An Ontology-Based Intelligent Information System for
Urbanism and Civil Engineering Data
Stefan Trausan-Matu1,2 and Anca Neacsu1

1
“Politehnica” University of Bucharest,
Department of Computer Science and Engineering,
Splaiul Independetei nr. 313, Bucharest, Romania

2
Research Institute for Artificial Intelligence of the Romanian Academy
Calea 13 Septembrie nr.13, Bucharest, Romania

Corresponding author: Stefan Trausan-Matu,
email: trausan@cs.pub.ro,
fax: +40.318.153290
phone: +40.724.985518




Abstract. The paper presents a prototype of an intelligent information
system for urban and civil engineering data centered on an ontology. The
system will provide intelligent personalized access to concepts from the
ontology and to a collection of relevant associated documents indexed
according to the ontology’s concepts. The declarative knowledge of the
ontology and an associated set of production rules may be used for
automatic inferences that will enable reasoning for getting intelligent

answers to users’ queries, under an expert system dialog. The paper uses
examples from a first version of the ontology for urban and civil engineering
concepts that is in the center of the system. The ontology is developed
following an integrated cognitive and socio-cultural approach. It contains a
taxonomy of objects structured according to Engestrom’s Theory of Activity
and Sowa’s ontology. This structured development facilitates further
knowledge acquisition.
Keywords: information systems, ontology, Semantic Web, expert systems,
production rules, Jess
1 Introduction
The number of documents containing data about urbanism and civil engineering is
becoming higher every day. Moreover, some of them are changing as new
recommendations, regulations, and laws appear or substitute old ones. The
majority of this data is now available on the web and they may be accessed both by
general search engines like Google (http://www.google.com) or by specific search
tools available on particular web sites. However, a major problem is that all these
search engines are keyword-based and they are not able to make inferences and to
cope with relationships among documents.
A solution to the above problems is to develop an intelligent information
system, based on the knowledge of the urban development and civil engineering
domains. The knowledge in this base should be both declarative and procedural.
Declarative knowledge refers to what is known to exist in the domain, what are the
concepts and how they are related. This kind of knowledge is usually constructed
around a taxonomy and it may be viewed as an ontology of the domain. In addition
to domain knowledge, general knowledge may be also used, e.g. as a top level
ontology, containing, for example general concepts describing human activities
and regulations.
Procedural knowledge contains rules, recipes about how to use knowledge. For
example, a general rule could say that, if you want to do an activity related to an
object in a community, you should see what laws or regulations you should
respect.
A collection of relevant associated documents (e.g. texts of law, official
regulations, etc.) indexed according to the ontology’s concepts is also integrated
with the ontology.
. The paper continues with a section describing some theoretical concepts about
ontologies and production rules. The following section introduces some original
ideas about intelligent information systems. Section 4 presents an intelligent

information system for urbanism and civil engineering. The paper ends with some
conclusions and further development ideas.
2 Ontologies and rules
An ontology is, in the context of intelligent, knowledge-based systems, a
declarative knowledge base containing the concepts and the relations that exist in a
given domain, it is "a specification of a conceptualization. That is, an ontology is a
description (like a formal specification of a program) of the concepts and
relationships that can exist for an agent or a community of agents. This definition
is consistent with the usage of ontology as set-of-concept-definitions, but more
general. " (Gruber). The name is obviously inspired from philosophy, where it
means a “branch of metaphysics concerned specifically with what (kinds of)
things there are” (www.shef.ac.uk/~phil/other/philterms.html).
From a knowledge representation perspective, ontologies are semantic networks
that state what kinds of concepts exist and what abstraction-particularization
relations hold among them. If a concept is a particularization of another concept, it
has all the features of the more abstract concept and, maybe, some particular ones,
For example, in figure 1 (the Protégé environment - http://protege.stanford.edu -
was used for the development of the ontology), the fact that the concept
“BridgesAndElevatedHighways” has “Crossovers”, “FootBridge”,
“MobileBridge”, “Overpasses”, “RailBridge” and “RoadBridge”, implicitly
enumerates the only possible cases. Moreover, all these inherit properties (e.g.
regulations) that belong to “BridgesAndElevatedHighways” or its ancestors.

