=Paper=
{{Paper
|id=Vol-1388/PEGOV2015-paper3
|storemode=property
|title=Personalized Extended Government for Local Public Administrations
|pdfUrl=https://ceur-ws.org/Vol-1388/PEGOV2015-paper3.pdf
|volume=Vol-1388
|dblpUrl=https://dblp.org/rec/conf/um/BiancalanaMS15
}}
==Personalized Extended Government for Local Public Administrations==
https://ceur-ws.org/Vol-1388/PEGOV2015-paper3.pdf
Personalized Extended Government
for Local Public Administrations
Claudio Biancalana1,2 , Alessandro Micarelli1 , and Giuseppe Sansonetti1
1
Roma Tre University, Department of Engineering
Via della Vasca Navale 79, 00146 Rome, Italy
2
Lazio Innovazione Tecnologica, LAit S.p.A.
Via Adelaide Bono Cairoli 68, 00144 Rome, Italy
claudio.biancalana@laitspa.it,{micarel,gsansone}@dia.uniroma3.it
Abstract. This paper discusses the enterprise organization environment
and reports our experience and lessons learned in developing an exten-
sion of the traditional virtual enterprise model, we named personalized
extended government (PEG) model. The aim of such model is to sim-
plify and enhance the effectiveness of e-Government services, by realizing
Administration to Administration (A2A) and Administration to Citizen
(A2C) processes in a personalized perspective. The features of the pro-
posed model make it suitable for use in local public administrations. As
a proof of this, it has been successfully deployed to realize the Italian
Open Government Data Portal of Regione Lazio, which allows every cit-
izen to be informed about the employment of public resources on regional
territory.
Keywords: personalization, e-Government, virtual enterprise model
1 Introduction
In the late 1990s, the concept of virtual enterprise (VE) model has been in-
troduced [2], where every business organization unit is connected to each other
through a data transmission network, in order to explore market opportunities
and cooperate, on a temporary basis, to better respond to business opportuni-
ties. Hence, a VE can be seen as a heterogeneous network for both enterprises
and individuals with integrated cooperation, exploiting ICT technologies and
protocols for a specific business process. Over the years, a second model has
been developed, substantially similar to the VE model, but based on more sta-
ble and long-term agreements: the extended enterprise (EE) model [7]. Recently,
an organization model similar to EE has been implemented at a government
level and can be recognized in initiatives such as the Italian Open Government
Data Portal of Regione Lazio 3 . Such a portal provides a web interface that cen-
tralizes access to all open datasets for anyone, in particular for data journalists,
3
https://dati.lazio.it
public administrations, scientists, and business people. The project has been de-
signed to realize Administration to Administration (A2A) and Administration
to Citizen (A2C) processes, and relies on stable and deeply defined agreements.
Unlike the EE model, however, the focus is not on business opportunities, but on
making e-Government services simpler and more effective. In addition, the need
to manage and discover unstructured information has resulted in the gradual
awareness of the need to adopt knowledge management systems (KMSs) based
on semantic and user-modeling functions. This high degree of similarity allows
us to introduce a new definition to refer to the concepts described so far, namely,
personalized extended government (PEG), as a personalized and context-oriented
extension of EE type. This paper: (1) defines PEG; and (2) provides notes on
the design of a KMS that supports PEG.
2 Personalized Extended Government
Hereafter, we refer to personalized extended government (PEG) as an integrated
unit of organizations, agreements, protocols and ICT resources able to support
public administrations to deploy a context-oriented model to build Administra-
tion to Administration (A2A) and Administration to Citizen (A2C) scenarios,
in order to simplify and to improve the effectiveness of e-Government services.
By analogy with the EE model, we can define the following major PEG model
features:
– e-Government service-driven cooperation: A2A and A2C processes are al-
ways aimed at providing electronic government services to citizens and busi-
nesses, with the goal to simplify and make them more efficient and effective;
– Complementarity: administrations exchange with each other only correct
and complete data;
– Process integration and resource sharing: more specifically, data, information
and knowledge;
– Interdependence: process integration and resource sharing are carried out
according to well defined cooperation agreements.
