from general-to-specific classifications, as those were produced by automatical-
ly executing different types/levels of implemented SPIN rules. First, the user is cate-
gorized as PregnantFemalePerson, SensitiveHealthPerson and Sports-
WalkingPerson, conforming to relevant information provided in the user profile
(Table 1-A); the user is then categorized in two combined classes (Preg-
nant_Sensitive_Person and Pregnant_SportsWalking_Person) which have
direct association to the available recommendations provided by the system. Consid-
ering these two specific subclasses that the user belongs to, and given the fact that the
air quality index in the area of interest is inferred as AQI_bad, the system suggests
two different recommendations, one related to the user’s preferred activity (walking)
and the other to the user’s health sensitivity (pregnancy, asthma).
In a different scenario, the rule-based inference mechanism would follow the same
reasoning sequence but would trigger different rules that correspond to the semantics
behind the interpreted data. For example, different user profile characteristics or pref-
erences would lead to different user profile classifications and, thus, different recom-
mendation messages to the users, on the basis of existing AQ condition. Representa-
tive scenarios that were created within the hackAIR project, with support from the
project partners and environmental experts, are demonstrated in [23].
5 Evaluation
For the evaluation of the proposed representation and reasoning framework, we focus
on the following aspects: (i) the consistency of the provided results, by examining if
the inferred recommendations comply with those targeted to be given through the
classification and reasoning process; (ii) its performance, in terms of elapsed time
when a request is submitted to the system. Unfortunately, a direct comparison of
response times between the proposed framework and alternative approaches ([5, 15])
is not feasible, since there are no benchmarks to follow; systems have different com-
plexity, demonstrate different functionalities, input or internal processes, target differ-
ent recommendation outcomes, and the implementation details that are missing block
the reproduction of identical experiments within our proposed context.
For the consistency checking task, the environmental experts and pilot users of the
hackAIR project performed a thorough analysis of the reasoning process by examin-
ing the recommendation inferences for each ontology-represented use case. The eval-
uation showed a deviation from the planned system behavior in only two cases, due to
wrong intermediate classifications. The problem was fixed by correcting the corre-
sponding rules in the ontology.
Regarding the performance evaluation of the implemented DSS, we considered 11
problem (request) descriptions, differing in size and content: simple and complex
profiles, with one or more involved users per request, with different profile character-
istics, etc. For all use cases, the response time of the overall DS process ranged from
1.32 to 1.89 sec. (any differentiation in times depends mainly on the complexity of the
rules performed per case), with an average time of 1.61 sec. The evaluation ran on a
computer with: Intel® Core™ i5-4690K, x64-based processor at 3.50GHz, with
16GB RAM memory. Overall, our ontology-based DSS was proved to be a light-
weight approach, complying with the needs for fast computations and response times
of the reasoning service, avoiding any redundant system complexity.
6 Advantages of the Proposed Implementation
Through the extensive use of ontologies we achieve to create an integrated, stand-
alone DS framework that is self-identified (structures the content and dynamically
computes implicit values based on knowledge stated in the schema), and self-
triggered (fires the reasoning process automatically and appropriate rules are execut-
ed when needed). The advantages of such an implementation are described below.
Uniformity: SPIN rules are defined as RDF triples alongside the ontology model.
Such an approach prevents additional programming effort (rule-based inference is
operated via the integrated SPIN engine) and facilitates the adoption of the DS mod-
ule and external sources through implemented RESTful web services.
Flexibility: The multifaceted proposed framework permits multilingual definition of
recommendations through the initialization of a single instance of Recommendation
type per message. This can “carry” the same text in any of the supported languages,
while an appropriate SPIN rule will return to the user the inferred message on his/her
preferred spoken language. Different recommendation messages for the same use case
are also easily supported, in a sense that the proposed framework can select either
randomly, or by validating specific weighted factors, a personalized recommendation
from a “bag of messages”, all of which are defined as relevant for the examined case.
Modularity: The parameterization of implemented SPIN rules and functions allow
complex schema and property declarations to be manageable in terms of maintenance,
adoption and re-use; changing the body of a rule does not affect its use in other stages
of the rule layer, while changing the parameters used/returned only alters its function
signature. Moreover, SPIN enables the explicit definition of the rule execution order,
giving priority to or isolating groups of rules under specified circu mstances.
Extensibility: The hierarchical capabilities of the proposed framework facilitate the
update and extension of rules and concepts supporting additional input from AQ,
health or other related domain knowledge, without the need to change rules in other
layers. More specifically, in our implementation, air pollutant measurements are con-
verted via rules to corresponding AQI values; such data serve as input to subsequent
levels of the reasoning process. If an additional air pollutant is to be added to the
schema, rules will be enriched only for the task of converting numerical to nominal
values, without any interference to AQI rules or SPIN rules interconnected to them.
Appropriateness: The combination of ontologies with SPIN rules, as an extra layer
of rule-based reasoning, is perfectly suited for more advanced, rule-based inference.
This sophisticated implementation provides the required expressiveness for the do-
main’s knowledge representation and the respective inference mechanisms which can
efficiently deal with the provision of user-profile driven recommendations.
7 Conclusions and Future Work
This paper presented a novel ontology-based framework, which integrates both the
static parts and the dynamic processes of a DSS in a uniformly structured and seman-
tically enriched decision support KB. The implementation is based on a 3-layered
approach which consists of the following components: (i) the ontological schema of
abstract concepts and relations in the domain of interest, (ii) the structured initializa-
tion of actual data in the ontology, and (iii) the rule-based mechanism for interpreting
the semantics behind the stored data and generating the targeted recommendations to
the users, with respect to their user profile characteristics. The developed schema and
the mechanisms integrated in the proposed DSS framework, reflect the dynamics of
the AQ domain from the citizens’ perspective: in every request, the system combines
the status of the environment and the specific characteristics of the requesting user, so
as to produce simple personalized recommendations encompassing the direct influ-
ence of severe AQ conditions in the users’ health and planned activities.
The proposed implementation highlights the strategic advantages of the use of on-
tologies on the basis of the multifaceted needs of DSSs: the layered structure and the
semantic declaration of concepts and rules can efficiently handle the heterogeneity of
represented data and facilitate the modularity and extensibility of the system. Moreo-
ver, the ontology-based implementation is domain-agnostic and can be exploited in
different application contexts, following a relevant configuration mechanism of the
targeted user profiles and recommendations.
As future work, we aim to further evaluate the satisfiability and coverage of the
recommendation system from the users perspective. We also aim to investigate the
integration of fuzzy reasoning techniques for covering more complex relations (addi-
tional parameters, cumulative effects, etc.) Finally, we will propose an Ontology
Design Pattern for the formal representation of the reasoning mechanism, and exploit
new rule-based approaches with the adoption of SPIN’s next generation called
SHACL [24].
Acknowledgments. This work is partially funded by the European Commission under
the contract number H2020-688363 hackAIR.
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