Figure 1 A fragment of the urban development and civil engineering ontology

Ontologies simplify computing in a similar way with Object-Oriented
Programming (whose idea has common ancestors with ontologies). For example,
an ontology may be seen as a library of concepts that may be used for many
applications. Another important resemblance is encapsulation and centralization,
which simplify changes: When something changes, it is enough to make a
modification in a single place and all the descendants will inherit the new version.
However, ontologies do not cover all kinds of knowledge representation. In
addition to declarative knowledge representation, there is a need also for
procedural knowledge, saying what to do in a given context. Such type of
knowledge may be represented by production rules, which are pairs condition –
action: IF condition holds, THEN PERFORM action. Conditions usually contain
patterns and variables that may be linked to facts. A production rule system has a
conflict resolution strategy that selects the rule that will be applied from the rules
that may be applied.
3 Intelligent information systems
An information system stores data, with the goal of providing information starting
from that data, when needed. Therefore, we can identify three main features that
characterize an information system: the way data is stored, the way new data is
included, and the way information is provided as results to queries.
3.1 Storage alternatives
Many information systems are developed around a database. This case has the
advantage of efficient implementations but, because data has a very precise and
inflexible structure, any change in the conceptual schema and any intelligent
dialogue are not possible.
In the context of the huge number of documents available now on the web, it is
natural to consider the case of information systems using text as content instead
databases. However, even if search engines like Google are able to provide a lot of
useful information, a problem is that many times, such engines provide too much
information, too much documents as a result to a query. This problem is due to the
fact that documents are unstructured and that natural language processing is not
able to handle ambiguity and context issues (Winograd and Flores, 1986).
One of the ways to handle the problems of information retrieval from texts on
the web is to add explicit knowledge descriptions (metadata) of the content of
documents and to integrate this metadata in conceptual frameworks (ontologies)
for each domain. This approach is supported by the Semantic Web perspective,
which aims at facilitating programs to process texts on the web (Berners-Lee et al.,

2001). A first step towards the Semantic Web is to annotate texts with XML
(http://www.w3.org/XML/). Using RDF (Resource Description Framework -
http://www.w3.org/RDF/) to state facts about web resources is the second step. The
next step is the development of ontologies using OWL (Ontology Web Language -
http://www.w3.org/2004/OWL/).
3.2 Inclusion of new data
In databases, new data may be included only as new instances, which strictly
follow the fixed conceptual model. The case of texts is totally different, practically
there are no restrictions. For example, the addition a new document on the web is
not restricted by any conceptual model.
Ontology based storage, following the Semantic Web ideas, allows the addition
of any document, but requires its metadata annotation. However, the conceptual
model is not fixed, it can be changed, by modifying the ontology.
3.3 Intelligent query processing
In order to provide the needed data to various types of users and to different kinds
of questions, an intelligent information system should provide several ways of
interaction. For example, in the context of the web, a natural way is to browse
pages containing useful information. In addition to classical browsing of a fixed
structure of web pages, user’s profile may be considered for generating a
personalized structure of web pages, starting from the domain’s ontology (Trausan-
Matu et al., 2002). The ideal information system should be, however, able to enter
into a dialog with the user, using his/her own language.
Any information act is, in fact, dialogistic. Moreover, as Bakhtin emphasized,
any text is a dialog (Bakhtin, 1986). Even if you write something and you put
something on the web, this is a potential dialog with the readers of the text.
Different ways of querying in information systems are, in our opinion, different
ways of entering in dialog:
a. database query
b. hypertext browsing
c. keyword-based search engine
d. intelligent search engine
e. expert system dialog
f. controlled natural language
g. question answering
h. natural language dialog.