In order to deploy the PEG organization model, it is necessary to define the
following aspects:
– Guidelines for every single participant IT assets integration. This problem is
due to different technologies used by every administration and the need to
preserve both investments and administration autonomy. For these reasons,
it is necessary to define a technological infrastructure that guarantees inter-
operability regardless of the organization structures and single participant
legacy systems;
– Maturity model, which is a structured collection of elements that describe
certain aspects of maturity in an organization, for example, to provide a way
to define what improvement means for an organization;
– Common governance model through the administrations of all participants
and citizens.
Regione Lazio’s experience raises an issue: the realization of A2A and A2C iso-
lated processes leads to fragmented knowledge and to a loss of fundamental
information used to integrate management relationship between administrations
and citizens or enterprises. For this reason, LAit S.p.A. and Regione Lazio have
planned a knowledge management system (KMS) design with basic concepts in-
spired to both EE model and PEG model, in order to develop research ideas in
the EE field. The main principles of KMSs should be the following:
– Affordable setup: no more heavy bulked social networks held by central pub-
lic administrations. As a normal web user can now start a forum or a blog
using third party (often free) software, he should also be able to use a web
host or a hosting service;
– Accessibility through (semantic?) search engines: in our vision, this is surely
something related to the open nature of KMSs, but it would gain some
commitment from search engines, which will be able to improve quality of
searches through proper indexing of published semantic annotations;
– Scalable open architecture: a given service may explicitly be built upon a
KMS, committing to its ontologies and content organization. Vice versa, in
an even more open view, independent services may be linked by a given
KMS. This would allow users to tag the content of these services accord-
ing to the OASIS reference ontologies, thus easily putting traditional (non
semantic-driven) services immediately into practice. The same process would
be applied to standard web pages. People could write web pages directly con-
nected to a KMS making explicit reference to its vocabulary, as embedded
RDFa, or they could semantically bookmark an external web page (or anno-
tate part of its content) against that same vocabulary.
2.1 Knowledge Indexing
Before addressing the problem of knowledge retrieval, it is essential to analyze
how the system indexes the available information, that is, which representation
has been chosen to guarantee an efficient and effective retrieval phase. The re-
quirements are twofold: it is essential that on the one hand knowledge is quickly
retrieved by users, on the other hand this knowledge accurately satisfies users
information needs in terms of high precision. An indexing system for business
companies must also be able to deal with different kinds of information represen-
tations, from unstructured documents based on natural language to ontology-
based knowledge and relational databases. Moreover, it should provide a com-
prehensive and homogenous human-computer interface for knowledge retrieval.
In order to provide the aforementioned prerequisites, it is necessary to consider
different types of information and the degrees of information “richness”. Informa-
tion based on ontological standards, for instance, expresses relationships between
typically non-structured information, such as natural language text and meta-
data. Such metadata usually describe features or classes related to given pieces
of information. A typical example is the association between a document and
one particular category in a predefined taxonomy. As for information stored in
databases, we have an underlying relational model that clearly states the seman-
tic meaning of each piece of information unit, such as price, address, and location,
and therefore enables the interpretation/recovery process. In order to define a
unique representation that deals with all the different types of available infor-
mation (i.e., natural language, ontology-based, and databases) we must define a
subset of shared features that is possible to generalize, and automatic or semi-
automatic methods and techniques for translating information from one of these
representations to the internal one. This sort of intermediate representation con-
sists of traditional non-structured information with associated meta-information
related to concepts of a taxonomy of the business domain for the given public
administration unit (PAU). Briefly, each information unit is classified in a subset
of categories from a simplified ontology. Such meta-information can be exploited
both in the retrieval phase, to reduce possible ambiguities in the processed in-
formation, and to re-organize the knowledge in more efficient ways for further
user search activities, such as online hierarchical clustering. Information based
on ontological standards does not pose relevant issues. In this case, the source is
based on a rich language while our internal representation simplifies some fea-
tures, such as the kinds of relations between concepts. In our representation, we
have relations hu, Ci where u is the information unit and C is a set of categories
in the given taxonomy related to the concept u. We only have IS-A relationships,
so it is not hard to extract them from the initial ontology. The selection of the
most important concepts from the initial ontology is the only task that knowledge
experts have to perform before populating the internal taxonomy. Considering
the current amount of unstructured information available within companies, the
problem of making such information accessible to users is likely to be the most
important issue to solve in our knowledge system. Specifically, having chosen a
particular representation for PAU domains, it is necessary to find a technique
that allows us to autonomously process the unstructured information and popu-
late the internal knowledge base. In input we have information objects, typically
text documents, reports, hypertext pages, etc. These objects are processed for
named entity extraction, text segmentation, and text categorization. Given an
information object, we initially locate and classify atomic elements in text into
predefined categories, such as names of persons, organizations, locations, ex-
pressions of times, quantities, monetary values, percentages, etc. To this aim,
we used named entity recognition (NER) systems based on linguistic grammar-
based techniques, statistical models and dictionaries (or gazetteers). NER is a
well-known research field, subtask of information extraction, which does not fo-
cus on semantic interpretation of languages but on more practical and easier
goals, so obtaining excellent results in terms of precision of results. The proper
nouns of companies, persons, etc. in output of the NER module are used to in-
crease the weight of these entities during the indexing/retrieval steps. A further
step we follow to process the input information is text segmentation. In short, a
given document is divided into sequences of words or other similar meaningful
units that are separately stored in the knowledge base. This step is useful when-
ever we have long documents, such as reports or e-books, which cover several
different topics (i.e., categories in the internal taxonomy). In order to increase
the retrieval precision, it is better to split them into meaningful coherent regions.
Our segmentation algorithm is based on the Choi’s work [3]. It performs three
steps: (1) tokenization, (2) lexical score determination, and (3) boundary identi-
fication. Basically, after breaking the document up into a sequence of tokens, we
use a similarity measure to analyze the semantic coherence among contiguous
text regions. Finally, we determine the boundaries whenever we have relevant
variations of the semantic coherence measure. The last process we perform on
unstructured information is text categorization. After recognizing semantically
coherent information units and - for each unit - its relevant entities, we assign a
subset of taxonomic categories to it through text categorization techniques [8].
As for the training phase, we use a subset of documents already categorized by
the knowledge expert, performing an ad-hoc feature selection that also exploits
the aforementioned NER module to assign more weight to terms that correspond
to relevant semantic classes, such as proper names and locations. The categoriza-
tion output is a tuple of couples hci , αi i, where for each taxonomic category ci we
have a value αi between [0, 1] that represents the degree of relatedness of the in-
put document to the class. This information is stored along with the document
in the knowledge base and it is used during the retrieval and personalization
phases, as described in the following section.
2.2 Semantic Querying and Personalization
The most popular paradigm for querying a textual database is to submit short
queries. Users express their information needs through a small set of keywords
that must be present in the retrieved documents. The retrieval system returns
an ordered list of references, based on matching algorithms that assign a rel-
evance weight to each indexed document. In our knowledge base, along with
each document, we have a list of assigned categories referenced in the internal
taxonomy. In order to exploit this information, the query should include one
or more categories that users are interested in. We named these enhanced user
needs semantic queries [1]. One of the most important problems that occurs
while querying a corpus of textual documents is the choice of the right keywords
for retrieval. Synonymy (i.e., two words that express the same meaning) and
polysemy (i.e., different meanings expressed by one word) of natural language
may decrease the recall and precision of the retrieval process [4]. For that rea-
son, we have included a user modeling component to represent the users needs.
This component is involved during the querying in order to help disambiguate
the meaning of the query terms. Some user modeling strategies have been pro-
posed in the e-Government services field (see, for instance, [6]). The proposed
user modeling is based on a concept network paradigm [5] instantiated on the
taxonomy of the PAU domain. Concept networks are usually employed as a form
of knowledge representation. They consist of graphs of concepts and edges that
represent semantic relations between these concepts. We use concept networks
to weight which concepts users are more interested in, that is, concepts related
to the user needs. In our first prototype, the relations between concepts are not
considered.
3 Conclusion
In this paper we have introduced a natural extension of the virtual enterprise
model, we called personalized extended government (PEG) model, whose aim
is to encourage and facilitate the exchange of public domain and community
information in a personalized perspective, respecting the public administrations
and citizens information needs. The implementation of the proposed model at
a government level has represented a strategic roadmap for Regione Lazio ICT
Government.
Obviously, the further development of the PEG model first of all involves
planning and performing an in-depth experimentation, in order to assess the ac-
tual satisfaction of stakeholders. Furthermore, several future developments of all
aspects of this work are possible. More specifically, we would like to enhance the
effectiveness of the proposed model by exploiting information extracted from so-
cial media. In literature, indeed, some works (e.g., see [9]) show how government
services can be improved based on user-generated contents found in publicly
available social media.
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