Figure 2 The main concepts of the Activity Theory of Yrjö Engeström
From the above list, only natural language dialog and question answering are, at
least for the moment, not satisfactory implemented. All the other ways of
information querying are, more or less, possible to implement.
4 An intelligent information system for urbanism and civil
engineering
4.1 A socio-cultural ontology for urbanism and civil engineering
The Activity Theory of Yrjö Engeström (1987), emphasizes categories (subjects,
objects, and communities) and mediators (general artifacts, social rules and
division of labor), see also figure 2. This theory provides a theoretical framework
that has been used for developing an ontology for urban development (Trausan-
Matu, 2007) that has as basic concepts the components of the above mentioned two
group of entities.


Artifact
Subject Object
Rules Community Division of labor

Each of these six entities are a basic concept (or “class”) in the socio-cultural
ontology (see figure 1). These concepts may have attributes, sub-concepts (that
may be also sub-concepts of several other concepts, i.e. multiple inheritance of
properties is allowed), and relations with other concepts (see figure 3).


Figure 3 The relations of the urban development and civil engineering ontology
In addition to generic concepts, the ontology contains also individuals
(instances). For example, the “Subject” class has 12 instances (see figure 4). One
of these, the “LocalAuthority” instance has several relations (“provides”,
“releases”, “controls”, etc.) with other individuals.

Figure 4 The “LocalAuthority” individual
4.2 Intelligent querying
In the first version of the intelligent information system was implemented an expert system
that enters into a dialog with an user, for providing information about topics related to
getting urbanism authorizations for new buildings. An expert system is a knowledge-
based program in which there is a clear distinction between the knowledge (for example,
OWL classes and rules) and the inference engine. The Jess production rule system
(http://www.jessrules.com/jess) was used. A program in Jess is a collection of rules that
can be matched to the existing data in the working memory. Each rule has a first,
matching part, and a second, action one, which modifies the working memory or prints
something. A rule may have variables that are linked to values in the working memory
using pattern matching. For example, a rule that prints the information that local
authorities may provide is below exemplified. In this rule, the variables $?p, $?r, and
$?c are matched to all the available data, in the working memory, regarding what the
local authority provides, releases and controls.
(defrule local_authority
(declare (salience 1))
(print go_to_local_authority)
?f <- (object (is-a Subject)
(:NAME "LocalAuthority")
(provides $?p)(releases $?r)(controls $?c))
(not (answer ?))
=>
(printout t (slot-get ?f :NAME) " provides: " crlf)
(foreach ?x $?p (printout t " - "(instance-name ?x) crlf))
(printout t " releases: " crlf)
(foreach ?x $?r (printout t " - "(instance-name ?x) crlf))
(printout t " in accordance with: " crlf)

(foreach ?x $?c (printout t " - "(instance-name ?x) crlf))

In figure 5 is illustrated a simple dialog that, among others, presents what the
“LocalAuthority” can provide, release and control. An important observation is
that the data is obtained from the ontology and it may be different if the ontology
changes.


Figure 5 A dialog in the expert system session
4 Conclusions and further developments
Production rules may be used for developing expert systems that can implement
intelligent dialogues that provide information contained in ontologies. One
important advantage of such intelligent information systems is their flexibility: new
rules may be added and the ontology may change.
The system is now under testing and extension, by adding new rules and
ontology components. In the next versions of the system, new facilities that use the
ontology will be added. For example, the intelligent search and controlled natural
language will from another intelligent information system (Trausan-Matu et al.,
2006) will be integrated. The ontology will also be used for document search that
could be included in the collection (Trausan-Matu et al., 2006).
However, one of the main problems in ontology development is that many
times, experts do not agree about a given taxonomy. Moreover, taxonomies may
change according to different perspectives of the same person. A solution to these
problems is to consider folksonomies instead ontologies.

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